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is-small is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Yilmaz%2C+O&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Yilmaz%2C+O&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Yilmaz%2C+O&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18865">arXiv:2407.18865</a> <span> [<a href="https://arxiv.org/pdf/2407.18865">pdf</a>, <a href="https://arxiv.org/ps/2407.18865">ps</a>, <a href="https://arxiv.org/format/2407.18865">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zerin%2C+M+C">Melih Can Zerin</a>, <a href="/search/cs?searchtype=author&query=Vural%2C+E">Elif Vural</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.18865v4-abstract-short" style="display: inline;"> In this paper, we propose an algorithm for downlink (DL) channel covariance matrix (CCM) estimation for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) communication systems with base station (BS) possessing a uniform linear array (ULA) antenna structure. We consider a setting where the UL CCM is mapped to DL CCM by a mapping function. We first present a theoretica… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18865v4-abstract-full').style.display = 'inline'; document.getElementById('2407.18865v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18865v4-abstract-full" style="display: none;"> In this paper, we propose an algorithm for downlink (DL) channel covariance matrix (CCM) estimation for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) communication systems with base station (BS) possessing a uniform linear array (ULA) antenna structure. We consider a setting where the UL CCM is mapped to DL CCM by a mapping function. We first present a theoretical error analysis of learning a nonlinear embedding by constructing a mapping function, which points to the importance of the Lipschitz regularity of the mapping function for achieving high estimation performance. Then, based on the theoretical ground, we propose a representation learning algorithm as a solution for the estimation problem, where Gaussian RBF kernel interpolators are chosen to map UL CCMs to their DL counterparts. The proposed algorithm is based on the optimization of an objective function that fits a regression model between the DL CCM and UL CCM samples in the training dataset and preserves the local geometric structure of the data in the UL CCM space, while explicitly regulating the Lipschitz continuity of the mapping function in light of our theoretical findings. The proposed algorithm surpasses benchmark methods in terms of three error metrics as shown by simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18865v4-abstract-full').style.display = 'none'; document.getElementById('2407.18865v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.04984">arXiv:2310.04984</a> <span> [<a href="https://arxiv.org/pdf/2310.04984">pdf</a>, <a href="https://arxiv.org/format/2310.04984">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Model-adapted Fourier sampling for generative compressed sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berk%2C+A">Aaron Berk</a>, <a href="/search/cs?searchtype=author&query=Brugiapaglia%2C+S">Simone Brugiapaglia</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Scott%2C+M">Matthew Scott</a>, <a href="/search/cs?searchtype=author&query=Sheng%2C+X">Xia Sheng</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.04984v2-abstract-short" style="display: inline;"> We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case). It was recently shown that $\textit{O}(kdn\| \boldsymbol伪\|_{\infty}^{2})$ uniformly random Fourier measurements are sufficient to recover signals in the range of a neural network $G:\mathbb{R}^k \to \mathbb{R}^n$ of depth $d$, where each comp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04984v2-abstract-full').style.display = 'inline'; document.getElementById('2310.04984v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04984v2-abstract-full" style="display: none;"> We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case). It was recently shown that $\textit{O}(kdn\| \boldsymbol伪\|_{\infty}^{2})$ uniformly random Fourier measurements are sufficient to recover signals in the range of a neural network $G:\mathbb{R}^k \to \mathbb{R}^n$ of depth $d$, where each component of the so-called local coherence vector $\boldsymbol伪$ quantifies the alignment of a corresponding Fourier vector with the range of $G$. We construct a model-adapted sampling strategy with an improved sample complexity of $\textit{O}(kd\| \boldsymbol伪\|_{2}^{2})$ measurements. This is enabled by: (1) new theoretical recovery guarantees that we develop for nonuniformly random sampling distributions and then (2) optimizing the sampling distribution to minimize the number of measurements needed for these guarantees. This development offers a sample complexity applicable to natural signal classes, which are often almost maximally coherent with low Fourier frequencies. Finally, we consider a surrogate sampling scheme, and validate its performance in recovery experiments using the CelebA dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04984v2-abstract-full').style.display = 'none'; document.getElementById('2310.04984v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 4 figures. Submitted to the NeurIPS 2023 Workshop on Deep Learning and Inverse Problems. This revision features additional attribution of work, aknowledgmenents, and a correction in definition 1.1</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04354">arXiv:2306.04354</a> <span> [<a href="https://arxiv.org/pdf/2306.04354">pdf</a>, <a href="https://arxiv.org/format/2306.04354">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LCOMM.2024.3363878">10.1109/LCOMM.2024.3363878 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quasi-Newton FDE in One-Bit Pseudo-Randomly Quantized Massive MIMO-OFDM Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+G">G枚khan Y谋lmaz</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04354v3-abstract-short" style="display: inline;"> This letter offers a new frequency domain equalization (FDE) scheme that can work with a pseudo-random quantization (PRQ) scheme utilizing non-zero threshold quantization in one-bit uplink multi-user massive multiple-input multiple-output (MIMO) systems to mitigate quantization distortion and support high-order modulation schemes. The equalizer is based on Newton's method (NM) and applicable for o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04354v3-abstract-full').style.display = 'inline'; document.getElementById('2306.04354v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04354v3-abstract-full" style="display: none;"> This letter offers a new frequency domain equalization (FDE) scheme that can work with a pseudo-random quantization (PRQ) scheme utilizing non-zero threshold quantization in one-bit uplink multi-user massive multiple-input multiple-output (MIMO) systems to mitigate quantization distortion and support high-order modulation schemes. The equalizer is based on Newton's method (NM) and applicable for orthogonal frequency division multiplexing (OFDM) transmission under frequency-selective fading by exploiting the properties of massive MIMO. We develop a low-complexity FDE scheme to obtain a quasi-Newton method. The proposed detector outperforms the benchmark detector with comparable complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04354v3-abstract-full').style.display = 'none'; document.getElementById('2306.04354v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04329">arXiv:2306.04329</a> <span> [<a href="https://arxiv.org/pdf/2306.04329">pdf</a>, <a href="https://arxiv.org/format/2306.04329">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2023.3318081">10.1109/TWC.2023.3318081 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Pseudo-Random Quantization Based Two-Stage Detection in One-Bit Massive MIMO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+G">G枚khan Y谋lmaz</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04329v1-abstract-short" style="display: inline;"> Utilizing low-resolution analog-to-digital converters (ADCs) in uplink massive multiple-input multiple-output (MIMO) systems is a practical solution to decrease power consumption. The performance gap between the low and high-resolution systems is small at low signal-to-noise ratio (SNR) regimes. However, at high SNR and with high modulation orders, the achievable rate saturates after a finite SNR… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04329v1-abstract-full').style.display = 'inline'; document.getElementById('2306.04329v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04329v1-abstract-full" style="display: none;"> Utilizing low-resolution analog-to-digital converters (ADCs) in uplink massive multiple-input multiple-output (MIMO) systems is a practical solution to decrease power consumption. The performance gap between the low and high-resolution systems is small at low signal-to-noise ratio (SNR) regimes. However, at high SNR and with high modulation orders, the achievable rate saturates after a finite SNR value due to the stochastic resonance (SR) phenomenon. This paper proposes a novel pseudo-random quantization (PRQ) scheme by modifying the quantization thresholds that can help compensate for the effects of SR and makes communication with high-order modulation schemes such as $1024$-QAM in one-bit quantized uplink massive MIMO systems possible. Moreover, modified linear detectors for non-zero threshold quantization are derived, and a two-stage uplink detector for single-carrier (SC) multi-user systems is proposed. The first stage is an iterative method called Boxed Newton Detector (BND) that utilizes Newton's Method to maximize the log-likelihood with box constraints. The second stage, Nearest Codeword Detector (NCD), exploits the first stage solution and creates a small set of most likely candidates based on sign constraints to increase detection performance. The proposed two-stage method with PRQ outperforms the state-of-the-art detectors from the literature with comparable complexity while supporting high-order modulation schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04329v1-abstract-full').style.display = 'none'; document.getElementById('2306.04329v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.03215">arXiv:2304.03215</a> <span> [<a href="https://arxiv.org/pdf/2304.03215">pdf</a>, <a href="https://arxiv.org/format/2304.03215">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Taghibakhshi%2C+A">Ali Taghibakhshi</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+M">Mingyuan Ma</a>, <a href="/search/cs?searchtype=author&query=Aithal%2C+A">Ashwath Aithal</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Onur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Maron%2C+H">Haggai Maron</a>, <a href="/search/cs?searchtype=author&query=West%2C+M">Matthew West</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.03215v2-abstract-short" style="display: inline;"> Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different devices belonging to the same person, utilizing sequence logs. Previous data mining techniques have struggled to address the long-range dependencies and higher-order connections between the logs. Recently, researchers hav… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03215v2-abstract-full').style.display = 'inline'; document.getElementById('2304.03215v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.03215v2-abstract-full" style="display: none;"> Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different devices belonging to the same person, utilizing sequence logs. Previous data mining techniques have struggled to address the long-range dependencies and higher-order connections between the logs. Recently, researchers have modeled this problem as a graph problem and proposed a two-tier graph contextual embedding (TGCE) neural network architecture, which outperforms previous methods. In this paper, we propose a novel hierarchical graph neural network architecture (HGNN), which has a more computationally efficient second level design than TGCE. Furthermore, we introduce a cross-attention (Cross-Att) mechanism in our model, which improves performance by 5% compared to the state-of-the-art TGCE method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03215v2-abstract-full').style.display = 'none'; document.getElementById('2304.03215v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.06675">arXiv:2301.06675</a> <span> [<a href="https://arxiv.org/pdf/2301.06675">pdf</a>, <a href="https://arxiv.org/format/2301.06675">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Delavari%2C+P">Parsa Delavari</a>, <a href="/search/cs?searchtype=author&query=Ozturan%2C+G">Gulcenur Ozturan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Oruc%2C+I">Ipek Oruc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.06675v1-abstract-short" style="display: inline;"> Purpose: Convolutional neural networks can be trained to detect various conditions or patient traits based on retinal fundus photographs, some of which, such as the patient sex, are invisible to the expert human eye. Here we propose a methodology for explainable classification of fundus images to uncover the mechanism(s) by which CNNs successfully predict the labels. We used patient sex as a case… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.06675v1-abstract-full').style.display = 'inline'; document.getElementById('2301.06675v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.06675v1-abstract-full" style="display: none;"> Purpose: Convolutional neural networks can be trained to detect various conditions or patient traits based on retinal fundus photographs, some of which, such as the patient sex, are invisible to the expert human eye. Here we propose a methodology for explainable classification of fundus images to uncover the mechanism(s) by which CNNs successfully predict the labels. We used patient sex as a case study to validate our proposed methodology. Approach: First, we used a set of 4746 fundus images, including training, validation and test partitions, to fine-tune a pre-trained CNN on the sex classification task. Next, we utilized deep learning explainability tools to hypothesize possible ways sex differences in the retina manifest. We measured numerous retinal properties relevant to our hypotheses through image segmentation to identify those significantly different between males and females. To tackle the multiple comparisons problem, we shortlisted the parameters by testing them on a set of 100 fundus images distinct from the images used for fine-tuning. Finally, we used an additional 400 images, not included in any previous set, to reveal significant sex differences in the retina. Results: We observed that the peripapillary area is darker in males compared to females ($p=.023, d=.243$). We also observed that males have richer retinal vasculature networks by showing a higher number of branches ($p=.016, d=.272$) and nodes ($p=.014, d=.299$) and a larger total length of branches ($p=.045, d=.206$) in the vessel graph. Also, vessels cover a greater area in the superior temporal quadrant of the retina in males compared to females ($p=0.048, d=.194$). Conclusions: Our methodology reveals retinal features in fundus photographs that allow CNNs to predict traits currently unknown, but meaningful to experts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.06675v1-abstract-full').style.display = 'none'; document.getElementById('2301.06675v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.02719">arXiv:2211.02719</a> <span> [<a href="https://arxiv.org/pdf/2211.02719">pdf</a>, <a href="https://arxiv.org/format/2211.02719">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Embracing Off-the-Grid Samples </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=L%C3%B3pez%2C+O">Oscar L贸pez</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.02719v2-abstract-short" style="display: inline;"> Many empirical studies suggest that samples of continuous-time signals taken at locations randomly deviated from an equispaced grid (i.e., off-the-grid) can benefit signal acquisition, e.g., undersampling and anti-aliasing. However, explicit statements of such advantages and their respective conditions are scarce in the literature. This paper provides some insight on this topic when the sampling p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02719v2-abstract-full').style.display = 'inline'; document.getElementById('2211.02719v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.02719v2-abstract-full" style="display: none;"> Many empirical studies suggest that samples of continuous-time signals taken at locations randomly deviated from an equispaced grid (i.e., off-the-grid) can benefit signal acquisition, e.g., undersampling and anti-aliasing. However, explicit statements of such advantages and their respective conditions are scarce in the literature. This paper provides some insight on this topic when the sampling positions are known, with grid deviations generated i.i.d. from a variety of distributions. By solving a square-root LASSO decoder with an interpolation kernel we demonstrate the capabilities of nonuniform samples for compressive sampling, an effective paradigm for undersampling and anti-aliasing. For functions in the Wiener algebra that admit a discrete $s$-sparse representation in some transform domain, we show that $\mathcal{O}(s\log N)$ random off-the-grid samples are sufficient to recover an accurate $\frac{N}{2}$-bandlimited approximation of the signal. For sparse signals (i.e., $s \ll N$), this sampling complexity is a great reduction in comparison to equispaced sampling where $\mathcal{O}(N)$ measurements are needed for the same quality of reconstruction (Nyquist-Shannon sampling theorem). We further consider noise attenuation via oversampling (relative to a desired bandwidth), a standard technique with limited theoretical understanding when the sampling positions are non-equispaced. By solving a least squares problem, we show that $\mathcal{O}(N\log N)$ i.i.d. randomly deviated samples provide an accurate $\frac{N}{2}$-bandlimited approximation of the signal with suppression of the noise energy by a factor $\sim\frac{1}{\sqrt{\log N}}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02719v2-abstract-full').style.display = 'none'; document.getElementById('2211.02719v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09624">arXiv:2207.09624</a> <span> [<a href="https://arxiv.org/pdf/2207.09624">pdf</a>, <a href="https://arxiv.org/format/2207.09624">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Learning from few examples: Classifying sex from retinal images via deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berk%2C+A">Aaron Berk</a>, <a href="/search/cs?searchtype=author&query=Ozturan%2C+G">Gulcenur Ozturan</a>, <a href="/search/cs?searchtype=author&query=Delavari%2C+P">Parsa Delavari</a>, <a href="/search/cs?searchtype=author&query=Maberley%2C+D">David Maberley</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a>, <a href="/search/cs?searchtype=author&query=Oruc%2C+I">Ipek Oruc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.09624v1-abstract-short" style="display: inline;"> Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenable to such automated approaches. Recent work in analyzing fundus images using CNNs relies on access to massive data for training and validation - hund… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09624v1-abstract-full').style.display = 'inline'; document.getElementById('2207.09624v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09624v1-abstract-full" style="display: none;"> Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenable to such automated approaches. Recent work in analyzing fundus images using CNNs relies on access to massive data for training and validation - hundreds of thousands of images. However, data residency and data privacy restrictions stymie the applicability of this approach in medical settings where patient confidentiality is a mandate. Here, we showcase results for the performance of DL on small datasets to classify patient sex from fundus images - a trait thought not to be present or quantifiable in fundus images until recently. We fine-tune a Resnet-152 model whose last layer has been modified for binary classification. In several experiments, we assess performance in the small dataset context using one private (DOVS) and one public (ODIR) data source. Our models, developed using approximately 2500 fundus images, achieved test AUC scores of up to 0.72 (95% CI: [0.67, 0.77]). This corresponds to a mere 25% decrease in performance despite a nearly 1000-fold decrease in the dataset size compared to prior work in the literature. Even with a hard task like sex categorization from retinal images, we find that classification is possible with very small datasets. Additionally, we perform domain adaptation experiments between DOVS and ODIR; explore the effect of data curation on training and generalizability; and investigate model ensembling to maximize CNN classifier performance in the context of small development datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09624v1-abstract-full').style.display = 'none'; document.getElementById('2207.09624v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 62P10; 92C50; 92C55; 94A08; 94A12 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1; I.4.7; I.4.9; I.4.10; I.5.1; I.5.2; I.5.4; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09340">arXiv:2207.09340</a> <span> [<a href="https://arxiv.org/pdf/2207.09340">pdf</a>, <a href="https://arxiv.org/format/2207.09340">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> A coherence parameter characterizing generative compressed sensing with Fourier measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berk%2C+A">Aaron Berk</a>, <a href="/search/cs?searchtype=author&query=Brugiapaglia%2C+S">Simone Brugiapaglia</a>, <a href="/search/cs?searchtype=author&query=Joshi%2C+B">Babhru Joshi</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Scott%2C+M">Matthew Scott</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+%C3%96">脰zg眉r Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.09340v5-abstract-short" style="display: inline;"> In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN). The problem of compressed sensing with GNNs has since been extensively analyzed when the measurement matrix and/or network weights follow a subgaussian distribution. We mov… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09340v5-abstract-full').style.display = 'inline'; document.getElementById('2207.09340v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09340v5-abstract-full" style="display: none;"> In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN). The problem of compressed sensing with GNNs has since been extensively analyzed when the measurement matrix and/or network weights follow a subgaussian distribution. We move beyond the subgaussian assumption, to measurement matrices that are derived by sampling uniformly at random rows of a unitary matrix (including subsampled Fourier measurements as a special case). Specifically, we prove the first known restricted isometry guarantee for generative compressed sensing with subsampled isometries and provide recovery bounds, addressing an open problem of Scarlett et al. (2022, p. 10). Recovery efficacy is characterized by the coherence, a new parameter, which measures the interplay between the range of the network and the measurement matrix. Our approach relies on subspace counting arguments and ideas central to high-dimensional probability. Furthermore, we propose a regularization strategy for training GNNs to have favourable coherence with the measurement operator. We provide compelling numerical simulations that support this regularized training strategy: our strategy yields low coherence networks that require fewer measurements for signal recovery. This, together with our theoretical results, supports coherence as a natural quantity for characterizing generative compressed sensing with subsampled isometries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09340v5-abstract-full').style.display = 'none'; document.getElementById('2207.09340v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07; 60F10; 68P30; 94A08; 94A16 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.06922">arXiv:2102.06922</a> <span> [<a href="https://arxiv.org/pdf/2102.06922">pdf</a>, <a href="https://arxiv.org/format/2102.06922">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Theoretical Performance Bound for Joint Beamformer Design of Wireless Fronthaul and Access Links in Downlink C-RAN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kadan%2C+F+E">Fehmi Emre Kadan</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.06922v1-abstract-short" style="display: inline;"> It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with fin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.06922v1-abstract-full').style.display = 'inline'; document.getElementById('2102.06922v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.06922v1-abstract-full" style="display: none;"> It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink C-RAN with a wireless fronthaul and aim to minimize total power spent by jointly designing beamformers for fronthaul and access links. We consider the case where perfect channel state information is not available in the CP. We first derive a novel theoretical performance bound for the problem defined. Then we propose four algorithms with different complexities to show the tightness of the bound. The first two algorithms apply successive convex optimizations with semi-definite relaxation idea where other two are adapted from well-known beamforming design methods. The detailed simulations under realistic channel conditions show that as the complexity of the algorithm increases, the corresponding performance becomes closer to the bound. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.06922v1-abstract-full').style.display = 'none'; document.getElementById('2102.06922v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages, single column, 11 figures, submitted to Transactions on Wireless Communications in Oct. 20, 2020. Major Revision decision was made in Jan. 16, 2021. After the revision, it will be resubmitted to the same journal until the end of February, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.06916">arXiv:2102.06916</a> <span> [<a href="https://arxiv.org/pdf/2102.06916">pdf</a>, <a href="https://arxiv.org/format/2102.06916">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Beamformer Design with Smooth Constraint-Free Approximation in Downlink Cloud Radio Access Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kadan%2C+F+E">Fehmi Emre Kadan</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.06916v1-abstract-short" style="display: inline;"> It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. I… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.06916v1-abstract-full').style.display = 'inline'; document.getElementById('2102.06916v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.06916v1-abstract-full" style="display: none;"> It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink scenario and aim to minimize total power spent by designing beamformers. We consider the case where perfect channel state information is not available in the CP. The original problem includes discontinuous terms with many constraints. We propose a novel method which transforms the problem into a smooth constraint-free form and a solution is found by the gradient descent approach. As a comparison, we consider the optimal method solving an extensive number of convex sub-problems, a known heuristic search algorithm and some sparse solution techniques. Heuristic search methods find a solution by solving a subset of all possible convex sub-problems. Sparse techniques apply some norm approximation ($\ell_0/\ell_1, \ell_0/\ell_2$) or convex approximation to make the objective function more tractable. We also derive a theoretical performance bound in order to observe how far the proposed method performs off the optimal method when running the optimal method is prohibitive due to computational complexity. Detailed simulations show that the performance of the proposed method is close to the optimal one, and it outperforms other methods analyzed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.06916v1-abstract-full').style.display = 'none'; document.getElementById('2102.06916v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 12 figures, submitted to IEEE Access in Feb. 03, 2021. It is a revised version of the paper submitted to IEEE Access in Nov. 23, 2020. Revisions were made according to the reviewer comments</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.12886">arXiv:2012.12886</a> <span> [<a href="https://arxiv.org/pdf/2012.12886">pdf</a>, <a href="https://arxiv.org/ps/2012.12886">ps</a>, <a href="https://arxiv.org/format/2012.12886">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Friedlander%2C+M+P">Michael P. Friedlander</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Halyun Jeong</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.12886v1-abstract-short" style="display: inline;"> The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. There have been several works about BIHT but a theoretical understanding of the corresponding approximation error and convergence rate still remains open. This paper shows that the normalized version of BIHT (NBHIT) achiev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12886v1-abstract-full').style.display = 'inline'; document.getElementById('2012.12886v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.12886v1-abstract-full" style="display: none;"> The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. There have been several works about BIHT but a theoretical understanding of the corresponding approximation error and convergence rate still remains open. This paper shows that the normalized version of BIHT (NBHIT) achieves an approximation error rate optimal up to logarithmic factors. More precisely, using $m$ one-bit measurements of an $s$-sparse vector $x$, we prove that the approximation error of NBIHT is of order $O \left(1 \over m \right)$ up to logarithmic factors, which matches the information-theoretic lower bound $惟\left(1 \over m \right)$ proved by Jacques, Laska, Boufounos, and Baraniuk in 2013. To our knowledge, this is the first theoretical analysis of a BIHT-type algorithm that explains the optimal rate of error decay empirically observed in the literature. This also makes NBIHT the first provable computationally-efficient one-bit compressed sensing algorithm that breaks the inverse square root error decay rate $O \left(1 \over m^{1/2} \right)$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12886v1-abstract-full').style.display = 'none'; document.getElementById('2012.12886v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to a journal</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 94-XX </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.08884">arXiv:2010.08884</a> <span> [<a href="https://arxiv.org/pdf/2010.08884">pdf</a>, <a href="https://arxiv.org/format/2010.08884">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TIT.2021.3138772">10.1109/TIT.2021.3138772 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> On the best choice of Lasso program given data parameters </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berk%2C+A">Aaron Berk</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+%C3%96">脰zg眉r Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.08884v1-abstract-short" style="display: inline;"> Generalized compressed sensing (GCS) is a paradigm in which a structured high-dimensional signal may be recovered from random, under-determined, and corrupted linear measurements. Generalized Lasso (GL) programs are effective for solving GCS problems due to their proven ability to leverage underlying signal structure. Three popular GL programs are equivalent in a sense and sometimes used interchan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08884v1-abstract-full').style.display = 'inline'; document.getElementById('2010.08884v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.08884v1-abstract-full" style="display: none;"> Generalized compressed sensing (GCS) is a paradigm in which a structured high-dimensional signal may be recovered from random, under-determined, and corrupted linear measurements. Generalized Lasso (GL) programs are effective for solving GCS problems due to their proven ability to leverage underlying signal structure. Three popular GL programs are equivalent in a sense and sometimes used interchangeably. Tuned by a governing parameter, each admit an optimal parameter choice. For sparse or low-rank signal structures, this choice yields minimax order-optimal error. While GCS is well-studied, existing theory for GL programs typically concerns this optimally tuned setting. However, the optimal parameter value for a GL program depends on properties of the data, and is typically unknown in practical settings. Performance in empirical problems thus hinges on a program's parameter sensitivity: it is desirable that small variation about the optimal parameter choice begets small variation about the optimal risk. We examine the risk for these three programs and demonstrate that their parameter sensitivity can differ for the same data. We prove a gauge-constrained GL program admits asymptotic cusp-like behaviour of its risk in the limiting low-noise regime. We prove that a residual-constrained Lasso program has asymptotically suboptimal risk for very sparse vectors. These results contrast observations about an unconstrained Lasso program, which is relatively less sensitive to its parameter choice. We support the asymptotic theory with numerical simulations, demonstrating that parameter sensitivity of GL programs is readily observed for even modest dimensional parameters. Importantly, these simulations demonstrate regimes in which a GL program exhibits sensitivity to its parameter choice, though the other two do not. We hope this work aids practitioners in selecting a GL program for their problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08884v1-abstract-full').style.display = 'none'; document.getElementById('2010.08884v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 90C47; 90C25; 94B75; 46N10; 47L07; <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> E.4; H.1.1; G.1.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.08467">arXiv:2007.08467</a> <span> [<a href="https://arxiv.org/pdf/2007.08467">pdf</a>, <a href="https://arxiv.org/ps/2007.08467">ps</a>, <a href="https://arxiv.org/format/2007.08467">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%9C%C3%A7%C3%BCnc%C3%BC%2C+A+B">Ali Bulut 脺莽眉nc眉</a>, <a href="/search/cs?searchtype=author&query=G%C3%BCvensen%2C+G+M">G枚khan Muzaffer G眉vensen</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.08467v3-abstract-short" style="display: inline;"> Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative rece… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.08467v3-abstract-full').style.display = 'inline'; document.getElementById('2007.08467v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.08467v3-abstract-full" style="display: none;"> Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.08467v3-abstract-full').style.display = 'none'; document.getElementById('2007.08467v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.07594">arXiv:2002.07594</a> <span> [<a href="https://arxiv.org/pdf/2002.07594">pdf</a>, <a href="https://arxiv.org/format/2002.07594">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TCOMM.2019.2954512">10.1109/TCOMM.2019.2954512 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of Quantized Uplink Massive MIMO-OFDM With Oversampling Under Adjacent Channel Interference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%9C%C3%A7%C3%BCnc%C3%BC%2C+A+B">Ali Bulut 脺莽眉nc眉</a>, <a href="/search/cs?searchtype=author&query=Bj%C3%B6rnson%2C+E">Emil Bj枚rnson</a>, <a href="/search/cs?searchtype=author&query=Johansson%2C+H">H氓kan Johansson</a>, <a href="/search/cs?searchtype=author&query=Larsson%2C+E+G">Erik G. Larsson</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.07594v1-abstract-short" style="display: inline;"> Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single-antenna systems. Owing to the many antennas, low-cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with low-resolution analog-to-digital converters (ADC) have been investigated in many studies. Howev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07594v1-abstract-full').style.display = 'inline'; document.getElementById('2002.07594v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.07594v1-abstract-full" style="display: none;"> Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single-antenna systems. Owing to the many antennas, low-cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with low-resolution analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with low-resolution ADCs under frequency selective fading with orthogonal frequency division multiplexing in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic capacity. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07594v1-abstract-full').style.display = 'none'; document.getElementById('2002.07594v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.10631">arXiv:2001.10631</a> <span> [<a href="https://arxiv.org/pdf/2001.10631">pdf</a>, <a href="https://arxiv.org/ps/2001.10631">ps</a>, <a href="https://arxiv.org/format/2001.10631">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Sub-Gaussian Matrices on Sets: Optimal Tail Dependence and Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Halyun Jeong</a>, <a href="/search/cs?searchtype=author&query=Li%2C+X">Xiaowei Li</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.10631v2-abstract-short" style="display: inline;"> Random linear mappings are widely used in modern signal processing, compressed sensing and machine learning. These mappings may be used to embed the data into a significantly lower dimension while at the same time preserving useful information. This is done by approximately preserving the distances between data points, which are assumed to belong to $\mathbb{R}^n$. Thus, the performance of these m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.10631v2-abstract-full').style.display = 'inline'; document.getElementById('2001.10631v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.10631v2-abstract-full" style="display: none;"> Random linear mappings are widely used in modern signal processing, compressed sensing and machine learning. These mappings may be used to embed the data into a significantly lower dimension while at the same time preserving useful information. This is done by approximately preserving the distances between data points, which are assumed to belong to $\mathbb{R}^n$. Thus, the performance of these mappings is usually captured by how close they are to an isometry on the data. Gaussian linear mappings have been the object of much study, while the sub-Gaussian settings is not yet fully understood. In the latter case, the performance depends on the sub-Gaussian norm of the rows. In many applications, e.g., compressed sensing, this norm may be large, or even growing with dimension, and thus it is important to characterize this dependence. We study when a sub-Gaussian matrix can become a near isometry on a set, show that previous best known dependence on the sub-Gaussian norm was sub-optimal, and present the optimal dependence. Our result not only answers a remaining question posed by Liaw, Mehrabian, Plan and Vershynin in 2017, but also generalizes their work. We also develop a new Bernstein type inequality for sub-exponential random variables, and a new Hanson-Wright inequality for quadratic forms of sub-Gaussian random variables, in both cases improving the bounds in the sub-Gaussian regime under moment constraints. Finally, we illustrate popular applications such as Johnson-Lindenstrauss embeddings, null space property for 0-1 matrices, randomized sketches and blind demodulation, whose theoretical guarantees can be improved by our results (in the sub-Gaussian case). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.10631v2-abstract-full').style.display = 'none'; document.getElementById('2001.10631v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.07525">arXiv:1911.07525</a> <span> [<a href="https://arxiv.org/pdf/1911.07525">pdf</a>, <a href="https://arxiv.org/format/1911.07525">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> </div> </div> <p class="title is-5 mathjax"> On one-stage recovery for $危螖$-quantized compressed sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arian%2C+A">Arman Arian</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.07525v1-abstract-short" style="display: inline;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. When such a signal is acquired according to the principles of CS, the measurements still take on values in the continuum. In today's "digital" world, a subsequent quantization step, where these measurements are replaced with elements from a finite se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07525v1-abstract-full').style.display = 'inline'; document.getElementById('1911.07525v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.07525v1-abstract-full" style="display: none;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. When such a signal is acquired according to the principles of CS, the measurements still take on values in the continuum. In today's "digital" world, a subsequent quantization step, where these measurements are replaced with elements from a finite set is crucial. We focus on one of the approaches that yield efficient quantizers for CS: $危螖$ quantization, followed by a one-stage tractable reconstruction method, which was developed by Saab et al. with theoretical error guarantees in the case of sub-Gaussian matrices. We propose two alternative approaches that extend this result to a wider class of measurement matrices including (certain unitary transforms of) partial bounded orthonormal systems and deterministic constructions based on chirp sensing matrices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07525v1-abstract-full').style.display = 'none'; document.getElementById('1911.07525v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.07497">arXiv:1911.07497</a> <span> [<a href="https://arxiv.org/pdf/1911.07497">pdf</a>, <a href="https://arxiv.org/format/1911.07497">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> </div> </div> <p class="title is-5 mathjax"> Deterministic partial binary circulant compressed sensing matrices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arian%2C+A">Arman Arian</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.07497v1-abstract-short" style="display: inline;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". Most of matrices used in CS are random matrices as they satisfy the restricted isometry proper… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07497v1-abstract-full').style.display = 'inline'; document.getElementById('1911.07497v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.07497v1-abstract-full" style="display: none;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". Most of matrices used in CS are random matrices as they satisfy the restricted isometry property (RIP) in an optimal regime of number of measurements with high probability. However, these matrices have their own caveats and for this reason, deterministic measurement matrices have been proposed. While there is a wide classes of deterministic matrices in the literature, we propose a novel class of deterministic matrices using the Legendre symbol. This construction has a simple structure, it enjoys being a binary matrix, and having a partial circulant structure which provides a fast matrix-vector multiplication and a fast reconstruction algorithm. We will derive a bound on the sparsity level of signals that can be measured (and be reconstructed) with this class of matrices. We perform quantization using these matrices, and we verify the performance of these matrices (and compare with other existing constructions) numerically. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07497v1-abstract-full').style.display = 'none'; document.getElementById('1911.07497v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.07428">arXiv:1911.07428</a> <span> [<a href="https://arxiv.org/pdf/1911.07428">pdf</a>, <a href="https://arxiv.org/format/1911.07428">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> </div> </div> <p class="title is-5 mathjax"> RIP constants for deterministic compressed sensing matrices-beyond Gershgorin </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arian%2C+A">Arman Arian</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.07428v1-abstract-short" style="display: inline;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". If the measurement satisfies the so-called restricted isometry property (RIP), then it will b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07428v1-abstract-full').style.display = 'inline'; document.getElementById('1911.07428v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.07428v1-abstract-full" style="display: none;"> Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". If the measurement satisfies the so-called restricted isometry property (RIP), then it will be appropriate to be used in compressed sensing. While a wide class of random matrices provably satisfy the RIP with high probability, explicit and deterministic constructions have been shown (so far) to satisfy the RIP only in a significantly suboptimal regime. In this paper, we propose two novel approaches for improving the RIP constant estimates based on Gershgorin circle theorem for a specific deterministic construction based on Paley tight frames, obtaining an improvement over the Gershgorin bound by a multiplicative constant. In one approach we use a recent result on the spectra of the skew-adjacency matrices of oriented graphs. In the other approach, we use the so-called Dembo bounds on the extreme eigenvalues of a positive semidefinite Hermitian matrix. We also generalize these bounds and we combine the new bounds with a conjecture we make regarding the distribution of quadratic residues in a finite field to provide a potential path to break the so-called "square-root barrier"-we provide a proof based on the assumption that the conjecture holds. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.07428v1-abstract-full').style.display = 'none'; document.getElementById('1911.07428v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.11968">arXiv:1810.11968</a> <span> [<a href="https://arxiv.org/pdf/1810.11968">pdf</a>, <a href="https://arxiv.org/format/1810.11968">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/imaiai/iaaa014">10.1093/imaiai/iaaa014 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Sensitivity of $\ell_{1}$ minimization to parameter choice </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berk%2C+A">Aaron Berk</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+%C3%96">脰zg眉r Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.11968v3-abstract-short" style="display: inline;"> The use of generalized LASSO is a common technique for recovery of structured high-dimensional signals. Each generalized LASSO program has a governing parameter whose optimal value depends on properties of the data. At this optimal value, compressed sensing theory explains why LASSO programs recover structured high-dimensional signals with minimax order-optimal error. Unfortunately in practice, th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.11968v3-abstract-full').style.display = 'inline'; document.getElementById('1810.11968v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.11968v3-abstract-full" style="display: none;"> The use of generalized LASSO is a common technique for recovery of structured high-dimensional signals. Each generalized LASSO program has a governing parameter whose optimal value depends on properties of the data. At this optimal value, compressed sensing theory explains why LASSO programs recover structured high-dimensional signals with minimax order-optimal error. Unfortunately in practice, the optimal choice is generally unknown and must be estimated. Thus, we investigate stability of each LASSO program with respect to its governing parameter. Our goal is to aid the practitioner in answering the following question: given real data, which LASSO program should be used? We take a step towards answering this by analyzing the case where the measurement matrix is identity (the so-called proximal denoising setup) and we use $\ell_{1}$ regularization. For each LASSO program, we specify settings in which that program is provably unstable with respect to its governing parameter. We support our analysis with detailed numerical simulations. For example, there are settings where a 0.1% underestimate of a LASSO parameter can increase the error significantly; and a 50% underestimate can cause the error to increase by a factor of $10^{9}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.11968v3-abstract-full').style.display = 'none'; document.getElementById('1810.11968v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 90C31; 90C47; 94A15; 60D05 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.00108">arXiv:1804.00108</a> <span> [<a href="https://arxiv.org/pdf/1804.00108">pdf</a>, <a href="https://arxiv.org/ps/1804.00108">ps</a>, <a href="https://arxiv.org/format/1804.00108">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSP.2018.2879031">10.1109/TSP.2018.2879031 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Learning tensors from partial binary measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghadermarzy%2C+N">Navid Ghadermarzy</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1804.00108v1-abstract-short" style="display: inline;"> In this paper we generalize the 1-bit matrix completion problem to higher order tensors. We prove that when $r=O(1)$ a bounded rank-$r$, order-$d$ tensor $T$ in $\mathbb{R}^{N} \times \mathbb{R}^{N} \times \cdots \times \mathbb{R}^{N}$ can be estimated efficiently by only $m=O(Nd)$ binary measurements by regularizing its max-qnorm and M-norm as surrogates for its rank. We prove that similar to the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.00108v1-abstract-full').style.display = 'inline'; document.getElementById('1804.00108v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.00108v1-abstract-full" style="display: none;"> In this paper we generalize the 1-bit matrix completion problem to higher order tensors. We prove that when $r=O(1)$ a bounded rank-$r$, order-$d$ tensor $T$ in $\mathbb{R}^{N} \times \mathbb{R}^{N} \times \cdots \times \mathbb{R}^{N}$ can be estimated efficiently by only $m=O(Nd)$ binary measurements by regularizing its max-qnorm and M-norm as surrogates for its rank. We prove that similar to the matrix case, i.e., when $d=2$, the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is the same as recovering it from unquantized measurements. Moreover, we show the advantage of using 1-bit tensor completion over matricization both theoretically and numerically. Specifically, we show how the 1-bit measurement model can be used for context-aware recommender systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.00108v1-abstract-full').style.display = 'none'; document.getElementById('1804.00108v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 62B10; 94A17; 15A69; 62D05 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> H.3.3; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.04965">arXiv:1711.04965</a> <span> [<a href="https://arxiv.org/pdf/1711.04965">pdf</a>, <a href="https://arxiv.org/ps/1711.04965">ps</a>, <a href="https://arxiv.org/format/1711.04965">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Near-optimal sample complexity for convex tensor completion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghadermarzy%2C+N">Navid Ghadermarzy</a>, <a href="/search/cs?searchtype=author&query=Plan%2C+Y">Yaniv Plan</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.04965v1-abstract-short" style="display: inline;"> We analyze low rank tensor completion (TC) using noisy measurements of a subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots \times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm minimization problem. However, this bound is significantly larger than the number of free v… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.04965v1-abstract-full').style.display = 'inline'; document.getElementById('1711.04965v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.04965v1-abstract-full" style="display: none;"> We analyze low rank tensor completion (TC) using noisy measurements of a subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots \times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm minimization problem. However, this bound is significantly larger than the number of free variables in a low rank tensor which is $O(dN)$. In this paper, we show that by using an atomic-norm whose atoms are rank-$1$ sign tensors, one can obtain a sample complexity of $O(dN)$. Moreover, we generalize the matrix max-norm definition to tensors, which results in a max-quasi-norm (max-qnorm) whose unit ball has small Rademacher complexity. We prove that solving a constrained least squares estimation using either the convex atomic-norm or the nonconvex max-qnorm results in optimal sample complexity for the problem of low-rank tensor completion. Furthermore, we show that these bounds are nearly minimax rate-optimal. We also provide promising numerical results for max-qnorm constrained tensor completion, showing improved recovery results compared to matricization and alternating least squares. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.04965v1-abstract-full').style.display = 'none'; document.getElementById('1711.04965v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 62H12; 94A15; 15A69 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1608.03462">arXiv:1608.03462</a> <span> [<a href="https://arxiv.org/pdf/1608.03462">pdf</a>, <a href="https://arxiv.org/ps/1608.03462">ps</a>, <a href="https://arxiv.org/format/1608.03462">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Multi-View Product Image Search Using Deep ConvNets Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bastan%2C+M">Muhammet Bastan</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1608.03462v2-abstract-short" style="display: inline;"> Multi-view product image queries can improve retrieval performance over single view queries significantly. In this paper, we investigated the performance of deep convolutional neural networks (ConvNets) on multi-view product image search. First, we trained a VGG-like network to learn deep ConvNets representations of product images. Then, we computed the deep ConvNets representations of database an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.03462v2-abstract-full').style.display = 'inline'; document.getElementById('1608.03462v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1608.03462v2-abstract-full" style="display: none;"> Multi-view product image queries can improve retrieval performance over single view queries significantly. In this paper, we investigated the performance of deep convolutional neural networks (ConvNets) on multi-view product image search. First, we trained a VGG-like network to learn deep ConvNets representations of product images. Then, we computed the deep ConvNets representations of database and query images and performed single view queries, and multi-view queries using several early and late fusion approaches. We performed extensive experiments on the publicly available Multi-View Object Image Dataset (MVOD 5K) with both clean background queries from the Internet and cluttered background queries from a mobile phone. We compared the performance of ConvNets to the classical bag-of-visual-words (BoWs). We concluded that (1) multi-view queries with deep ConvNets representations perform significantly better than single view queries, (2) ConvNets perform much better than BoWs and have room for further improvement, (3) pre-training of ConvNets on a different image dataset with background clutter is needed to obtain good performance on cluttered product image queries obtained with a mobile phone. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.03462v2-abstract-full').style.display = 'none'; document.getElementById('1608.03462v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 August, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 16 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.08990">arXiv:1606.08990</a> <span> [<a href="https://arxiv.org/pdf/1606.08990">pdf</a>, <a href="https://arxiv.org/ps/1606.08990">ps</a>, <a href="https://arxiv.org/format/1606.08990">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Extended OOBE Comparisons for OFDM, GFDM and WCP-COQAM at Equal Spectral Efficiency </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%9C%C3%A7%C3%BCnc%C3%BC%2C+A+B">Ali Bulut 脺莽眉nc眉</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1606.08990v1-abstract-short" style="display: inline;"> Generalized frequency division multiplexing (GFDM), windowed cyclic prefix circular offset quadrature amplitude modulation (WCP-COQAM) and orthogonal frequency division multiplexing (OFDM) are among the candidate 5G modulation formats. In this study, we present additional results for the OOBE comparisons between OFDM, GFDM and WCP-COQAM under equal or unequal spectral efficiency conditions for var… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.08990v1-abstract-full').style.display = 'inline'; document.getElementById('1606.08990v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.08990v1-abstract-full" style="display: none;"> Generalized frequency division multiplexing (GFDM), windowed cyclic prefix circular offset quadrature amplitude modulation (WCP-COQAM) and orthogonal frequency division multiplexing (OFDM) are among the candidate 5G modulation formats. In this study, we present additional results for the OOBE comparisons between OFDM, GFDM and WCP-COQAM under equal or unequal spectral efficiency conditions for various simulation scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.08990v1-abstract-full').style.display = 'none'; document.getElementById('1606.08990v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1603.04408">arXiv:1603.04408</a> <span> [<a href="https://arxiv.org/pdf/1603.04408">pdf</a>, <a href="https://arxiv.org/ps/1603.04408">ps</a>, <a href="https://arxiv.org/format/1603.04408">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> U-CATCH: Using Color ATtribute of image patCHes in binary descriptors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdulkhaev%2C+A">Alisher Abdulkhaev</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1603.04408v3-abstract-short" style="display: inline;"> In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework. Our method expands the dimension of binary comparisons into RGB and YCbCr spaces, showing more than 100% matching improve ment compared to non-color binary descriptors for a wide range of hard-to-match cases. The proposed method is general and can be applied t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1603.04408v3-abstract-full').style.display = 'inline'; document.getElementById('1603.04408v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1603.04408v3-abstract-full" style="display: none;"> In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework. Our method expands the dimension of binary comparisons into RGB and YCbCr spaces, showing more than 100% matching improve ment compared to non-color binary descriptors for a wide range of hard-to-match cases. The proposed method is general and can be applied to any binary descriptor to make it color sensitive. It is faster than classical binary descriptors for RGB sampling due to the abandonment of grayscale conversion and has almost identical complexity (insignificant compared to smoothing operation) for YCbCr sampling. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1603.04408v3-abstract-full').style.display = 'none'; document.getElementById('1603.04408v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 March, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1602.07250">arXiv:1602.07250</a> <span> [<a href="https://arxiv.org/pdf/1602.07250">pdf</a>, <a href="https://arxiv.org/ps/1602.07250">ps</a>, <a href="https://arxiv.org/format/1602.07250">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Reducing MIMO Detection Complexity via Hierarchical Modulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ugur%2C+Y">Yigit Ugur</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">Ali Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1602.07250v1-abstract-short" style="display: inline;"> This work considers multiple-input multiple-output (MIMO) communication systems using hierarchical modulation. A disadvantage of the maximum-likelihood (ML) MIMO detector is that computational complexity increases exponentially with the number of transmit antennas. To reduce complexity, we propose a hierarchical modulation scheme to be used in MIMO trans- mission where base and enhancement layers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.07250v1-abstract-full').style.display = 'inline'; document.getElementById('1602.07250v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1602.07250v1-abstract-full" style="display: none;"> This work considers multiple-input multiple-output (MIMO) communication systems using hierarchical modulation. A disadvantage of the maximum-likelihood (ML) MIMO detector is that computational complexity increases exponentially with the number of transmit antennas. To reduce complexity, we propose a hierarchical modulation scheme to be used in MIMO trans- mission where base and enhancement layers are incorporated. In the proposed receiver, the base layer is detected first with a minimum mean square error (MMSE) detector which is followed by ML detection of the enhancement layer. Our results indicate that the proposed low complexity scheme does not compromise performance when design parameters such as code rates and constellation ratio are chosen carefully. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.07250v1-abstract-full').style.display = 'none'; document.getElementById('1602.07250v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 5 figures, submitted to IEEE Communications Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1510.01201">arXiv:1510.01201</a> <span> [<a href="https://arxiv.org/pdf/1510.01201">pdf</a>, <a href="https://arxiv.org/ps/1510.01201">ps</a>, <a href="https://arxiv.org/format/1510.01201">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Out-of-Band Radiation Comparison of GFDM, WCP-COQAM and OFDM at Equal Spectral Efficiency </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%9C%C3%A7%C3%BCnc%C3%BC%2C+A+B">Ali Bulut 脺莽眉nc眉</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1510.01201v1-abstract-short" style="display: inline;"> GFDM and WCP-COQAM are amongst the candidate physical layer modulation formats to be used in 5G, whose claimed lower out-of-band (OOB) emissions are important with respect to cognitive radio based dynamic spectrum access solutions. In this study, we compare OFDM, GFDM and WCP-COQAM in terms of OOB emissions in a fair manner such that their spectral efficiencies are the same and OOB emission reduct… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.01201v1-abstract-full').style.display = 'inline'; document.getElementById('1510.01201v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1510.01201v1-abstract-full" style="display: none;"> GFDM and WCP-COQAM are amongst the candidate physical layer modulation formats to be used in 5G, whose claimed lower out-of-band (OOB) emissions are important with respect to cognitive radio based dynamic spectrum access solutions. In this study, we compare OFDM, GFDM and WCP-COQAM in terms of OOB emissions in a fair manner such that their spectral efficiencies are the same and OOB emission reduction techniques are applied to all of the modulation types. Analytical PSD expressions are also correlated with the simulation based OOB emission results. Maintaining the same spectral efficiency, carrier frequency offset immunities will also be compared. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1510.01201v1-abstract-full').style.display = 'none'; document.getElementById('1510.01201v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">submitted to IEEE Signal Processing Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1509.00977">arXiv:1509.00977</a> <span> [<a href="https://arxiv.org/pdf/1509.00977">pdf</a>, <a href="https://arxiv.org/ps/1509.00977">ps</a>, <a href="https://arxiv.org/format/1509.00977">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Pulse Shaping Methods for OQAM/OFDM and WCP-COQAM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C3%9C%C3%A7%C3%BCnc%C3%BC%2C+A+B">Ali Bulut 脺莽眉nc眉</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1509.00977v1-abstract-short" style="display: inline;"> GFDM is a new modulation format whose advantages compared to OFDM reportedly make it a preferable modulation format for 5G. However, the non-orthogonal nature of GFDM with matched filtering (MF) receiver for pulses with good time-frequency localization is one of its disadvantages, leading to the proposal of WCP-COQAM, employing offset quadrature amplitude modulation (OQAM). In this paper, we prove… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.00977v1-abstract-full').style.display = 'inline'; document.getElementById('1509.00977v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1509.00977v1-abstract-full" style="display: none;"> GFDM is a new modulation format whose advantages compared to OFDM reportedly make it a preferable modulation format for 5G. However, the non-orthogonal nature of GFDM with matched filtering (MF) receiver for pulses with good time-frequency localization is one of its disadvantages, leading to the proposal of WCP-COQAM, employing offset quadrature amplitude modulation (OQAM). In this paper, we prove that a pulse satisfying orthogonality conditions for OQAM-OFDM will also satisfy orthogonality with WCP-COQAM, thus the pulse design methods developed for OQAM-OFDM can also be used with WCP-COQAM. This statement is also verified by the simulation based results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.00977v1-abstract-full').style.display = 'none'; document.getElementById('1509.00977v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 September, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 1 figure, submitted to IEEE signal processing letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1505.01350">arXiv:1505.01350</a> <span> [<a href="https://arxiv.org/pdf/1505.01350">pdf</a>, <a href="https://arxiv.org/ps/1505.01350">ps</a>, <a href="https://arxiv.org/format/1505.01350">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Classification of Occluded Objects using Fast Recurrent Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1505.01350v1-abstract-short" style="display: inline;"> Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforwa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.01350v1-abstract-full').style.display = 'inline'; document.getElementById('1505.01350v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1505.01350v1-abstract-full" style="display: none;"> Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2$\times$ improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1505.01350v1-abstract-full').style.display = 'none'; document.getElementById('1505.01350v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 May, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:1409.8576 by other authors</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1504.00984">arXiv:1504.00984</a> <span> [<a href="https://arxiv.org/pdf/1504.00984">pdf</a>, <a href="https://arxiv.org/format/1504.00984">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Sparse regression with highly correlated predictors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghorbani%2C+B">Behrooz Ghorbani</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1504.00984v1-abstract-short" style="display: inline;"> We consider a linear regression $y=X尾+u$ where $X\in\mathbb{\mathbb{R}}^{n\times p}$, $p\gg n,$ and $尾$ is $s$-sparse. Motivated by examples in financial and economic data, we consider the situation where $X$ has highly correlated and clustered columns. To perform sparse recovery in this setting, we introduce the \emph{clustering removal algorithm} (CRA), that seeks to decrease the correlation in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1504.00984v1-abstract-full').style.display = 'inline'; document.getElementById('1504.00984v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1504.00984v1-abstract-full" style="display: none;"> We consider a linear regression $y=X尾+u$ where $X\in\mathbb{\mathbb{R}}^{n\times p}$, $p\gg n,$ and $尾$ is $s$-sparse. Motivated by examples in financial and economic data, we consider the situation where $X$ has highly correlated and clustered columns. To perform sparse recovery in this setting, we introduce the \emph{clustering removal algorithm} (CRA), that seeks to decrease the correlation in $X$ by removing the cluster structure without changing the parameter vector $尾$. We show that as long as certain assumptions hold about $X$, the decorrelated matrix will satisfy the restricted isometry property (RIP) with high probability. We also provide examples of the empirical performance of CRA and compare it with other sparse recovery techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1504.00984v1-abstract-full').style.display = 'none'; document.getElementById('1504.00984v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1504.00087">arXiv:1504.00087</a> <span> [<a href="https://arxiv.org/pdf/1504.00087">pdf</a>, <a href="https://arxiv.org/ps/1504.00087">ps</a>, <a href="https://arxiv.org/format/1504.00087">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Quantization of compressive samples with stable and robust recovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saab%2C+R">Rayan Saab</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+R">Rongrong Wang</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1504.00087v1-abstract-short" style="display: inline;"> In this paper we study the quantization stage that is implicit in any compressed sensing signal acquisition paradigm. We propose using Sigma-Delta quantization and a subsequent reconstruction scheme based on convex optimization. We prove that the reconstruction error due to quantization decays polynomially in the number of measurements. Our results apply to arbitrary signals, including compressibl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1504.00087v1-abstract-full').style.display = 'inline'; document.getElementById('1504.00087v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1504.00087v1-abstract-full" style="display: none;"> In this paper we study the quantization stage that is implicit in any compressed sensing signal acquisition paradigm. We propose using Sigma-Delta quantization and a subsequent reconstruction scheme based on convex optimization. We prove that the reconstruction error due to quantization decays polynomially in the number of measurements. Our results apply to arbitrary signals, including compressible ones, and account for measurement noise. Additionally, they hold for sub-Gaussian (including Gaussian and Bernoulli) random compressed sensing measurements, as well as for both high bit-depth and coarse quantizers, and they extend to 1-bit quantization. In the noise-free case, when the signal is strictly sparse we prove that by optimizing the order of the quantization scheme one can obtain root-exponential decay in the reconstruction error due to quantization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1504.00087v1-abstract-full').style.display = 'none'; document.getElementById('1504.00087v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1503.00851">arXiv:1503.00851</a> <span> [<a href="https://arxiv.org/pdf/1503.00851">pdf</a>, <a href="https://arxiv.org/ps/1503.00851">ps</a>, <a href="https://arxiv.org/format/1503.00851">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1503.00851v3-abstract-short" style="display: inline;"> We introduce a novel framework of reservoir computing, that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. Th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.00851v3-abstract-full').style.display = 'inline'; document.getElementById('1503.00851v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1503.00851v3-abstract-full" style="display: none;"> We introduce a novel framework of reservoir computing, that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is capable of long short-term memory and it requires orders of magnitude less computation compared to Echo State Networks. We prove that cellular automaton reservoir holds a distributed representation of attribute statistics, which provides a more effective computation than local representation. It is possible to estimate the kernel for linear cellular automata via metric learning, that enables a much more efficient distance computation in support vector machine framework. Also, binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.00851v3-abstract-full').style.display = 'none'; document.getElementById('1503.00851v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 April, 2015; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Corrected Typos. Responded some comments on section 8. Added appendix for details. Recurrent architecture emphasized</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1502.05807">arXiv:1502.05807</a> <span> [<a href="https://arxiv.org/pdf/1502.05807">pdf</a>, <a href="https://arxiv.org/format/1502.05807">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Functional Analysis">math.FA</span> </div> </div> <p class="title is-5 mathjax"> Noise-shaping Quantization Methods for Frame-based and Compressive Sampling Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chou%2C+E">Evan Chou</a>, <a href="/search/cs?searchtype=author&query=G%C3%BCnt%C3%BCrk%2C+C+S">C. Sinan G眉nt眉rk</a>, <a href="/search/cs?searchtype=author&query=Krahmer%2C+F">Felix Krahmer</a>, <a href="/search/cs?searchtype=author&query=Saab%2C+R">Rayan Saab</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1502.05807v1-abstract-short" style="display: inline;"> Noise shaping refers to an analog-to-digital conversion methodology in which quantization error is arranged to lie mostly outside the signal spectrum by means of oversampling and feedback. Recently it has been successfully applied to more general redundant linear sampling and reconstruction systems associated with frames as well as non-linear systems associated with compressive sampling. This chap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1502.05807v1-abstract-full').style.display = 'inline'; document.getElementById('1502.05807v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1502.05807v1-abstract-full" style="display: none;"> Noise shaping refers to an analog-to-digital conversion methodology in which quantization error is arranged to lie mostly outside the signal spectrum by means of oversampling and feedback. Recently it has been successfully applied to more general redundant linear sampling and reconstruction systems associated with frames as well as non-linear systems associated with compressive sampling. This chapter reviews some of the recent progress in this subject. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1502.05807v1-abstract-full').style.display = 'none'; document.getElementById('1502.05807v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 2 figures. chapter in the SAMPTA 2013 book</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1410.0162">arXiv:1410.0162</a> <span> [<a href="https://arxiv.org/pdf/1410.0162">pdf</a>, <a href="https://arxiv.org/ps/1410.0162">ps</a>, <a href="https://arxiv.org/format/1410.0162">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Reservoir Computing using Cellular Automata </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1410.0162v1-abstract-short" style="display: inline;"> We introduce a novel framework of reservoir computing. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.0162v1-abstract-full').style.display = 'inline'; document.getElementById('1410.0162v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1410.0162v1-abstract-full" style="display: none;"> We introduce a novel framework of reservoir computing. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is capable of long short-term memory and it requires orders of magnitude less computation compared to Echo State Networks. Also, for additive cellular automaton rules, reservoir features can be combined using Boolean operations, which provides a direct way for concept building and symbolic processing, and it is much more efficient compared to state-of-the-art approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1410.0162v1-abstract-full').style.display = 'none'; document.getElementById('1410.0162v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1405.3057">arXiv:1405.3057</a> <span> [<a href="https://arxiv.org/pdf/1405.3057">pdf</a>, <a href="https://arxiv.org/ps/1405.3057">ps</a>, <a href="https://arxiv.org/format/1405.3057">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2016.2640962">10.1109/TWC.2016.2640962 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Low-Complexity Graph-Based LMMSE Receiver for MIMO ISI Channels with M-QAM Modulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sen%2C+P">Pinar Sen</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">Ali Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1405.3057v3-abstract-short" style="display: inline;"> In this paper, we propose a low complexity graph-based linear minimum mean square error (LMMSE) equalizer in order to remove inter-symbol and inter-stream interference in multiple input multiple output (MIMO) communication. The proposed state space representation inflicted on the graph provides linearly increasing computational complexity with block length. Also, owing to the Gaussian assumption u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1405.3057v3-abstract-full').style.display = 'inline'; document.getElementById('1405.3057v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1405.3057v3-abstract-full" style="display: none;"> In this paper, we propose a low complexity graph-based linear minimum mean square error (LMMSE) equalizer in order to remove inter-symbol and inter-stream interference in multiple input multiple output (MIMO) communication. The proposed state space representation inflicted on the graph provides linearly increasing computational complexity with block length. Also, owing to the Gaussian assumption used in the presented cycle-free factor graph, the complexity of the suggested equalizer structure is not affected by the size of the signalling space. In addition, we introduce an efficient way of computing extrinsic bit log-likelihood ratio (LLR) values for LMMSE estimation compatible with higher order alphabets which is shown to perform better than the other methods in the literature. Overall, we provide an efficient receiver structure reaching high data rates in frequency selective MIMO systems whose performance is shown to be very close to a genie-aided matched filter bound through extensive simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1405.3057v3-abstract-full').style.display = 'none'; document.getElementById('1405.3057v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 May, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">28 pages, 6 figures, 2 tables, submitted to IEEE Transactions on Wireless Communications</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Transactions on Wireless Communications , vol.PP, no.99, pp.1-1, Dec. 2016 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1401.1016">arXiv:1401.1016</a> <span> [<a href="https://arxiv.org/pdf/1401.1016">pdf</a>, <a href="https://arxiv.org/ps/1401.1016">ps</a>, <a href="https://arxiv.org/format/1401.1016">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Factor Graph Based LMMSE Filtering for Colored Gaussian Processes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sen%2C+P">Pinar Sen</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">Ali Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1401.1016v2-abstract-short" style="display: inline;"> We propose a low complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white characteristics of a random process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.1016v2-abstract-full').style.display = 'inline'; document.getElementById('1401.1016v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1401.1016v2-abstract-full" style="display: none;"> We propose a low complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white characteristics of a random process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The proposed method can be used with any random process with a known autocorrelation function with the help of an approximation to an autoregressive (AR) process. We show through extensive simulations that our method performs very close to the optimal block LMMSE filtering for Gaussian input signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1401.1016v2-abstract-full').style.display = 'none'; document.getElementById('1401.1016v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 January, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1312.1593">arXiv:1312.1593</a> <span> [<a href="https://arxiv.org/pdf/1312.1593">pdf</a>, <a href="https://arxiv.org/ps/1312.1593">ps</a>, <a href="https://arxiv.org/format/1312.1593">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of Network Coded Systems Under Quasi-static Rayleigh Fading Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aktas%2C+T">Tugcan Aktas</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">A. Ozgur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Aktas%2C+E">Emre Aktas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1312.1593v1-abstract-short" style="display: inline;"> In the area of basic and network coded cooperative communication, the expected end-to-end bit error rate (BER) values are frequently required to compare the proposed coding, relaying, and decoding techniques. Instead of obtaining these values via time consuming Monte Carlo simulations, deriving closed form expressions using approximations is crucial. In this work, the ultimate goal is to derive an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1312.1593v1-abstract-full').style.display = 'inline'; document.getElementById('1312.1593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1312.1593v1-abstract-full" style="display: none;"> In the area of basic and network coded cooperative communication, the expected end-to-end bit error rate (BER) values are frequently required to compare the proposed coding, relaying, and decoding techniques. Instead of obtaining these values via time consuming Monte Carlo simulations, deriving closed form expressions using approximations is crucial. In this work, the ultimate goal is to derive an approximate average BER expression for a network coded system. While reaching this goal, we firstly consider the cooperative systems' instantaneous BER values that are commonly composed of Q-functions of more than one variables. For these Q-functions, we investigate the convergence characteristics of the sampling property and generalize this property to arbitrary functions of multiple variables. Second, we adapt the equivalent channel approach to the network coded scenario for the ease of analysis and propose a network decoder with reduced complexity. Finally, by combining these techniques, we show that the obtained closed form expressions well agree with simulation results in a wide SNR range. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1312.1593v1-abstract-full').style.display = 'none'; document.getElementById('1312.1593v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 7 figures, Submitted to IEEE Transactions on Communications. arXiv admin note: text overlap with arXiv:1301.6471</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1311.3773">arXiv:1311.3773</a> <span> [<a href="https://arxiv.org/pdf/1311.3773">pdf</a>, <a href="https://arxiv.org/format/1311.3773">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Non-Convex Compressed Sensing Using Partial Support Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghadermarzy%2C+N">Navid Ghadermarzy</a>, <a href="/search/cs?searchtype=author&query=Mansour%2C+H">Hassan Mansour</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1311.3773v1-abstract-short" style="display: inline;"> In this paper we address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that weighted $\ell_p$ minimization with $0<p<1$ is stable and robust under weaker sufficient conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient recovery conditions of we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.3773v1-abstract-full').style.display = 'inline'; document.getElementById('1311.3773v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1311.3773v1-abstract-full" style="display: none;"> In this paper we address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that weighted $\ell_p$ minimization with $0<p<1$ is stable and robust under weaker sufficient conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient recovery conditions of weighted $\ell_p$ are weaker than those of regular $\ell_p$ minimization if at least $50%$ of the support estimate is accurate. We also review some algorithms which exist to solve the non-convex $\ell_p$ problem and illustrate our results with numerical experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1311.3773v1-abstract-full').style.display = 'none'; document.getElementById('1311.3773v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 94A12; 94A20; 94A08 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1310.3381">arXiv:1310.3381</a> <span> [<a href="https://arxiv.org/pdf/1310.3381">pdf</a>, <a href="https://arxiv.org/ps/1310.3381">ps</a>, <a href="https://arxiv.org/format/1310.3381">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/WCNC.2014.6952123">10.1109/WCNC.2014.6952123 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Low-Complexity Graph-Based LMMSE Receiver Designed for Colored Noise Induced by FTN-Signaling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sen%2C+P">Pinar Sen</a>, <a href="/search/cs?searchtype=author&query=Aktas%2C+T">Tugcan Aktas</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">A. Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1310.3381v2-abstract-short" style="display: inline;"> We propose a low complexity graph-based linear minimum mean square error (LMMSE) equalizer which considers both the intersymbol interference (ISI) and the effect of non-white noise inherent in Faster-than-Nyquist (FTN) signaling. In order to incorporate the statistics of noise signal into the factor graph over which the LMMSE algorithm is implemented, we suggest a method that models it as an autor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1310.3381v2-abstract-full').style.display = 'inline'; document.getElementById('1310.3381v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1310.3381v2-abstract-full" style="display: none;"> We propose a low complexity graph-based linear minimum mean square error (LMMSE) equalizer which considers both the intersymbol interference (ISI) and the effect of non-white noise inherent in Faster-than-Nyquist (FTN) signaling. In order to incorporate the statistics of noise signal into the factor graph over which the LMMSE algorithm is implemented, we suggest a method that models it as an autoregressive (AR) process. Furthermore, we develop a new mechanism for exchange of information between the proposed equalizer and the channel decoder through turbo iterations. Based on these improvements, we show that the proposed low complexity receiver structure performs close to the optimal decoder operating in ISI-free ideal scenario without FTN signaling through simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1310.3381v2-abstract-full').style.display = 'none'; document.getElementById('1310.3381v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 October, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 6 figures, IEEE Wireless Communications and Networking Conference 2014, Istanbul, Turkey</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1308.0818">arXiv:1308.0818</a> <span> [<a href="https://arxiv.org/pdf/1308.0818">pdf</a>, <a href="https://arxiv.org/format/1308.0818">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Effects of Individual Success on Globally Distributed Team Performance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+O">Onur Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1308.0818v1-abstract-short" style="display: inline;"> Necessity of different competencies with high level of knowledge makes it inevitable that software development is a team work. With the today's technology, teams can communicate both synchronously and asynchronously using different online collaboration tools throughout the world. Researches indicate that there are many factors that affect the team success and in this paper, effect of individual su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1308.0818v1-abstract-full').style.display = 'inline'; document.getElementById('1308.0818v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1308.0818v1-abstract-full" style="display: none;"> Necessity of different competencies with high level of knowledge makes it inevitable that software development is a team work. With the today's technology, teams can communicate both synchronously and asynchronously using different online collaboration tools throughout the world. Researches indicate that there are many factors that affect the team success and in this paper, effect of individual success on globally distributed team performance will be analyzed. Student team projects undertaken by other researchers will be used to analyze collected data and conclusions will be drawn for further analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1308.0818v1-abstract-full').style.display = 'none'; document.getElementById('1308.0818v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 August, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 8 figures, research paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1306.4549">arXiv:1306.4549</a> <span> [<a href="https://arxiv.org/pdf/1306.4549">pdf</a>, <a href="https://arxiv.org/ps/1306.4549">ps</a>, <a href="https://arxiv.org/format/1306.4549">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Sigma-Delta quantization of sub-Gaussian frame expansions and its application to compressed sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Krahmer%2C+F">Felix Krahmer</a>, <a href="/search/cs?searchtype=author&query=Saab%2C+R">Rayan Saab</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1306.4549v1-abstract-short" style="display: inline;"> Suppose that the collection $\{e_i\}_{i=1}^m$ forms a frame for $\R^k$, where each entry of the vector $e_i$ is a sub-Gaussian random variable. We consider expansions in such a frame, which are then quantized using a Sigma-Delta scheme. We show that an arbitrary signal in $\R^k$ can be recovered from its quantized frame coefficients up to an error which decays root-exponentially in the oversamplin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.4549v1-abstract-full').style.display = 'inline'; document.getElementById('1306.4549v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1306.4549v1-abstract-full" style="display: none;"> Suppose that the collection $\{e_i\}_{i=1}^m$ forms a frame for $\R^k$, where each entry of the vector $e_i$ is a sub-Gaussian random variable. We consider expansions in such a frame, which are then quantized using a Sigma-Delta scheme. We show that an arbitrary signal in $\R^k$ can be recovered from its quantized frame coefficients up to an error which decays root-exponentially in the oversampling rate $m/k$. Here the quantization scheme is assumed to be chosen appropriately depending on the oversampling rate and the quantization alphabet can be coarse. The result holds with high probability on the draw of the frame uniformly for all signals. The crux of the argument is a bound on the extreme singular values of the product of a deterministic matrix and a sub-Gaussian frame. For fine quantization alphabets, we leverage this bound to show polynomial error decay in the context of compressed sensing. Our results extend previous results for structured deterministic frame expansions and Gaussian compressed sensing measurements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1306.4549v1-abstract-full').style.display = 'none'; document.getElementById('1306.4549v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 94A12; 94A20; 41A25; 15B52 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1305.3803">arXiv:1305.3803</a> <span> [<a href="https://arxiv.org/pdf/1305.3803">pdf</a>, <a href="https://arxiv.org/ps/1305.3803">ps</a>, <a href="https://arxiv.org/format/1305.3803">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> A fast randomized Kaczmarz algorithm for sparse solutions of consistent linear systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansour%2C+H">Hassan Mansour</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1305.3803v1-abstract-short" style="display: inline;"> The Kaczmarz algorithm is a popular solver for overdetermined linear systems due to its simplicity and speed. In this paper, we propose a modification that speeds up the convergence of the randomized Kaczmarz algorithm for systems of linear equations with sparse solutions. The speedup is achieved by projecting every iterate onto a weighted row of the linear system while maintaining the random row… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1305.3803v1-abstract-full').style.display = 'inline'; document.getElementById('1305.3803v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1305.3803v1-abstract-full" style="display: none;"> The Kaczmarz algorithm is a popular solver for overdetermined linear systems due to its simplicity and speed. In this paper, we propose a modification that speeds up the convergence of the randomized Kaczmarz algorithm for systems of linear equations with sparse solutions. The speedup is achieved by projecting every iterate onto a weighted row of the linear system while maintaining the random row selection criteria of Strohmer and Vershynin. The weights are chosen to attenuate the contribution of row elements that lie outside of the estimated support of the sparse solution. While the Kaczmarz algorithm and its variants can only find solutions to overdetermined linear systems, our algorithm surprisingly succeeds in finding sparse solutions to underdetermined linear systems as well. We present empirical studies which demonstrate the acceleration in convergence to the sparse solution using this modified approach in the overdetermined case. We also demonstrate the sparse recovery capabilities of our approach in the underdetermined case and compare the performance with that of $\ell_1$ minimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1305.3803v1-abstract-full').style.display = 'none'; document.getElementById('1305.3803v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2013. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1302.1596">arXiv:1302.1596</a> <span> [<a href="https://arxiv.org/pdf/1302.1596">pdf</a>, <a href="https://arxiv.org/format/1302.1596">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Tag-based Semantic Website Recommendation for Turkish Language </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+O">Onur Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1302.1596v3-abstract-short" style="display: inline;"> With the dramatic increase in the number of websites on the internet, tagging has become popular for finding related, personal and important documents. When the potentially increasing internet markets are analyzed, Turkey, in which most of the people use Turkish language on the internet, found to be exponentially increasing. In this paper, a tag-based website recommendation method is presented, wh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1302.1596v3-abstract-full').style.display = 'inline'; document.getElementById('1302.1596v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1302.1596v3-abstract-full" style="display: none;"> With the dramatic increase in the number of websites on the internet, tagging has become popular for finding related, personal and important documents. When the potentially increasing internet markets are analyzed, Turkey, in which most of the people use Turkish language on the internet, found to be exponentially increasing. In this paper, a tag-based website recommendation method is presented, where similarity measures are combined with semantic relationships of tags. In order to evaluate the system, an experiment with 25 people from Turkey is undertaken and participants are firstly asked to provide websites and tags in Turkish and then they are asked to evaluate recommended websites. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1302.1596v3-abstract-full').style.display = 'none'; document.getElementById('1302.1596v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 August, 2013; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 February, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, research and experiment about recommendation system for Turkish</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1301.6471">arXiv:1301.6471</a> <span> [<a href="https://arxiv.org/pdf/1301.6471">pdf</a>, <a href="https://arxiv.org/ps/1301.6471">ps</a>, <a href="https://arxiv.org/format/1301.6471">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ISIT.2013.6620185">10.1109/ISIT.2013.6620185 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Generalizing the Sampling Property of the Q-function for Error Rate Analysis of Cooperative Communication in Fading Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aktas%2C+T">Tugcan Aktas</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">Ali Ozgur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Aktas%2C+E">Emre Aktas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1301.6471v1-abstract-short" style="display: inline;"> This paper extends some approximation methods that are used to identify closed form Bit Error Rate (BER) expressions which are frequently utilized in investigation and comparison of performance for wireless communication systems in the literature. By using this group of approximation methods, some expectation integrals, which are complicated to analyze and have high computational complexity to eva… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1301.6471v1-abstract-full').style.display = 'inline'; document.getElementById('1301.6471v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1301.6471v1-abstract-full" style="display: none;"> This paper extends some approximation methods that are used to identify closed form Bit Error Rate (BER) expressions which are frequently utilized in investigation and comparison of performance for wireless communication systems in the literature. By using this group of approximation methods, some expectation integrals, which are complicated to analyze and have high computational complexity to evaluate through Monte Carlo simulations, are computed. For these integrals, by using the sampling property of the integrand functions of one or more arguments, reliable BER expressions revealing the diversity and coding gains are derived. Although the methods we present are valid for a larger class of integration problems, in this work we show the step by step derivation of the BER expressions for a canonical cooperative communication scenario in addition to a network coded system starting from basic building blocks. The derived expressions agree with the simulation results for a very wide range of signal-to-noise ratio (SNR) values. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1301.6471v1-abstract-full').style.display = 'none'; document.getElementById('1301.6471v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 January, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2013. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 5 figures, Submitted to IEEE International Symposium on Information Theory, ISIT 2013, Istanbul, Turkey</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1209.2079">arXiv:1209.2079</a> <span> [<a href="https://arxiv.org/pdf/1209.2079">pdf</a>, <a href="https://arxiv.org/ps/1209.2079">ps</a>, <a href="https://arxiv.org/format/1209.2079">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2013.051613.121309">10.1109/TWC.2013.051613.121309 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Error Rate Analysis of GF(q) Network Coded Detect-and-Forward Wireless Relay Networks Using Equivalent Relay Channel Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C5%9Eafak%2C+I">Ilg谋n 艦afak</a>, <a href="/search/cs?searchtype=author&query=Akta%C5%9F%2C+E">Emre Akta艧</a>, <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+A+%C3%96">Ali 脰zg眉r Y谋lmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1209.2079v2-abstract-short" style="display: inline;"> This paper investigates simple means of analyzing the error rate performance of a general q-ary Galois Field network coded detect-and-forward cooperative relay network with known relay error statistics at the destination. Equivalent relay channels are used in obtaining an approximate error rate of the relay network, from which the diversity order is found. Error rate analyses using equivalent rela… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1209.2079v2-abstract-full').style.display = 'inline'; document.getElementById('1209.2079v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1209.2079v2-abstract-full" style="display: none;"> This paper investigates simple means of analyzing the error rate performance of a general q-ary Galois Field network coded detect-and-forward cooperative relay network with known relay error statistics at the destination. Equivalent relay channels are used in obtaining an approximate error rate of the relay network, from which the diversity order is found. Error rate analyses using equivalent relay channel models are shown to be closely matched with simulation results. Using the equivalent relay channels, low complexity receivers are developed whose performances are close to that of the optimal maximum likelihood receiver. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1209.2079v2-abstract-full').style.display = 'none'; document.getElementById('1209.2079v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2013; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 September, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">28 pages, 10 figures. This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1205.6846">arXiv:1205.6846</a> <span> [<a href="https://arxiv.org/pdf/1205.6846">pdf</a>, <a href="https://arxiv.org/format/1205.6846">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Support driven reweighted $\ell_1$ minimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansour%2C+H">Hassan Mansour</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1205.6846v1-abstract-short" style="display: inline;"> In this paper, we propose a support driven reweighted $\ell_1$ minimization algorithm (SDRL1) that solves a sequence of weighted $\ell_1$ problems and relies on the support estimate accuracy. Our SDRL1 algorithm is related to the IRL1 algorithm proposed by Cand{猫}s, Wakin, and Boyd. We demonstrate that it is sufficient to find support estimates with \emph{good} accuracy and apply constant weights… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1205.6846v1-abstract-full').style.display = 'inline'; document.getElementById('1205.6846v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1205.6846v1-abstract-full" style="display: none;"> In this paper, we propose a support driven reweighted $\ell_1$ minimization algorithm (SDRL1) that solves a sequence of weighted $\ell_1$ problems and relies on the support estimate accuracy. Our SDRL1 algorithm is related to the IRL1 algorithm proposed by Cand{猫}s, Wakin, and Boyd. We demonstrate that it is sufficient to find support estimates with \emph{good} accuracy and apply constant weights instead of using the inverse coefficient magnitudes to achieve gains similar to those of IRL1. We then prove that given a support estimate with sufficient accuracy, if the signal decays according to a specific rate, the solution to the weighted $\ell_1$ minimization problem results in a support estimate with higher accuracy than the initial estimate. We also show that under certain conditions, it is possible to achieve higher estimate accuracy when the intersection of support estimates is considered. We demonstrate the performance of SDRL1 through numerical simulations and compare it with that of IRL1 and standard $\ell_1$ minimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1205.6846v1-abstract-full').style.display = 'none'; document.getElementById('1205.6846v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March, 2012</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1205.6845">arXiv:1205.6845</a> <span> [<a href="https://arxiv.org/pdf/1205.6845">pdf</a>, <a href="https://arxiv.org/format/1205.6845">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Weighted-{$\ell_1$} minimization with multiple weighting sets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mansour%2C+H">Hassan Mansour</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+O">Ozgur Yilmaz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1205.6845v1-abstract-short" style="display: inline;"> In this paper, we study the support recovery conditions of weighted $\ell_1$ minimization for signal reconstruction from compressed sensing measurements when multiple support estimate sets with different accuracy are available. We identify a class of signals for which the recovered vector from $\ell_1$ minimization provides an accurate support estimate. We then derive stability and robustness guar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1205.6845v1-abstract-full').style.display = 'inline'; document.getElementById('1205.6845v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1205.6845v1-abstract-full" style="display: none;"> In this paper, we study the support recovery conditions of weighted $\ell_1$ minimization for signal reconstruction from compressed sensing measurements when multiple support estimate sets with different accuracy are available. We identify a class of signals for which the recovered vector from $\ell_1$ minimization provides an accurate support estimate. We then derive stability and robustness guarantees for the weighted $\ell_1$ minimization problem with more than one support estimate. We show that applying a smaller weight to support estimate that enjoy higher accuracy improves the recovery conditions compared with the case of a single support estimate and the case with standard, i.e., non-weighted, $\ell_1$ minimization. Our theoretical results are supported by numerical simulations on synthetic signals and real audio signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1205.6845v1-abstract-full').style.display = 'none'; document.getElementById('1205.6845v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the SPIE, Wavelets and Sparsity XIV, San Diego, August 2011</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1112.3208">arXiv:1112.3208</a> <span> [<a href="https://arxiv.org/pdf/1112.3208">pdf</a>, <a href="https://arxiv.org/ps/1112.3208">ps</a>, <a href="https://arxiv.org/format/1112.3208">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Practical Methods for Wireless Network Coding with Multiple Unicast Transmissions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aktas%2C+T">Tugcan Aktas</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">A. Ozgur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Aktas%2C+E">Emre Aktas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1112.3208v3-abstract-short" style="display: inline;"> We propose a simple yet effective wireless network coding and decoding technique for a multiple unicast network. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. In order to exemplify the technique, we make use of greedy codes over the binary field and show that the arbitrary divers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.3208v3-abstract-full').style.display = 'inline'; document.getElementById('1112.3208v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1112.3208v3-abstract-full" style="display: none;"> We propose a simple yet effective wireless network coding and decoding technique for a multiple unicast network. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. In order to exemplify the technique, we make use of greedy codes over the binary field and show that the arbitrary diversity orders can be flexibly assigned to nodes. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a low-complexity network decoder using the sum-product (SP) algorithm. The proposed SP detector exhibits near optimal performance. We also show asymptotic superiority of network coding over a method that utilizes the wireless channel in a repetitive manner without network coding (NC) and give related rate-diversity trade-off curves. Finally, we extend the given encoding method through selective encoding in order to obtain extra coding gains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1112.3208v3-abstract-full').style.display = 'none'; document.getElementById('1112.3208v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 September, 2012; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 9 figures, Submitted to the IEEE Transactions on Communications on 14.12.2011, revised on 18.05.2012 and on 04.09.2012. arXiv admin note: text overlap with arXiv:1110.0594</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1110.0594">arXiv:1110.0594</a> <span> [<a href="https://arxiv.org/pdf/1110.0594">pdf</a>, <a href="https://arxiv.org/ps/1110.0594">ps</a>, <a href="https://arxiv.org/format/1110.0594">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/WCNC.2012.6214460">10.1109/WCNC.2012.6214460 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Practical Wireless Network Coding and Decoding Methods for Multiple Unicast Transmissions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aktas%2C+T">Tugcan Aktas</a>, <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+O">Ali Ozgur Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Aktas%2C+E">Emre Aktas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1110.0594v1-abstract-short" style="display: inline;"> We propose a simple yet effective wireless network coding and decoding technique. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. For this purpose, we make use of greedy codes over the binary field and show that desired diversity orders can be flexibly assigned to nodes in a multip… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1110.0594v1-abstract-full').style.display = 'inline'; document.getElementById('1110.0594v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1110.0594v1-abstract-full" style="display: none;"> We propose a simple yet effective wireless network coding and decoding technique. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. For this purpose, we make use of greedy codes over the binary field and show that desired diversity orders can be flexibly assigned to nodes in a multiple unicast network, contrary to the previous findings in the literature. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a network decoder using the sum-product algorithm. The proposed sum-product detector exhibits near optimal performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1110.0594v1-abstract-full').style.display = 'none'; document.getElementById('1110.0594v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 5 figures, Submitted to WCNC 2012, IEEE Wireless Communication and Networking Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1110.0020">arXiv:1110.0020</a> <span> [<a href="https://arxiv.org/pdf/1110.0020">pdf</a>, <a href="https://arxiv.org/ps/1110.0020">ps</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1613/jair.2065">10.1613/jair.2065 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Causes of Ineradicable Spurious Predictions in Qualitative Simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Y%C4%B1lmaz%2C+%C3%96">脰. Y谋lmaz</a>, <a href="/search/cs?searchtype=author&query=Say%2C+A+C+C">A. C. C. Say</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1110.0020v1-abstract-short" style="display: inline;"> It was recently proved that a sound and complete qualitative simulator does not exist, that is, as long as the input-output vocabulary of the state-of-the-art QSIM algorithm is used, there will always be input models which cause any simulator with a coverage guarantee to make spurious predictions in its output. In this paper, we examine whether a meaningfully expressive restriction of this vocabul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1110.0020v1-abstract-full').style.display = 'inline'; document.getElementById('1110.0020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1110.0020v1-abstract-full" style="display: none;"> It was recently proved that a sound and complete qualitative simulator does not exist, that is, as long as the input-output vocabulary of the state-of-the-art QSIM algorithm is used, there will always be input models which cause any simulator with a coverage guarantee to make spurious predictions in its output. In this paper, we examine whether a meaningfully expressive restriction of this vocabulary is possible so that one can build a simulator with both the soundness and completeness properties. We prove several negative results: All sound qualitative simulators, employing subsets of the QSIM representation which retain the operating region transition feature, and support at least the addition and constancy constraints, are shown to be inherently incomplete. Even when the simulations are restricted to run in a single operating region, a constraint vocabulary containing just the addition, constancy, derivative, and multiplication relations makes the construction of sound and complete qualitative simulators impossible. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1110.0020v1-abstract-full').style.display = 'none'; document.getElementById('1110.0020v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Journal Of Artificial Intelligence Research, Volume 27, pages 551-575, 2006 </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" 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