Information Theory

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aria-labelledby="recent-cs.IT" href="/list/cs.IT/recent">recent</a> articles</p> <h3>Showing new listings for Wednesday, 27 November 2024</h3> <div class='paging'>Total of 15 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/cs.IT/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>New submissions (showing 5 of 5 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.16971" title="Abstract" id="2411.16971"> arXiv:2411.16971 </a> [<a href="/pdf/2411.16971" title="Download PDF" id="pdf-2411.16971" aria-labelledby="pdf-2411.16971">pdf</a>, <a href="https://arxiv.org/html/2411.16971v1" title="View HTML" id="html-2411.16971" aria-labelledby="html-2411.16971" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.16971" title="Other formats" id="oth-2411.16971" aria-labelledby="oth-2411.16971">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generative vs. Predictive Models in Massive MIMO Channel Prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+J">Ju-Hyung Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+J">Joohan Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Molisch,+A+F">Andreas F. Molisch</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Networking and Internet Architecture (cs.NI) </div> <p class='mathjax'> Massive MIMO (mMIMO) systems are essential for 5G/6G networks to meet high throughput and reliability demands, with machine learning (ML)-based techniques, particularly autoencoders (AEs), showing promise for practical deployment. However, standard AEs struggle under noisy channel conditions, limiting their effectiveness. This work introduces a Vector Quantization-based generative AE model (VQ-VAE) for robust mMIMO cross-antenna channel prediction. We compare Generative and Predictive AE-based models, demonstrating that Generative models outperform Predictive ones, especially in noisy environments. The proposed VQ-VAE achieves up to 15 [dB] NMSE gains over standard AEs and about 9 [dB] over VAEs. Additionally, we present a complexity analysis of AE-based models alongside a diffusion model, highlighting the trade-off between accuracy and computational efficiency. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.16990" title="Abstract" id="2411.16990"> arXiv:2411.16990 </a> [<a href="/pdf/2411.16990" title="Download PDF" id="pdf-2411.16990" aria-labelledby="pdf-2411.16990">pdf</a>, <a href="/format/2411.16990" title="Other formats" id="oth-2411.16990" aria-labelledby="oth-2411.16990">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Enabling Skip Graphs to Process K-Dimensional Range Queries in a Mobile Sensor Network </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Brault,+G+J">Gregory J. Brault</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Augeri,+C+J">Christopher James Augeri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mullins,+B+E">Barry E. Mullins</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Baldwin,+R+O">Rusty O. Baldwin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mayer,+C+B">Christopher B. Mayer</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> archival pre-print of this k-D distributed prefix-free distributed encoding technique ahead of another arXiv release which will cite this work, specifically with respect to using a more extensive prefix-free encoding technique to localize token partitions in an arbitrary input/output context of an LLM, SSM, or other k-D tokenized model </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Proceedings of the 6th IEEE International Symposium on Network Computing and Applications NCA, Cambridge, MA, IEEE, 12-14 July 2007 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS); Networking and Internet Architecture (cs.NI) </div> <p class='mathjax'> A skip graph is a resilient application-layer routing structure that supports range queries of distributed k-dimensional data. By sorting deterministic keys into groups based on locally computed random membership vectors, nodes in a standard skip graph can optimize range query performance in mobile networks such as unmanned aerial vehicle swarms. We propose a skip graph extension that inverts the key and membership vector roles and bases group membership on deterministic vectors derived from the z-ordering of k-dimensional data and sorting within groups is based on locally computed random keys. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2411.17056" title="Abstract" id="2411.17056"> arXiv:2411.17056 </a> [<a href="/pdf/2411.17056" title="Download PDF" id="pdf-2411.17056" aria-labelledby="pdf-2411.17056">pdf</a>, <a href="https://arxiv.org/html/2411.17056v1" title="View HTML" id="html-2411.17056" aria-labelledby="html-2411.17056" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17056" title="Other formats" id="oth-2411.17056" aria-labelledby="oth-2411.17056">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robust Max-Min Fair Beamforming Design for Rate Splitting Multiple Access-aided Visible Light Communications </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Qiu,+Z">Zhengqing Qiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mao,+Y">Yijie Mao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+S">Shuai Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Clerckx,+B">Bruno Clerckx</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> This paper addresses the robust beamforming design for rate splitting multiple access (RSMA)-aided visible light communication (VLC) networks with imperfect channel state information at the transmitter (CSIT). In particular, we first derive the theoretical lower bound for the channel capacity of RSMA-aided VLC <a href="http://networks.Then" rel="external noopener nofollow" class="link-external link-http">this http URL</a> we investigate the beamforming design to solve the max-min fairness (MMF) problem of RSMA-aided VLC networks under the practical optical power constraint and electrical power constraint while considering the practical imperfect CSIT <a href="http://scenario.To" rel="external noopener nofollow" class="link-external link-http">this http URL</a> address the problem, we propose a constrained-concave-convex programming (CCCP)-based beamforming design algorithm which exploits semidefinite relaxation (SDR) technique and a penalty method to deal with the rank-one constraint caused by <a href="http://SDR.Numerical" rel="external noopener nofollow" class="link-external link-http">this http URL</a> results show that the proposed robust beamforming design algorithm for RSMA-aided VLC network achieves a superior performance over the existing ones for space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA). </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2411.17241" title="Abstract" id="2411.17241"> arXiv:2411.17241 </a> [<a href="/pdf/2411.17241" title="Download PDF" id="pdf-2411.17241" aria-labelledby="pdf-2411.17241">pdf</a>, <a href="https://arxiv.org/html/2411.17241v1" title="View HTML" id="html-2411.17241" aria-labelledby="html-2411.17241" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17241" title="Other formats" id="oth-2411.17241" aria-labelledby="oth-2411.17241">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Divergence Inequalities with Applications in Ergodic Theory </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=George,+I">Ian George</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+A">Alice Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bansal,+A">Akshay Bansal</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Preliminary Version; Section IV presented at ITW 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Quantum Physics (quant-ph) </div> <p class='mathjax'> The data processing inequality is central to information theory and motivates the study of monotonic divergences. However, it is not clear operationally we need to consider all such divergences. We establish a simple method for Pinsker inequalities as well as general bounds in terms of $\chi^{2}$-divergences for twice-differentiable $f$-divergences. These tools imply new relations for input-dependent contraction coefficients. We use these relations to show for many $f$-divergences the rate of contraction of a time homogeneous Markov chain is characterized by the input-dependent contraction coefficient of the $\chi^{2}$-divergence. This is efficient to compute and the fastest it could converge for a class of divergences. We show similar ideas hold for mixing times. Moreover, we extend these results to the Petz $f$-divergences in quantum information theory, albeit without any guarantee of efficient computation. These tools may have applications in other settings where iterative data processing is relevant. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2411.17559" title="Abstract" id="2411.17559"> arXiv:2411.17559 </a> [<a href="/pdf/2411.17559" title="Download PDF" id="pdf-2411.17559" aria-labelledby="pdf-2411.17559">pdf</a>, <a href="/format/2411.17559" title="Other formats" id="oth-2411.17559" aria-labelledby="oth-2411.17559">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Degrees of Freedom of Cache-Aided Interference Channels Assisted by Active Intelligent Reflecting Surfaces </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Changizi,+A">Abolfazl Changizi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bafghi,+A+H+A">Ali H. Abdollahi Bafghi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nasiri-Kenari,+M">Masoumeh Nasiri-Kenari</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span> </div> <p class='mathjax'> This paper studies cache-aided wireless networks in the presence of active intelligent reflecting surfaces (IRS) from an information-theoretic perspective. Specifically, we explore interference management in a cache-aided wireless network assisted by an active IRS, to enhance the achievable degrees of freedom (DoF). To this end, we jointly design the content placement, delivery phase, and phase shifts of the IRS and propose a one-shot achievable scheme. Our scheme exploits transmitters' cooperation, cache contents (as side information), interference alignment, and IRS capabilities, adapting to the network's parameters. We derive the achievable one-shot sum-DoF for different sizes of cache memories, network configurations, and numbers of IRS elements. Our results highlight the potential of deploying an IRS in cache-aided wireless communication systems, underscoring the enhancement of achievable DoF for various parameter regimes, particularly when the sizes of the caches (especially at the transmitters) are inadequate. Notably, we show that access to an IRS with a sufficient number of elements enables the achievement of the maximum possible DoF for various parameter regimes of interest. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 4 of 4 entries)</h3> <dt> <a name='item6'>[6]</a> <a href ="/abs/2411.16913" title="Abstract" id="2411.16913"> arXiv:2411.16913 </a> (cross-list from math.PR) [<a href="/pdf/2411.16913" title="Download PDF" id="pdf-2411.16913" aria-labelledby="pdf-2411.16913">pdf</a>, <a href="https://arxiv.org/html/2411.16913v1" title="View HTML" id="html-2411.16913" aria-labelledby="html-2411.16913" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.16913" title="Other formats" id="oth-2411.16913" aria-labelledby="oth-2411.16913">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Entropies of the Poisson distribution as functions of intensity: "normal" and "anomalous" behavior </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Finkelshtein,+D">Dmitri Finkelshtein</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Malyarenko,+A">Anatoliy Malyarenko</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Mishura,+Y">Yuliya Mishura</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Ralchenko,+K">Kostiantyn Ralchenko</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Probability (math.PR)</span>; Information Theory (cs.IT) </div> <p class='mathjax'> The paper extends the analysis of the entropies of the Poisson distribution with parameter $\lambda$. It demonstrates that the Tsallis and Sharma-Mittal entropies exhibit monotonic behavior with respect to $\lambda$, whereas two generalized forms of the R茅nyi entropy may exhibit "anomalous" (non-monotonic) behavior. Additionally, we examine the asymptotic behavior of the entropies as $\lambda \to \infty$ and provide both lower and upper bounds for them. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2411.17050" title="Abstract" id="2411.17050"> arXiv:2411.17050 </a> (cross-list from quant-ph) [<a href="/pdf/2411.17050" title="Download PDF" id="pdf-2411.17050" aria-labelledby="pdf-2411.17050">pdf</a>, <a href="/format/2411.17050" title="Other formats" id="oth-2411.17050" aria-labelledby="oth-2411.17050">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Targeted Clifford logical gates for hypergraph product codes </div> <div class='list-authors'><a href="https://arxiv.org/search/quant-ph?searchtype=author&query=Patra,+A">Adway Patra</a>, <a href="https://arxiv.org/search/quant-ph?searchtype=author&query=Barg,+A">Alexander Barg</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Quantum Physics (quant-ph)</span>; Information Theory (cs.IT) </div> <p class='mathjax'> We construct explicit targeted logical gates for hypergraph product codes. Starting with symplectic matrices for CNOT, CZ, Phase, and Hadamard operators, which together generate the Clifford group, we design explicit transformations that result in targeted logical gates for arbitrary HGP codes. As a concrete example, we give logical circuits for the $[[18,2,3]]$ toric code. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2411.17434" title="Abstract" id="2411.17434"> arXiv:2411.17434 </a> (cross-list from math.RT) [<a href="/pdf/2411.17434" title="Download PDF" id="pdf-2411.17434" aria-labelledby="pdf-2411.17434">pdf</a>, <a href="https://arxiv.org/html/2411.17434v1" title="View HTML" id="html-2411.17434" aria-labelledby="html-2411.17434" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17434" title="Other formats" id="oth-2411.17434" aria-labelledby="oth-2411.17434">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Recovering a group from few orbits </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Mixon,+D+G">Dustin G. Mixon</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Vose,+B">Brantley Vose</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 3 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Representation Theory (math.RT)</span>; Information Theory (cs.IT) </div> <p class='mathjax'> For an unknown finite group $G$ of automorphisms of a finite-dimensional Hilbert space, we find sharp bounds on the number of generic $G$-orbits needed to recover $G$ up to group isomorphism, as well as the number needed to recover $G$ as a concrete set of automorphisms. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2411.17692" title="Abstract" id="2411.17692"> arXiv:2411.17692 </a> (cross-list from q-bio.NC) [<a href="/pdf/2411.17692" title="Download PDF" id="pdf-2411.17692" aria-labelledby="pdf-2411.17692">pdf</a>, <a href="https://arxiv.org/html/2411.17692v1" title="View HTML" id="html-2411.17692" aria-labelledby="html-2411.17692" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17692" title="Other formats" id="oth-2411.17692" aria-labelledby="oth-2411.17692">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantifying information stored in synaptic connections rather than in firing patterns of neural networks </div> <div class='list-authors'><a href="https://arxiv.org/search/q-bio?searchtype=author&query=Fan,+X">Xinhao Fan</a>, <a href="https://arxiv.org/search/q-bio?searchtype=author&query=Mysore,+S+P">Shreesh P Mysore</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Neurons and Cognition (q-bio.NC)</span>; Information Theory (cs.IT); Biological Physics (physics.bio-ph) </div> <p class='mathjax'> A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is known about quantifying information encoded by its synaptic connections. Here, we develop a theoretical framework using continuous Hopfield networks as an exemplar for associative neural networks, and data that follow mixtures of broadly applicable multivariate log-normal distributions. Specifically, we analytically derive the Shannon mutual information between the data and singletons, pairs, triplets, quadruplets, and arbitrary n-tuples of synaptic connections within the network. Our framework corroborates well-established insights about storage capacity of, and distributed coding by, neural firing patterns. Strikingly, it discovers synergistic interactions among synapses, revealing that the information encoded jointly by all the synapses exceeds the 'sum of its parts'. Taken together, this study introduces an interpretable framework for quantitatively understanding information storage in neural networks, one that illustrates the duality of synaptic connectivity and neural population activity in learning and memory. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 6 of 6 entries)</h3> <dt> <a name='item10'>[10]</a> <a href ="/abs/2009.00951" title="Abstract" id="2009.00951"> arXiv:2009.00951 </a> (replaced) [<a href="/pdf/2009.00951" title="Download PDF" id="pdf-2009.00951" aria-labelledby="pdf-2009.00951">pdf</a>, <a href="https://arxiv.org/html/2009.00951v5" title="View HTML" id="html-2009.00951" aria-labelledby="html-2009.00951" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2009.00951" title="Other formats" id="oth-2009.00951" aria-labelledby="oth-2009.00951">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Embedded Blockchains: A Synthesis of Blockchains, Spread Spectrum Watermarking, Perceptual Hashing & Digital Signatures </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Blake,+S">Sam Blake</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Going in a different direction with this research </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Cryptography and Security (cs.CR); Multimedia (cs.MM) </div> <p class='mathjax'> In this paper we introduce a scheme for detecting manipulated audio and video. The scheme is a synthesis of blockchains, encrypted spread spectrum watermarks, perceptual hashing and digital signatures, which we call an Embedded Blockchain. Within this scheme, we use the blockchain for its data structure of a cryptographically linked list, cryptographic hashing for absolute comparisons, perceptual hashing for flexible comparisons, digital signatures for proof of ownership, and encrypted spread spectrum watermarking to embed the blockchain into the background noise of the media. So each media recording has its own unique blockchain, with each block holding information describing the media segment. The problem of verifying the integrity of the media is recast to traversing the blockchain, block-by-block, and segment-by-segment of the media. If any chain is broken, the difference in the computed and extracted perceptual hash is used to estimate the level of manipulation. </p> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2401.02037" title="Abstract" id="2401.02037"> arXiv:2401.02037 </a> (replaced) [<a href="/pdf/2401.02037" title="Download PDF" id="pdf-2401.02037" aria-labelledby="pdf-2401.02037">pdf</a>, <a href="https://arxiv.org/html/2401.02037v3" title="View HTML" id="html-2401.02037" aria-labelledby="html-2401.02037" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2401.02037" title="Other formats" id="oth-2401.02037" aria-labelledby="oth-2401.02037">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Simplified Information Geometry Approach for Massive MIMO-OFDM Channel Estimation -- Part II: Convergence Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+J">Jiyuan Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+M">Mingrui Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+X">Xiqi Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+X">Xiang-Gen Xia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Slock,+D">Dirk Slock</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> I'm merging the two parts of this paper (arXiv:<a href="https://arxiv.org/abs/2401.02035" data-arxiv-id="2401.02035" class="link-https">arXiv:2401.02035</a> and <a href="https://arxiv.org/abs/2401.02037" data-arxiv-id="2401.02037" class="link-https">arXiv:2401.02037</a>). The combined paper will appear as v2 of <a href="https://arxiv.org/abs/2401.02035" data-arxiv-id="2401.02035" class="link-https">arXiv:2401.02035</a>. So I need to withdraw this paper </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span> </div> <p class='mathjax'> In Part II of this two-part paper, we prove the convergence of the simplified information geometry approach (SIGA) proposed in Part I. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results. </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2410.05062" title="Abstract" id="2410.05062"> arXiv:2410.05062 </a> (replaced) [<a href="/pdf/2410.05062" title="Download PDF" id="pdf-2410.05062" aria-labelledby="pdf-2410.05062">pdf</a>, <a href="https://arxiv.org/html/2410.05062v2" title="View HTML" id="html-2410.05062" aria-labelledby="html-2410.05062" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.05062" title="Other formats" id="oth-2410.05062" aria-labelledby="oth-2410.05062">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Haoyun Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiao,+M">Ming Xiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+K">Kezhi Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+D+I">Dong In Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Debbah,+M">Merouane Debbah</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Information Theory (cs.IT)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> This letter investigates an unmanned aerial vehicle (UAV) network with integrated sensing and communication (ISAC) systems, where multiple UAVs simultaneously sense the locations of ground users and provide communication services with radars. To find the trade-off between communication and sensing (C\&S) in the system, we formulate a multi-objective optimization problem (MOP) to maximize the total network utility and the localization Cram茅r-Rao bounds (CRB) of ground users, which jointly optimizes the deployment and power control of UAVs. Inspired by the huge potential of large language models (LLM) for prediction and inference, we propose an LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP. We first adopt a decomposition-based scheme to decompose the MOP into a series of optimization sub-problems. We second integrate LLMs as black-box search operators with MOP-specifically designed prompt engineering into the framework of MOEA to solve optimization sub-problems simultaneously. Numerical results demonstrate that the proposed LEDMA can find the clear trade-off between C\&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2308.04355" title="Abstract" id="2308.04355"> arXiv:2308.04355 </a> (replaced) [<a href="/pdf/2308.04355" title="Download PDF" id="pdf-2308.04355" aria-labelledby="pdf-2308.04355">pdf</a>, <a href="https://arxiv.org/html/2308.04355v3" title="View HTML" id="html-2308.04355" aria-labelledby="html-2308.04355" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2308.04355" title="Other formats" id="oth-2308.04355" aria-labelledby="oth-2308.04355">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-induced Changes in ECG </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Ali,+S+A">S. Anas Ali</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Niaz,+M+S">M. Saqib Niaz</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rehman,+M">Mubashir Rehman</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mehmood,+A">Ahsan Mehmood</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rahman,+M+M+U">M. Mahboob Ur Rahman</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Riaz,+K">Kashif Riaz</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Abbasi,+Q+H">Qammer H. Abbasi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 7 figures, 5 tables, submitted to a journal for review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Information Theory (cs.IT) </div> <p class='mathjax'> Vascular age is traditionally measured using invasive methods or through 12-lead electrocardiogram (ECG). This paper utilizes a low-cost single-lead (lead-I) ECG module to predict the vascular age of an apparently healthy young person. In addition, we also study the impact of smoking on ECG traces of the light-but-habitual smokers. We begin by collecting (lead- I) ECG data from 42 apparently healthy subjects (smokers and non-smokers) aged 18 to 30 years, using our custom-built low- cost single-lead ECG module, and anthropometric data, e.g., body mass index, smoking status, blood pressure, etc. Under our proposed method, we first pre-process our dataset by denoising the ECG traces, followed by baseline drift removal, followed by z-score normalization. Next, we create another dataset by dividing the ECG traces into overlapping segments of five-second duration. We then feed both segmented and unsegmented datasets to a number of machine learning models, a 1D convolutional neural network, and ResNet18 model, for vascular ageing pre- diction. We also do transfer learning whereby we pre-train our models on a public PPG dataset, and later, fine-tune and evaluate them on our unsegmented ECG dataset. The random forest model outperforms all other models and previous works by achieving a mean squared error (MSE) of 0.07 and coefficient of determination R2 of 0.99, MSE of 3.56 and R2 of 0.26, MSE of 0.99 and R2 of 0.87, for segmented ECG dataset, for unsegmented ECG dataset, and for transfer learning scenario, respectively. Finally, we utilize the explainable AI framework to identify those ECG features that get affected due to smoking. This work is aligned with the sustainable development goals 3 and 10 of the United Nations which aim to provide low-cost but quality healthcare solutions to the unprivileged. This work also finds its applications in the broad domain of forensic science. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2407.09687" title="Abstract" id="2407.09687"> arXiv:2407.09687 </a> (replaced) [<a href="/pdf/2407.09687" title="Download PDF" id="pdf-2407.09687" aria-labelledby="pdf-2407.09687">pdf</a>, <a href="https://arxiv.org/html/2407.09687v2" title="View HTML" id="html-2407.09687" aria-labelledby="html-2407.09687" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2407.09687" title="Other formats" id="oth-2407.09687" aria-labelledby="oth-2407.09687">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shastri,+S+K">Saurav K. Shastri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schniter,+P">Philip Schniter</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Information Theory (cs.IT) </div> <p class='mathjax'> Accurately recovering images from phaseless measurements is a challenging and long-standing problem. In this work, we present "deepECpr," which combines expectation-consistent (EC) approximation with deep denoising networks to surpass state-of-the-art phase-retrieval methods in both speed and accuracy. In addition to applying EC in a non-traditional manner, deepECpr includes a novel stochastic damping scheme that is inspired by recent diffusion methods. Like existing phase-retrieval methods based on plug-and-play priors, regularization by denoising, or diffusion, deepECpr iterates a denoising stage with a measurement-exploitation stage. But unlike existing methods, deepECpr requires far fewer denoiser calls. We compare deepECpr to the state-of-the-art prDeep (Metzler et al., 2018), Deep-ITA (Wang et al., 2020), DOLPH (Shoushtari et al., 2023), and Diffusion Posterior Sampling (Chung et al., 2023) methods for noisy phase-retrieval of color, natural, and unnatural grayscale images on oversampled-Fourier and coded-diffraction-pattern measurements and find improvements in both PSNR and SSIM with significantly fewer denoiser calls. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2411.15144" title="Abstract" id="2411.15144"> arXiv:2411.15144 </a> (replaced) [<a href="/pdf/2411.15144" title="Download PDF" id="pdf-2411.15144" aria-labelledby="pdf-2411.15144">pdf</a>, <a href="/format/2411.15144" title="Other formats" id="oth-2411.15144" aria-labelledby="oth-2411.15144">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Chatelier,+B">Baptiste Chatelier</a> (INSA Rennes, IETR, MERCE-France), <a href="https://arxiv.org/search/eess?searchtype=author&query=Mateos-Ramos,+J+M">Jos茅 Miguel Mateos-Ramos</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Corlay,+V">Vincent Corlay</a> (MERCE-France), <a href="https://arxiv.org/search/eess?searchtype=author&query=H%C3%A4ger,+C">Christian H盲ger</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Crussi%C3%A8re,+M">Matthieu Crussi猫re</a> (INSA Rennes, IETR), <a href="https://arxiv.org/search/eess?searchtype=author&query=Wymeersch,+H">Henk Wymeersch</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Magoarou,+L+L">Luc Le Magoarou</a> (INSA Rennes, IETR)</div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG) </div> <p class='mathjax'> Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can negatively impact the obtained performance. This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach. Specifically, a differentiable version of the multiple signal classification (MUSIC) algorithm is derived, allowing efficient learning of the considered impairments. The proposed approach supports both supervised and unsupervised learning strategies, showcasing its practical potential. Simulation results indicate that the proposed method successfully learns significant inaccuracies in both antenna locations and complex gains. Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task. </p> </div> </dd> </dl> <div class='paging'>Total of 15 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/cs.IT/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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