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aria-labelledby="recent-eess.SP" href="/list/eess.SP/recent">recent</a> articles</p> <h3>Showing new listings for Thursday, 28 November 2024</h3> <div class='paging'>Total of 47 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/eess.SP/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 28 of 28 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.17702" title="Abstract" id="2411.17702"> arXiv:2411.17702 </a> [<a href="/pdf/2411.17702" title="Download PDF" id="pdf-2411.17702" aria-labelledby="pdf-2411.17702">pdf</a>, <a href="https://arxiv.org/html/2411.17702v1" title="View HTML" id="html-2411.17702" aria-labelledby="html-2411.17702" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17702" title="Other formats" id="oth-2411.17702" aria-labelledby="oth-2411.17702">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac Condition </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Jeong,+H">Hyewon Jeong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yun,+S">Suyeol Yun</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Adam,+H">Hammaad Adam</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> Electrocardiograms (ECGs) are an established technique to screen for abnormal cardiac signals. Recent work has established that it is possible to detect arrhythmia directly from the ECG signal using deep learning algorithms. While a few prior approaches with contrastive learning have been successful, the best way to define a positive sample remains an open question. In this project, we investigate several ways to define positive samples, and assess which approach yields the best performance in a downstream task of classifying arrhythmia. We explore spatiotemporal invariances, generic augmentations, demographic similarities, cardiac rhythms, and wave attributes of ECG as potential ways to match positive samples. We then evaluate each strategy with downstream task performance, and find that learned representations invariant to patient identity are powerful in arrhythmia detection. We made our code available in: <a href="https://github.com/mandiehyewon/goodviews_ecg.git" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.17705" title="Abstract" id="2411.17705"> arXiv:2411.17705 </a> [<a href="/pdf/2411.17705" title="Download PDF" id="pdf-2411.17705" aria-labelledby="pdf-2411.17705">pdf</a>, <a href="https://arxiv.org/html/2411.17705v1" title="View HTML" id="html-2411.17705" aria-labelledby="html-2411.17705" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17705" title="Other formats" id="oth-2411.17705" aria-labelledby="oth-2411.17705">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Peng,+W">Wei Peng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+K">Kang Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Shi,+J">Jiaxi Shi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hu,+J">Jianchen Hu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with functional impairments to regain mobility. We present a novel multi-scale atrous convolutional neural network (CNN) model called EEG-dilated convolution network (DCNet) to enhance the accuracy and efficiency of the EEG-based MI classification tasks. We incorporate the $1\times1$ convolutional layer and utilize the multi-branch parallel atrous convolutional architecture in EEG-DCNet to capture the highly nonlinear characteristics and multi-scale features of the EEG signals. Moreover, we utilize the sliding window to enhance the temporal consistency and utilize the attension mechanism to improve the accuracy of recognizing user intentions. The experimental results (via the BCI-IV-2a ,BCI-IV-2b and the High-Gamma datasets) show that EEG-DCNet outperforms existing state-of-the-art (SOTA) approaches in terms of classification accuracy and Kappa scores. Furthermore, since EEG-DCNet requires less number of parameters, the training efficiency and memory consumption are also improved. The experiment code is open-sourced at \href{<a href="https://github.com/Kanyooo/EEG-DCNet" rel="external noopener nofollow" class="link-external link-https">this https URL</a>}{here}. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2411.17707" title="Abstract" id="2411.17707"> arXiv:2411.17707 </a> [<a href="/pdf/2411.17707" title="Download PDF" id="pdf-2411.17707" aria-labelledby="pdf-2411.17707">pdf</a>, <a href="/format/2411.17707" title="Other formats" id="oth-2411.17707" aria-labelledby="oth-2411.17707">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+S">Siwei Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+J">Jiangwen Chen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lin,+H">Hua Lin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+W">Wei Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) </div> <p class='mathjax'> This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and EfficientNet large model using data-driven deep learning fault diagnosis technology. The aim is to evaluate the effectiveness of automatic deep learning-based large model technology through transfer learning in nuclear power plant scenarios. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2411.17709" title="Abstract" id="2411.17709"> arXiv:2411.17709 </a> [<a href="/pdf/2411.17709" title="Download PDF" id="pdf-2411.17709" aria-labelledby="pdf-2411.17709">pdf</a>, <a href="https://arxiv.org/html/2411.17709v1" title="View HTML" id="html-2411.17709" aria-labelledby="html-2411.17709" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17709" title="Other formats" id="oth-2411.17709" aria-labelledby="oth-2411.17709">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Poziomska,+M">Martyna Poziomska</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Dovgialo,+M">Marian Dovgialo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Olbratowski,+P">Przemys艂aw Olbratowski</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Niedbalski,+P">Pawe艂 Niedbalski</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ogniewski,+P">Pawe艂 Ogniewski</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zych,+J">Joanna Zych</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rogala,+J">Jacek Rogala</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=%C5%BBygierewicz,+J">Jaros艂aw 呕ygierewicz</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 20 pages, 17 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2411.17711" title="Abstract" id="2411.17711"> arXiv:2411.17711 </a> [<a href="/pdf/2411.17711" title="Download PDF" id="pdf-2411.17711" aria-labelledby="pdf-2411.17711">pdf</a>, <a href="https://arxiv.org/html/2411.17711v1" title="View HTML" id="html-2411.17711" aria-labelledby="html-2411.17711" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17711" title="Other formats" id="oth-2411.17711" aria-labelledby="oth-2411.17711">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> AnyECG: Foundational Models for Electrocardiogram Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+Y">Yue Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cao,+X">Xu Cao</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hu,+Y">Yaojun Hu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ying,+H">Haochao Ying</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rehg,+J+M">James Matthew Rehg</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sun,+J">Jimeng Sun</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wu,+J">Jian Wu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+J">Jintai Chen</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite these efforts, performance remains suboptimal. A key obstacle is the inherent complexity of ECG data, which includes heterogeneity (e.g., varying sampling rates), high levels of noise, demographic-related pattern shifts, and intricate rhythm-event associations. To overcome these challenges, this paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data. Specifically, a tailored ECG Tokenizer encodes each fixed-duration ECG fragment into a token and, guided by proxy tasks, converts noisy, continuous ECG features into discrete, compact, and clinically meaningful local rhythm codes. These codes encapsulate basic morphological, frequency, and demographic information (e.g., sex), effectively mitigating signal noise. We further pre-train the AnyECG to learn rhythmic pattern associations across ECG tokens, enabling the capture of cardiac event semantics. By being jointly pre-trained on diverse ECG data sources, AnyECG is capable of generalizing across a wide range of downstream tasks where ECG signals are recorded from various devices and scenarios. Experimental results in anomaly detection, arrhythmia detection, corrupted lead generation, and ultra-long ECG signal analysis demonstrate that AnyECG learns common ECG knowledge from data and significantly outperforms cutting-edge methods in each respective task. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2411.17715" title="Abstract" id="2411.17715"> arXiv:2411.17715 </a> [<a href="/pdf/2411.17715" title="Download PDF" id="pdf-2411.17715" aria-labelledby="pdf-2411.17715">pdf</a>, <a href="/format/2411.17715" title="Other formats" id="oth-2411.17715" aria-labelledby="oth-2411.17715">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Chandanwala,+A+A">Ali Asgar Chandanwala</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bhowmik,+S">Srutakirti Bhowmik</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chaudhury,+P">Parna Chaudhury</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pravin,+S+C">Sheena Christabel Pravin</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2411.17716" title="Abstract" id="2411.17716"> arXiv:2411.17716 </a> [<a href="/pdf/2411.17716" title="Download PDF" id="pdf-2411.17716" aria-labelledby="pdf-2411.17716">pdf</a>, <a href="https://arxiv.org/html/2411.17716v1" title="View HTML" id="html-2411.17716" aria-labelledby="html-2411.17716" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17716" title="Other formats" id="oth-2411.17716" aria-labelledby="oth-2411.17716">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generating CKM Using Others' Data: Cross-AP CKM Inference with Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Dai,+Z">Zhuoyin Dai</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wu,+D">Di Wu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+X">Xiaoli Xu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zeng,+Y">Yong Zeng</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Image and Video Processing (eess.IV); Systems and Control (eess.SY) </div> <p class='mathjax'> Channel knowledge map (CKM) is a promising paradigm shift towards environment-aware communication and sensing by providing location-specific prior channel knowledge before real-time communication. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently generate CKMs in dense networks. For a dense network with CKMs of existing access points (APs), it will be useful to efficiently generate CKMs of potentially new APs with only AP location information. The generation of inferred CKMs across APs can help dense networks achieve convenient initial CKM generation, environment-aware AP deployment, and cost-effective CKM updates. Considering that different APs in the same region share the same physical environment, there exists a natural correlation between the channel knowledge of different APs. Therefore, by mining the implicit correlation between location-specific channel knowledge, cross-AP CKM inference can be realized using data from other APs. This paper proposes a cross-AP inference method to generate CKMs of potentially new APs with deep learning. The location of the target AP is fed into the UNet model in combination with the channel knowledge of other existing APs, and supervised learning is performed based on the channel knowledge of the target AP. Based on the trained UNet and the channel knowledge of the existing APs, the CKM inference of the potentially new AP can be generated across APs. The generation results of the inferred CKM validate the feasibility and effectiveness of cross-AP CKM inference with other APs' channel knowledge. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2411.17717" title="Abstract" id="2411.17717"> arXiv:2411.17717 </a> [<a href="/pdf/2411.17717" title="Download PDF" id="pdf-2411.17717" aria-labelledby="pdf-2411.17717">pdf</a>, <a href="/format/2411.17717" title="Other formats" id="oth-2411.17717" aria-labelledby="oth-2411.17717">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Comprehensive Methodology for Sample Augmentation in EEG Biomarker Studies for Alzheimers Risk Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Isaza,+V+H">Veronica Henao Isaza</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Aguillon,+D">David Aguillon</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Quintero,+C+A+T">Carlos Andres Tobon Quintero</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lopera,+F">Francisco Lopera</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Gomez,+J+F+O">John Fredy Ochoa Gomez</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 20 pages, 7 figures, 2 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> Background: Dementia, marked by cognitive decline, is a global health challenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of cases. Electroencephalography (EEG) measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging. Objective: This study integrates signal processing, harmonization, and statistical techniques to enhance sample size and improve AD risk classification reliability. Methods: We used advanced EEG preprocessing, feature extraction, harmonization, and propensity score matching (PSM) to balance healthy non-carriers (HC) and asymptomatic E280A mutation carriers (ACr). Data from four databases were harmonized to adjust site effects while preserving covariates like age and sex. PSM ratios (2:1, 5:1, 10:1) were applied to assess sample size impact on model performance. The final dataset underwent machine learning analysis with decision trees and cross-validation for robust results. Results: Balancing sample sizes via PSM significantly improved classification accuracy, ranging from 0.92 to 0.96 across ratios. This approach enabled precise risk identification even with limited samples. Conclusion: Integrating data processing, harmonization, and balancing techniques improves AD risk classification accuracy, offering potential for other neurodegenerative diseases. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2411.17721" title="Abstract" id="2411.17721"> arXiv:2411.17721 </a> [<a href="/pdf/2411.17721" title="Download PDF" id="pdf-2411.17721" aria-labelledby="pdf-2411.17721">pdf</a>, <a href="/format/2411.17721" title="Other formats" id="oth-2411.17721" aria-labelledby="oth-2411.17721">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Automatic EEG Independent Component Classification Using ICLabel in Python </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Delorme,+A">Arnaud Delorme</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Truong,+D">Dung Truong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pion-Tonachini,+L">Luca Pion-Tonachini</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Makeig,+S">Scott Makeig</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%. </p> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2411.17725" title="Abstract" id="2411.17725"> arXiv:2411.17725 </a> [<a href="/pdf/2411.17725" title="Download PDF" id="pdf-2411.17725" aria-labelledby="pdf-2411.17725">pdf</a>, <a href="https://arxiv.org/html/2411.17725v1" title="View HTML" id="html-2411.17725" aria-labelledby="html-2411.17725" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17725" title="Other formats" id="oth-2411.17725" aria-labelledby="oth-2411.17725">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Channel Prediction for Beyond Diagonal RIS-Assisted MIMO Systems with Channel Aging </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Ginige,+N">Nipuni Ginige</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=de+Sena,+A+S">Arthur Sousa de Sena</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Mahmood,+N+H">Nurul Huda Mahmood</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rajatheva,+N">Nandana Rajatheva</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Latva-aho,+M">Matti Latva-aho</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> arXiv admin note: text overlap with <a href="https://arxiv.org/abs/2406.07387" data-arxiv-id="2406.07387" class="link-https">arXiv:2406.07387</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> Novel reconfigurable intelligent surface (RIS) architectures, known as beyond diagonal RISs (BD-RISs), have been proposed to enhance reflection efficiency and expand RIS capabilities. However, their passive nature, non-diagonal reflection matrix, and the large number of coupled reflecting elements complicate the channel state information (CSI) estimation process. The challenge further escalates in scenarios with fast-varying channels. In this paper, we address this challenge by proposing novel joint channel estimation and prediction strategies with low overhead and high accuracy for two different RIS architectures in a BD-RIS-assisted multiple-input multiple-output system under correlated fast-fading environments with channel aging. The channel estimation procedure utilizes the Tucker2 decomposition with bilinear alternative least squares, which is exploited to decompose the cascade channels of the BD-RIS-assisted system into effective channels of reduced dimension. The channel prediction framework is based on a convolutional neural network combined with an autoregressive predictor. The estimated/predicted CSI is then utilized to optimize the RIS phase shifts aiming at the maximization of the downlink sum rate. Insightful simulation results demonstrate that our proposed approach is robust to channel aging, and exhibits a high estimation accuracy. Moreover, our scheme can deliver a high average downlink sum rate, outperforming other state-of-the-art channel estimation methods. The results also reveal a remarkable reduction in pilot overhead of up to 98\% compared to baseline schemes, all imposing low computational complexity. </p> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2411.17731" title="Abstract" id="2411.17731"> arXiv:2411.17731 </a> [<a href="/pdf/2411.17731" title="Download PDF" id="pdf-2411.17731" aria-labelledby="pdf-2411.17731">pdf</a>, <a href="/format/2411.17731" title="Other formats" id="oth-2411.17731" aria-labelledby="oth-2411.17731">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Soil Characterization of Watermelon Field through Internet of Things: A New Approach to Soil Salinity Measurement </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Rahman,+M+N">Md. Naimur Rahman</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sozol,+S+S">Shafak Shahriar Sozol</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Samsuzzaman,+M">Md. Samsuzzaman</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hossin,+M+S">Md. Shahin Hossin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Islam,+M+T">Mohammad Tariqul Islam</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Islam,+S+T">S.M. Taohidul Islam</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Maniruzzaman,+M">Md. Maniruzzaman</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN). </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2411.17733" title="Abstract" id="2411.17733"> arXiv:2411.17733 </a> [<a href="/pdf/2411.17733" title="Download PDF" id="pdf-2411.17733" aria-labelledby="pdf-2411.17733">pdf</a>, <a href="https://arxiv.org/html/2411.17733v1" title="View HTML" id="html-2411.17733" aria-labelledby="html-2411.17733" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17733" title="Other formats" id="oth-2411.17733" aria-labelledby="oth-2411.17733">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Muthumala,+U">Uditha Muthumala</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+Y">Yuxuan Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Martinez-Rau,+L+S">Luciano Sebastian Martinez-Rau</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bader,+S">Sebastian Bader</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Conference Presentations (Accepted) at IEEE 10th World Forum on Internet of Things. "<a href="https://wfiot2024.iot.ieee.org/program/technical-paper-program"" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) </div> <p class='mathjax'> This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2411.17734" title="Abstract" id="2411.17734"> arXiv:2411.17734 </a> [<a href="/pdf/2411.17734" title="Download PDF" id="pdf-2411.17734" aria-labelledby="pdf-2411.17734">pdf</a>, <a href="/format/2411.17734" title="Other formats" id="oth-2411.17734" aria-labelledby="oth-2411.17734">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+H">Hanxiang Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pour,+S+Z">Saeed Zolfaghary Pour</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yan,+H">Hao Yan</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+P">Powei Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Arigong,+B">Bayaner Arigong</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse comparator network is configured by four novel port-transformation rat-race couplers. In specific, the proposed coupler is designed to symmetrically allocate the sum ({\Sigma}) / delta ({\Delta}) ports with input ports, where a 360掳 phase delay crossover is designed to transform the unsymmetrical ports in the conventional rat-race coupler. This new rat-race coupler resolves the issues in conventional monopulse receiver comparator network design using multilayer and expensive fabrication technology. To verify the design theory, a prototype of the proposed planar monopulse comparator network operating at 2 GHz is designed, simulated, and measured. In addition, the monopulse radiation patterns and direction of arrival are also decently evaluated. To further boost the accuracy of angular information, a deep neural network is introduced to map the misaligned target angular positions in the measurement to the actual physical location under detection. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2411.17747" title="Abstract" id="2411.17747"> arXiv:2411.17747 </a> [<a href="/pdf/2411.17747" title="Download PDF" id="pdf-2411.17747" aria-labelledby="pdf-2411.17747">pdf</a>, <a href="https://arxiv.org/html/2411.17747v1" title="View HTML" id="html-2411.17747" aria-labelledby="html-2411.17747" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17747" title="Other formats" id="oth-2411.17747" aria-labelledby="oth-2411.17747">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Nguyen,+N+T">Nhan Thanh Nguyen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Nguyen,+L+V">Ly V. Nguyen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Shlezinger,+N">Nir Shlezinger</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Eldar,+Y+C">Yonina C. Eldar</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Swindlehurst,+A+L">A. Lee Swindlehurst</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Juntti,+M">Markku Juntti</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> arXiv admin note: substantial text overlap with <a href="https://arxiv.org/abs/2307.04376" data-arxiv-id="2307.04376" class="link-https">arXiv:2307.04376</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders. Building on this, we develop a deep neural network as an unfolded version of the projected gradient ascent algorithm, which we refer to as UPGANet. This approach efficiently optimizes the communication-sensing performance tradeoff with fast convergence, enabled by the learned step sizes. UPGANet preserves the interpretability and flexibility of the conventional PGA optimizer while enhancing performance through data training. Our simulations show that UPGANet achieves up to a 33.5% higher communications sum rate and 2.5 dB lower beampattern error compared to conventional designs based on successive convex approximation and Riemannian manifold optimization. Additionally, it reduces runtime and computational complexity by up to 65% compared to PGA without unfolding. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2411.17748" title="Abstract" id="2411.17748"> arXiv:2411.17748 </a> [<a href="/pdf/2411.17748" title="Download PDF" id="pdf-2411.17748" aria-labelledby="pdf-2411.17748">pdf</a>, <a href="/format/2411.17748" title="Other formats" id="oth-2411.17748" aria-labelledby="oth-2411.17748">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Berramdane,+M+R">Mohammed Riadh Berramdane</a> (IFPEN), <a href="https://arxiv.org/search/eess?searchtype=author&query=Battiston,+A">Alexandre Battiston</a> (IFPEN), <a href="https://arxiv.org/search/eess?searchtype=author&query=Bardi,+M">Michele Bardi</a> (IFPEN), <a href="https://arxiv.org/search/eess?searchtype=author&query=Blet,+N">Nicolas Blet</a> (LEMTA), <a href="https://arxiv.org/search/eess?searchtype=author&query=R%C3%A9my,+B">Benjamin R茅my</a> (LEMTA), <a href="https://arxiv.org/search/eess?searchtype=author&query=Urbain,+M">Matthieu Urbain</a> (LEMTA)</div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> in French language. Conf{茅}rence des jeunes chercheurs en g{茅}nie {茅}lectrique, CNRS; GDR SEEDS, Jun 2024, Le croisic, France </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG); Machine Learning (stat.ML) </div> <p class='mathjax'> Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component thickness disparities or material physical properties, relying solely on experimental measurements. These parametric models emerge as a reliable alternative to FEM simulations and conventional thermal models, significantly simplifying system identification while ensuring high result accuracy. </p> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2411.17752" title="Abstract" id="2411.17752"> arXiv:2411.17752 </a> [<a href="/pdf/2411.17752" title="Download PDF" id="pdf-2411.17752" aria-labelledby="pdf-2411.17752">pdf</a>, <a href="https://arxiv.org/html/2411.17752v1" title="View HTML" id="html-2411.17752" aria-labelledby="html-2411.17752" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17752" title="Other formats" id="oth-2411.17752" aria-labelledby="oth-2411.17752">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Path Loss Prediction Using Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Dempsey,+R">Ryan Dempsey</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ethier,+J">Jonathan Ethier</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Yanikomeroglu,+H">Halim Yanikomeroglu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 5 pages, 5 figures, 3 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> Radio deployments and spectrum planning can benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from high-resolution obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived obstruction metrics. </p> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2411.17755" title="Abstract" id="2411.17755"> arXiv:2411.17755 </a> [<a href="/pdf/2411.17755" title="Download PDF" id="pdf-2411.17755" aria-labelledby="pdf-2411.17755">pdf</a>, <a href="https://arxiv.org/html/2411.17755v1" title="View HTML" id="html-2411.17755" aria-labelledby="html-2411.17755" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17755" title="Other formats" id="oth-2411.17755" aria-labelledby="oth-2411.17755">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deciphering Acoustic Emission with Machine Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Berta,+D">D茅nes Berta</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Katzer,+B">Balduin Katzer</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Schulz,+K">Katrin Schulz</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Isp%C3%A1novity,+P+D">P茅ter Dus谩n Isp谩novity</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) </div> <p class='mathjax'> Acoustic emission signals have been shown to accompany avalanche-like events in materials, such as dislocation avalanches in crystalline solids, collapse of voids in porous matter or domain wall movement in ferroics. The data provided by acoustic emission measurements is tremendously rich, but it is rather challenging to precisely connect it to the characteristics of the triggering avalanche. In our work we propose a machine learning based method with which one can infer microscopic details of dislocation avalanches in micropillar compression tests from merely acoustic emission data. As it is demonstrated in the paper, this approach is suitable for the prediction of the force-time response as it can provide outstanding prediction for the temporal location of avalanches and can also predict the magnitude of individual deformation events. Various descriptors (including frequency dependent and independent ones) are utilised in our machine learning approach and their importance in the prediction is analysed. The transferability of the method to other specimen sizes is also demonstrated and the possible application in more generic settings is discussed. </p> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2411.17779" title="Abstract" id="2411.17779"> arXiv:2411.17779 </a> [<a href="/pdf/2411.17779" title="Download PDF" id="pdf-2411.17779" aria-labelledby="pdf-2411.17779">pdf</a>, <a href="https://arxiv.org/html/2411.17779v1" title="View HTML" id="html-2411.17779" aria-labelledby="html-2411.17779" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17779" title="Other formats" id="oth-2411.17779" aria-labelledby="oth-2411.17779">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Decoupling Networks and Super-Quadratic Gains for RIS Systems with Mutual Coupling </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Semmler,+D">Dominik Semmler</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Nossek,+J+A">Josef A. Nossek</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Joham,+M">Michael Joham</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=B%C3%B6ck,+B">Benedikt B枚ck</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Utschick,+W">Wolfgang Utschick</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discussions neglecting mutual coupling can be directly applied when mutual coupling is present by utilizing our proposed decoupling networks. For example, by including decoupling networks, the channel gain maximization in RIS-aided single-input single-output (SISO) systems does not require an iterative algorithm but is given in closed form as opposed to using no decoupling network. In addition, this closed-form solution allows to analytically analyze scenarios under mutual coupling resulting in novel connections to the conventional transmit array gain. In particular, we show that super-quadratic (up to quartic) channel gains w.r.t. the number of RIS elements are possible and, therefore, the system with mutual coupling performs significantly better than the conventional uncoupled system in which only squared gains are possible. We consider diagonal as well as beyond diagonal (BD)-RISs and give various analytical and numerical results, including the inevitable losses at the RIS array. In addition, simulation results validate the superior performance of decoupling networks w.r.t. the channel gain compared to other state-of-the-art methods. </p> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2411.17781" title="Abstract" id="2411.17781"> arXiv:2411.17781 </a> [<a href="/pdf/2411.17781" title="Download PDF" id="pdf-2411.17781" aria-labelledby="pdf-2411.17781">pdf</a>, <a href="https://arxiv.org/html/2411.17781v1" title="View HTML" id="html-2411.17781" aria-labelledby="html-2411.17781" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17781" title="Other formats" id="oth-2411.17781" aria-labelledby="oth-2411.17781">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Etiabi,+Y">Yaya Etiabi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Eldeeb,+E">Eslam Eldeeb</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Shehab,+M">Mohammad Shehab</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Njima,+W">Wafa Njima</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Alves,+H">Hirley Alves</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Alouini,+M">Mohamed-Slim Alouini</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Amhoud,+E+M">El Mehdi Amhoud</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) </div> <p class='mathjax'> Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received signal strength indicator measurements with inertial measurement unit data to enhance localization accuracy. Our proposed GNN architecture, featuring dynamic edge construction (DEC), captures the spatial relationships between access points and underlying data patterns. MetaGraphLoc employs a meta-learning framework to adapt the GNN model to new environments with minimal data collection, significantly reducing calibration efforts. Extensive evaluations demonstrate the effectiveness of MetaGraphLoc. Data fusion reduces localization error by 15.92%, underscoring its importance. The GNN with DEC outperforms traditional deep neural networks by up to 30.89%, considering accuracy. Furthermore, the meta-learning approach enables efficient adaptation to new environments, minimizing data collection requirements. These advancements position MetaGraphLoc as a promising solution for indoor localization, paving the way for improved navigation and location-based services in the ever-evolving Internet of Things networks. </p> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2411.17785" title="Abstract" id="2411.17785"> arXiv:2411.17785 </a> [<a href="/pdf/2411.17785" title="Download PDF" id="pdf-2411.17785" aria-labelledby="pdf-2411.17785">pdf</a>, <a href="https://arxiv.org/html/2411.17785v1" title="View HTML" id="html-2411.17785" aria-labelledby="html-2411.17785" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17785" title="Other formats" id="oth-2411.17785" aria-labelledby="oth-2411.17785">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> New Test-Time Scenario for Biosignal: Concept and Its Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Jo,+Y">Yong-Yeon Jo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lee,+B+T">Byeong Tak Lee</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kim,+B+J">Beom Joon Kim</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hong,+J">Jeong-Ho Hong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lee,+H+S">Hak Seung Lee</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Kwon,+J">Joon-myoung Kwon</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 6 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Machine Learning (cs.LG) </div> <p class='mathjax'> Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples. Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types. Experiments show improved accuracy and adaptability under real-world conditions. </p> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2411.17939" title="Abstract" id="2411.17939"> arXiv:2411.17939 </a> [<a href="/pdf/2411.17939" title="Download PDF" id="pdf-2411.17939" aria-labelledby="pdf-2411.17939">pdf</a>, <a href="https://arxiv.org/html/2411.17939v1" title="View HTML" id="html-2411.17939" aria-labelledby="html-2411.17939" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17939" title="Other formats" id="oth-2411.17939" aria-labelledby="oth-2411.17939">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Signal Detection in Colored Noise Using the Condition Number of $F$-Matrices </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Udupitiya,+T">Tharindu Udupitiya</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Dharmawansa,+P">Prathapasinghe Dharmawansa</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Atapattu,+S">Saman Atapattu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tellambura,+C">Chintha Tellambura</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Debbah,+M">Merouane Debbah</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 3 figures, conference </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> Signal detection in colored noise with an unknown covariance matrix has numerous applications across various scientific and engineering disciplines. The analysis focuses on the square of the condition number $\kappa^2(\cdot)$, defined as the ratio of the largest to smallest eigenvalue $(\lambda_{\text{max}}/\lambda_{\text{min}})$ of the whitened sample covariance matrix $\bm{\widehat{\Psi}}$, constructed from $p$ signal-plus-noise samples and $n$ noise-only samples, both $m$-dimensional. This statistic is denoted as $\kappa^2(\bm{\widehat{\Psi}})$. A finite-dimensional characterization of the false alarm probability for this statistic under the null and alternative hypotheses has been an open problem. Therefore, in this work, we address this by deriving the cumulative distribution function (c.d.f.) of $\kappa^2(\bm{\widehat{\Psi}})$ using the powerful orthogonal polynomial approach in random matrix theory. These c.d.f. expressions have been used to statistically characterize the performance of $\kappa^2(\bm{\widehat{\Psi}})$. </p> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2411.18153" title="Abstract" id="2411.18153"> arXiv:2411.18153 </a> [<a href="/pdf/2411.18153" title="Download PDF" id="pdf-2411.18153" aria-labelledby="pdf-2411.18153">pdf</a>, <a href="https://arxiv.org/html/2411.18153v1" title="View HTML" id="html-2411.18153" aria-labelledby="html-2411.18153" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18153" title="Other formats" id="oth-2411.18153" aria-labelledby="oth-2411.18153">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Cheng,+Y">Yukun Cheng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+W">Wei Chen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Hou,+T">Tianwei Hou</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+G+Y">Geoffrey Ye Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Ai,+B">Bo Ai</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments. </p> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2411.18220" title="Abstract" id="2411.18220"> arXiv:2411.18220 </a> [<a href="/pdf/2411.18220" title="Download PDF" id="pdf-2411.18220" aria-labelledby="pdf-2411.18220">pdf</a>, <a href="https://arxiv.org/html/2411.18220v1" title="View HTML" id="html-2411.18220" aria-labelledby="html-2411.18220" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18220" title="Other formats" id="oth-2411.18220" aria-labelledby="oth-2411.18220">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Djuhera,+A">Aladin Djuhera</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Andrei,+V+C">Vlad C. Andrei</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pourghasemian,+M">Mohsen Pourghasemian</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Gacanin,+H">Haris Gacanin</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Boche,+H">Holger Boche</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Saad,+W">Walid Saad</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> Multi-task large language models (MTLLMs) are important for many applications at the wireless edge, where users demand specialized models to handle multiple tasks efficiently. However, training MTLLMs is complex and exhaustive, particularly when tasks are subject to change. Recently, the concept of model fusion via task vectors has emerged as an efficient approach for combining fine-tuning parameters to produce an MTLLM. In this paper, the problem of enabling edge users to collaboratively craft such MTTLMs via tasks vectors is studied, under the assumption of worst-case adversarial attacks. To this end, first the influence of adversarial noise to multi-task model fusion is investigated and a relationship between the so-called weight disentanglement error and the mean squared error (MSE) is derived. Using hypothesis testing, it is directly shown that the MSE increases interference between task vectors, thereby rendering model fusion ineffective. Then, a novel resilient MTLLM fusion (R-MTLLMF) is proposed, which leverages insights about the LLM architecture and fine-tuning process to safeguard task vector aggregation under adversarial noise by realigning the MTLLM. The proposed R-MTLLMF is then compared for both worst-case and ideal transmission scenarios to study the impact of the wireless channel. Extensive model fusion experiments with vision LLMs demonstrate R-MTLLMF's effectiveness, achieving close-to-baseline performance across eight different tasks in ideal noise scenarios and significantly outperforming unprotected model fusion in worst-case scenarios. The results further advocate for additional physical layer protection for a holistic approach to resilience, from both a wireless and LLM perspective. </p> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2411.18298" title="Abstract" id="2411.18298"> arXiv:2411.18298 </a> [<a href="/pdf/2411.18298" title="Download PDF" id="pdf-2411.18298" aria-labelledby="pdf-2411.18298">pdf</a>, <a href="https://arxiv.org/html/2411.18298v1" title="View HTML" id="html-2411.18298" aria-labelledby="html-2411.18298" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18298" title="Other formats" id="oth-2411.18298" aria-labelledby="oth-2411.18298">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Capacity Maximization for MIMO Channels Assisted by Beyond-Diagonal RIS </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Bj%C3%B6rnson,+E">Emil Bj枚rnson</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Demir,+%C3%96+T">脰zlem Tu臒fe Demir</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 5 pages, 4 figures, submitted as a conference paper </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'> Reconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To further enhance reconfigurability, a new architecture called beyond-diagonal RIS (BD-RIS) has been proposed. It allows for controllable signal flows between RIS elements, resulting in a non-diagonal reflection matrix, unlike the conventional RIS architecture. Previous studies on BD-RIS-assisted communications have predominantly considered single-antenna transmitters/receivers. One recent work provides an iterative capacity-improving algorithm for multiple-input multiple-output (MIMO) setups but without providing geometrical insights. <br>In this paper, we derive the first closed-form capacity-maximizing BD-RIS reflection matrix for a MIMO channel. We describe how this solution pairs together propagation paths, how it behaves when the signal-to-noise ratio is high, and what capacity is achievable with ideal semi-unitary channel matrices. The analytical results are corroborated numerically. </p> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2411.18307" title="Abstract" id="2411.18307"> arXiv:2411.18307 </a> [<a href="/pdf/2411.18307" title="Download PDF" id="pdf-2411.18307" aria-labelledby="pdf-2411.18307">pdf</a>, <a href="/format/2411.18307" title="Other formats" id="oth-2411.18307" aria-labelledby="oth-2411.18307">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Spatial separation of closely-spaced users in measured distributed massive MIMO channels </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Xu,+Y">Yingjie Xu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sandra,+M">Michiel Sandra</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cai,+X">Xuesong Cai</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Willhammar,+S">Sara Willhammar</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tufvesson,+F">Fredrik Tufvesson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> Aiming for the sixth generation (6G) wireless communications, distributed massive multiple-input multiple-output (MIMO) systems hold significant potential for spatial multiplexing. In order to evaluate the ability of a distributed massive MIMO system to spatially separate closely spaced users, this paper presents an indoor channel measurement campaign. The measurements are carried out at a carrier frequency of 5.6 GHz with a bandwidth of 400 MHz, employing distributed antenna arrays with a total of 128 elements. Multiple scalar metrics are selected to evaluate spatial separability in line-of-sight, non line-of-sight, and mixed conditions. Firstly, through studying the singular value spread, it is shown that in line-of-sight conditions, better user orthogonality is achieved with a distributed MIMO setup compared to a co-located MIMO array. Furthermore, the dirty-paper coding (DPC) capacity and zero forcing (ZF) precoding sum-rate capacities are investigated across varying numbers of antennas and their topologies. The results show that in all three conditions, the less complex ZF precoder can be applied in distributed massive MIMO systems while still achieving a large fraction of the DPC capacity. Additionally, in line-of-sight conditions, both sum-rate capacities and user fairness benefit from more antennas and a more distributed antenna topology. However, in the given NLoS condition, the improvement in spatial separability through distributed antenna topologies is limited. </p> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2411.18329" title="Abstract" id="2411.18329"> arXiv:2411.18329 </a> [<a href="/pdf/2411.18329" title="Download PDF" id="pdf-2411.18329" aria-labelledby="pdf-2411.18329">pdf</a>, <a href="https://arxiv.org/html/2411.18329v1" title="View HTML" id="html-2411.18329" aria-labelledby="html-2411.18329" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18329" title="Other formats" id="oth-2411.18329" aria-labelledby="oth-2411.18329">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Two-Timescale Digital Twin Assisted Model Interference and Retraining over Wireless Network </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Cong,+J">Jiayi Cong</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cheng,+G">Guoliang Cheng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=You,+C">Changsheng You</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Huang,+X">Xinyu Huang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wu,+W">Wen Wu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 4 figures </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'> In this paper, we investigate a resource allocation and model retraining problem for dynamic wireless networks by utilizing incremental learning, in which the digital twin (DT) scheme is employed for decision making. A two-timescale framework is proposed for computation resource allocation, mobile user association, and incremental training of user models. To obtain an optimal resource allocation and incremental learning policy, we propose an efficient two-timescale scheme based on hybrid DT-physical architecture with the objective to minimize long-term system delay. Specifically, in the large-timescale, base stations will update the user association and implement incremental learning decisions based on statistical state information from the DT system. Then, in the short timescale, an effective computation resource allocation and incremental learning data generated from the DT system is designed based on deep reinforcement learning (DRL), thus reducing the network system's delay in data transmission, data computation, and model retraining steps. Simulation results demonstrate the effectiveness of the proposed two-timescale scheme compared with benchmark schemes. </p> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2411.18392" title="Abstract" id="2411.18392"> arXiv:2411.18392 </a> [<a href="/pdf/2411.18392" title="Download PDF" id="pdf-2411.18392" aria-labelledby="pdf-2411.18392">pdf</a>, <a href="https://arxiv.org/html/2411.18392v1" title="View HTML" id="html-2411.18392" aria-labelledby="html-2411.18392" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18392" title="Other formats" id="oth-2411.18392" aria-labelledby="oth-2411.18392">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The more, the better? Evaluating the role of EEG preprocessing for deep learning applications </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Del+Pup,+F">Federico Del Pup</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zanola,+A">Andrea Zanola</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Tshimanga,+L+F">Louis Fabrice Tshimanga</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bertoldo,+A">Alessandra Bertoldo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Atzori,+M">Manfredo Atzori</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to IEEE for possible publication. Currently under revision. Federico Del Pup and Andrea Zanola are co-first authors. GitHub repository: see <a href="https://github.com/MedMaxLab/eegprepro" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span> </div> <p class='mathjax'> The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques. However, even deep learning models can underperform if trained with bad processed data. While preprocessing is essential to the analysis of EEG data, there is a need of research examining its precise impact on model performance. This causes uncertainty about whether and to what extent EEG data should be preprocessed in a deep learning scenario. This study aims at investigating the role of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the impact of different levels of preprocessing, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. Six classification tasks (eye blinking, motor imagery, Parkinson's and Alzheimer's disease, sleep deprivation, and first episode psychosis) and four different architectures commonly used in the EEG domain were considered for the evaluation. The analysis of 4800 different trainings revealed statistical differences between the preprocessing pipelines at the intra-task level, for each of the investigated models, and at the inter-task level, for the largest one. Raw data generally leads to underperforming models, always ranking last in averaged score. In addition, models seem to benefit more from minimal pipelines without artifact handling methods, suggesting that EEG artifacts may contribute to the performance of deep neural networks. </p> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2411.18523" title="Abstract" id="2411.18523"> arXiv:2411.18523 </a> [<a href="/pdf/2411.18523" title="Download PDF" id="pdf-2411.18523" aria-labelledby="pdf-2411.18523">pdf</a>, <a href="https://arxiv.org/html/2411.18523v1" title="View HTML" id="html-2411.18523" aria-labelledby="html-2411.18523" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18523" title="Other formats" id="oth-2411.18523" aria-labelledby="oth-2411.18523">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Non-reciprocal BD-RIS in Full-duplex Communications: A Perspective on Sum-rate Maximization </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+Z">Ziang Liu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+H">Hongyu Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Clerckx,+B">Bruno Clerckx</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to IEEE journal </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Systems and Control (eess.SY) </div> <p class='mathjax'> Reconfigurable intelligent surface (RIS) has been envisioned as a key technology in future wireless communication networks to enable smart radio environment. To further enhance the passive beamforming capability of RIS, beyond diagonal (BD)-RIS has been proposed considering interconnections among different RIS elements. BD-RIS has a unique feature that cannot be enabled by conventional diagonal RIS; it can be realized by non-reciprocal circuits and thus has asymmetric scattering matrix. This feature provides probability to break the wireless channel reciprocity, and thus has potential to benefit the full-duplex (FD) system. In this paper, we model the BD RIS-assisted FD systems, where the impact of BD-RIS non-reciprocity and that of structural scattering, which refers to the virtual direct channel constructed by RIS when the RIS is turned OFF, are explicitly captured. To visualize the analysis, we propose to design the scattering matrix, precoder and combiner to maximize the DL and UL sum-rates in the FD system. To tackle this optimization problem, we propose an iterative algorithm based on block coordination descent (BCD) and penalty dual decomposition (PDD). Numerical results demonstrate surprising benefits of non-reciprocal BD-RIS that it can achieve higher DL and UL sum-rates in the FD scenario than reciprocal BD-RIS and conventional diagonal RIS. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 9 of 9 entries)</h3> <dt> <a name='item29'>[29]</a> <a href ="/abs/2411.17728" title="Abstract" id="2411.17728"> arXiv:2411.17728 </a> (cross-list from cond-mat.str-el) [<a href="/pdf/2411.17728" title="Download PDF" id="pdf-2411.17728" aria-labelledby="pdf-2411.17728">pdf</a>, <a href="https://arxiv.org/html/2411.17728v1" title="View HTML" id="html-2411.17728" aria-labelledby="html-2411.17728" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17728" title="Other formats" id="oth-2411.17728" aria-labelledby="oth-2411.17728">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Analytic Continuation by Feature Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cond-mat?searchtype=author&query=Zhao,+Z">Zhe Zhao</a>, <a href="https://arxiv.org/search/cond-mat?searchtype=author&query=Xu,+J">Jingping Xu</a>, <a href="https://arxiv.org/search/cond-mat?searchtype=author&query=Wang,+C">Ce Wang</a>, <a href="https://arxiv.org/search/cond-mat?searchtype=author&query=Yang,+Y">Yaping Yang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 9 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Strongly Correlated Electrons (cond-mat.str-el)</span>; Machine Learning (cs.LG); Signal Processing (eess.SP); Computational Physics (physics.comp-ph); Machine Learning (stat.ML) </div> <p class='mathjax'> Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature Learning Network (FL-net), to enhance the prediction accuracy of spectral functions, achieving an improvement of at least $20\%$ over traditional methods, such as the Maximum Entropy Method (MEM), and previous neural network approaches. Furthermore, we develop an analytical method to evaluate the robustness of the proposed network. Using this method, we demonstrate that increasing the hidden dimensionality of FL-net, while leading to lower loss, results in decreased robustness. Overall, our model provides valuable insights into effectively addressing the complex challenges associated with analytic continuation. </p> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2411.17986" title="Abstract" id="2411.17986"> arXiv:2411.17986 </a> (cross-list from cs.IT) [<a href="/pdf/2411.17986" title="Download PDF" id="pdf-2411.17986" aria-labelledby="pdf-2411.17986">pdf</a>, <a href="https://arxiv.org/html/2411.17986v1" title="View HTML" id="html-2411.17986" aria-labelledby="html-2411.17986" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17986" title="Other formats" id="oth-2411.17986" aria-labelledby="oth-2411.17986">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hybrid Beamforming Design for Covert mmWave MIMO with Finite-Resolution DACs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ci,+W">Wei Ci</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qi,+C">Chenhao Qi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=You,+X">Xiaohu You</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'> We investigate hybrid beamforming design for covert millimeter wave multiple-input multiple-output systems with finite-resolution digital-to-analog converters (DACs), which impose practical hardware constraints not yet considered by the existing works and have negative impact on the covertness. Based on the additive quantization noise model, we derive the detection error probability of the warden considering finite-resolution DACs. Aiming at maximizing the sum covert rate (SCR) between the transmitter and legitimate users, we design hybrid beamformers subject to power and covertness constraints. To solve this nonconvex joint optimization problem, we propose an alternating optimization (AO) scheme based on fractional programming, quadratic transformation, and inner majorization-minimization methods to iteratively optimize the analog and digital beamformers. To reduce the computational complexity of the AO scheme, we propose a vector-space based heuristic (VSH) scheme to design the hybrid beamformer. We prove that as the number of antennas grows to be infinity, the SCR in the VSH scheme can approach the channel mutual information. Simulation results show that the AO and VSH schemes outperform the existing schemes and the VSH scheme can be used to obtain an initialization for the AO scheme to speed up its convergence. </p> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2411.17990" title="Abstract" id="2411.17990"> arXiv:2411.17990 </a> (cross-list from cs.IT) [<a href="/pdf/2411.17990" title="Download PDF" id="pdf-2411.17990" aria-labelledby="pdf-2411.17990">pdf</a>, <a href="https://arxiv.org/html/2411.17990v1" title="View HTML" id="html-2411.17990" aria-labelledby="html-2411.17990" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.17990" title="Other formats" id="oth-2411.17990" aria-labelledby="oth-2411.17990">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Beam Switching Based Beam Design for High-Speed Train mmWave Communications </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+J">Jingjia Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qi,+C">Chenhao Qi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dobre,+O+A">Octavia A. Dobre</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+G+Y">Geoffrey Ye Li</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'> For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimization problem aiming to minimize the number of switched beams within a predetermined railway range subject to that the receiving signal-to-noise ratio (RSNR) at the HST is no lower than a predetermined threshold. To solve this problem, we propose two sequential beam design schemes, both including two alternately-performed stages. In the first stage, given an updated beam coverage according to the railway range, we transform the problem into a feasibility problem and further convert it into a min-max optimization problem by relaxing the RSNR constraints into a penalty of the objective function. In the second stage, we evaluate the feasibility of the beamformer obtained from solving the min-max problem and determine the beam coverage accordingly. Simulation results show that compared to the first scheme, the second scheme can achieve 96.20\% reduction in computational complexity at the cost of only 0.0657\% performance degradation. </p> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2411.18123" title="Abstract" id="2411.18123"> arXiv:2411.18123 </a> (cross-list from cs.IT) [<a href="/pdf/2411.18123" title="Download PDF" id="pdf-2411.18123" aria-labelledby="pdf-2411.18123">pdf</a>, <a href="https://arxiv.org/html/2411.18123v1" title="View HTML" id="html-2411.18123" aria-labelledby="html-2411.18123" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18123" title="Other formats" id="oth-2411.18123" aria-labelledby="oth-2411.18123">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adaptive Cell Range Expansion in Multi-Band UAV Communication Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Feng,+X">Xinsong Feng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Roberts,+I+P">Ian P. Roberts</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 leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analysis and optimization. We then propose a simple yet effective user-UAV association policy, which strategically biases users towards mmWave UAVs to take advantage of lower interference and wider bandwidths compared to low-frequency UAVs. Under this scheme, we analytically derive the corresponding association probability, coverage probability, and spectral efficiency. We conclude by assessing our proposed association policy through simulation and analysis, demonstrating its effectiveness based on coverage probability and per-user data rates, as well as the alignment between analytical and simulation results. </p> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2411.18129" title="Abstract" id="2411.18129"> arXiv:2411.18129 </a> (cross-list from cs.NI) [<a href="/pdf/2411.18129" title="Download PDF" id="pdf-2411.18129" aria-labelledby="pdf-2411.18129">pdf</a>, <a href="https://arxiv.org/html/2411.18129v1" title="View HTML" id="html-2411.18129" aria-labelledby="html-2411.18129" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18129" title="Other formats" id="oth-2411.18129" aria-labelledby="oth-2411.18129">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Edge-Assisted Accelerated Cooperative Sensing for CAVs: Task Placement and Resource Allocation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yuxuan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qu,+K">Kaige Qu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+W">Wen Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xuemin">Xuemin</a> (Sherman)<a href="https://arxiv.org/search/cs?searchtype=author&query=Shen">Shen</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Networking and Internet Architecture (cs.NI)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized to process tasks in real time. To this end, for each task, we decide whether to compute it at the CAV or at the RSU and allocate resources accordingly. We first formulate a joint task placement and resource allocation problem for minimizing the total task completion time while satisfying sensing accuracy constraint. We then decouple the problem into two subproblems and propose a two-layer algorithm to solve them. The outer layer first makes task placement decision based on the Gibbs sampling theory, while the inner layer makes spectrum and computing resource allocation decisions via greedy-based and convex optimization subroutines, respectively. Simulation results based on the autonomous driving simulator CARLA demonstrate the effectiveness of the proposed scheme in reducing total task completion time, comparing to benchmark schemes. </p> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2411.18199" title="Abstract" id="2411.18199"> arXiv:2411.18199 </a> (cross-list from cs.LG) [<a href="/pdf/2411.18199" title="Download PDF" id="pdf-2411.18199" aria-labelledby="pdf-2411.18199">pdf</a>, <a href="https://arxiv.org/html/2411.18199v1" title="View HTML" id="html-2411.18199" aria-labelledby="html-2411.18199" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18199" title="Other formats" id="oth-2411.18199" aria-labelledby="oth-2411.18199">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+M">Milin Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abdi,+M">Mohammad Abdi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dasari,+V+R">Venkat R. Dasari</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Restuccia,+F">Francesco Restuccia</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted to ACM Computing Surveys (CSUR) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP) </div> <p class='mathjax'> Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges. </p> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2411.18222" title="Abstract" id="2411.18222"> arXiv:2411.18222 </a> (cross-list from eess.AS) [<a href="/pdf/2411.18222" title="Download PDF" id="pdf-2411.18222" aria-labelledby="pdf-2411.18222">pdf</a>, <a href="/format/2411.18222" title="Other formats" id="oth-2411.18222" aria-labelledby="oth-2411.18222">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Improved Objective Perceptual Audio Quality Assessment -- Part 1: A Novel Data-Driven Cognitive Model </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Delgado,+P+M">Pablo M. Delgado</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Herre,+J">J眉rgen Herre</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepter for publication in in IEEE/ACM Transactions on Audio, Speech, and Language Processing </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 4661-4675, 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Audio and Speech Processing (eess.AS)</span>; Sound (cs.SD); Signal Processing (eess.SP) </div> <p class='mathjax'> Efficient audio quality assessment is vital for streamlining audio codec development. Objective assessment tools have been developed over time to algorithmically predict quality ratings from subjective assessments, the gold standard for quality judgment. Many of these tools use perceptual auditory models to extract audio features that are mapped to a basic audio quality score prediction using machine learning algorithms and subjective scores as training data. However, existing tools struggle with generalization in quality prediction, especially when faced with unknown signal and distortion types. This is particularly evident in the presence of signals coded using non-waveform-preserving parametric techniques. Addressing these challenges, this two-part work proposes extensions to the Perceptual Evaluation of Audio Quality (PEAQ - ITU-R BS.1387-1) recommendation. Part 1 focuses on increasing generalization, while Part 2 targets accurate spatial audio quality measurement in audio coding. <br>To enhance prediction generalization, this paper (Part 1) introduces a novel machine learning approach that uses subjective data to model cognitive aspects of audio quality perception. The proposed method models the perceived severity of audible distortions by adaptively weighting different distortion metrics. The weights are determined using an interaction cost function that captures relationships between distortion salience and cognitive effects. Compared to other machine learning methods and established tools, the proposed architecture achieves higher prediction accuracy on large databases of previously unseen subjective quality scores. The perceptually-motivated model offers a more manageable alternative to general-purpose machine learning algorithms, allowing potential extensions and improvements to multi-dimensional quality measurement without complete retraining. </p> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2411.18480" title="Abstract" id="2411.18480"> arXiv:2411.18480 </a> (cross-list from cs.IT) [<a href="/pdf/2411.18480" title="Download PDF" id="pdf-2411.18480" aria-labelledby="pdf-2411.18480">pdf</a>, <a href="https://arxiv.org/html/2411.18480v1" title="View HTML" id="html-2411.18480" aria-labelledby="html-2411.18480" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18480" title="Other formats" id="oth-2411.18480" aria-labelledby="oth-2411.18480">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Novel Q-stem Connected Architecture for Beyond-Diagonal Reconfigurable Intelligent Surfaces </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+X">Xiaohua Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fang,+T">Tianyu Fang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mao,+Y">Yijie Mao</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'> Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has garnered significant research interest recently due to its ability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide enhanced performance through flexible inter-connection among RIS elements. However, current BD-RIS designs often face challenges related to high circuit complexity and computational complexity, and there is limited study on the trade-off between system performance and circuit complexity. To address these issues, in this work, we propose a novel BD-RIS architecture named Q-stem connected RIS that integrates the characteristics of existing single connected, tree connected, and fully connected BD-RIS, facilitating an effective trade-off between system performance and circuit complexity. Additionally, we propose two algorithms to design the RIS scattering matrix for a Q-stem connected RIS aided multi-user broadcast channels, namely, a low-complexity least squares (LS) algorithm and a suboptimal LS-based quasi-Newton algorithm. Simulations show that the proposed architecture is capable of attaining the sum channel gain achieved by fully connected RIS while reducing the circuit complexity. Moreover, the proposed LS-based quasi-Newton algorithm significantly outperforms the baselines, while the LS algorithm provides comparable performance with a substantial reduction in computational complexity. </p> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2411.18587" title="Abstract" id="2411.18587"> arXiv:2411.18587 </a> (cross-list from cs.HC) [<a href="/pdf/2411.18587" title="Download PDF" id="pdf-2411.18587" aria-labelledby="pdf-2411.18587">pdf</a>, <a href="https://arxiv.org/html/2411.18587v1" title="View HTML" id="html-2411.18587" aria-labelledby="html-2411.18587" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.18587" title="Other formats" id="oth-2411.18587" aria-labelledby="oth-2411.18587">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> EEG-Based Analysis of Brain Responses in Multi-Modal Human-Robot Interaction: Modulating Engagement </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Oliver,+S">Suzanne Oliver</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kitago,+T">Tomoko Kitago</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Buchwald,+A">Adam Buchwald</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Atashzar,+S+F">S. Farokh Atashzar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages, 7 figures. Submitted to IEEE TNSRE </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC) </div> <p class='mathjax'> User engagement, cognitive participation, and motivation during task execution in physical human-robot interaction are crucial for motor learning. These factors are especially important in contexts like robotic rehabilitation, where neuroplasticity is targeted. However, traditional robotic rehabilitation systems often face challenges in maintaining user engagement, leading to unpredictable therapeutic outcomes. To address this issue, various techniques, such as assist-as-needed controllers, have been developed to prevent user slacking and encourage active participation. In this paper, we introduce a new direction through a novel multi-modal robotic interaction designed to enhance user engagement by synergistically integrating visual, motor, cognitive, and auditory (speech recognition) tasks into a single, comprehensive activity. To assess engagement quantitatively, we compared multiple electroencephalography (EEG) biomarkers between this multi-modal protocol and a traditional motor-only protocol. Fifteen healthy adult participants completed 100 trials of each task type. Our findings revealed that EEG biomarkers, particularly relative alpha power, showed statistically significant improvements in engagement during the multi-modal task compared to the motor-only task. Moreover, while engagement decreased over time in the motor-only task, the multi-modal protocol maintained consistent engagement, suggesting that users could remain engaged for longer therapy sessions. Our observations on neural responses during interaction indicate that the proposed multi-modal approach can effectively enhance user engagement, which is critical for improving outcomes. This is the first time that objective neural response highlights the benefit of a comprehensive robotic intervention combining motor, cognitive, and auditory functions in healthy subjects. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 10 of 10 entries)</h3> <dt> <a name='item38'>[38]</a> <a href ="/abs/2306.08730" title="Abstract" id="2306.08730"> arXiv:2306.08730 </a> (replaced) [<a href="/pdf/2306.08730" title="Download PDF" id="pdf-2306.08730" aria-labelledby="pdf-2306.08730">pdf</a>, <a href="https://arxiv.org/html/2306.08730v2" title="View HTML" id="html-2306.08730" aria-labelledby="html-2306.08730" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2306.08730" title="Other formats" id="oth-2306.08730" aria-labelledby="oth-2306.08730">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Over-the-Air Learning-based Geometry Point Cloud Transmission </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Bian,+C">Chenghong Bian</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Shao,+Y">Yulin Shao</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Gunduz,+D">Deniz Gunduz</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, submitted to IEEE journal </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Multimedia (cs.MM) </div> <p class='mathjax'> This paper presents novel solutions for the efficient and reliable transmission of 3D point clouds over wireless channels. We first propose SEPT for the transmission of small-scale point clouds, which encodes the point cloud via an iterative downsampling and feature extraction process. At the receiver, SEPT decoder reconstructs the point cloud with latent reconstruction and offset-based upsampling. A novel channel-adaptive module is proposed to allow SEPT to operate effectively over a wide range of channel conditions. Next, we propose OTA-NeRF, a scheme inspired by neural radiance fields. OTA-NeRF performs voxelization to the point cloud input and learns to encode the voxelized point cloud into a neural network. Instead of transmitting the extracted feature vectors as in the SEPT scheme, it transmits the learned neural network weights over the air in an analog fashion along with few hyperparameters that are transmitted digitally. At the receiver, the OTA-NeRF decoder reconstructs the original point cloud using the received noisy neural network weights. To further increase the bandwidth efficiency of the OTA-NeRF scheme, a fine-tuning algorithm is developed, where only a fraction of the neural network weights are retrained and transmitted. Extensive numerical experiments confirm that both the SEPT and the OTA-NeRF schemes achieve superior or comparable performance over the conventional approaches, where an octree-based or a learning-based point cloud compression scheme is concatenated with a channel code. As an additional advantage, both schemes mitigate the cliff and leveling effects making them particularly attractive for highly mobile scenarios, where accurate channel estimation is challenging if not impossible. </p> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2304.08458" title="Abstract" id="2304.08458"> arXiv:2304.08458 </a> (replaced) [<a href="/pdf/2304.08458" title="Download PDF" id="pdf-2304.08458" aria-labelledby="pdf-2304.08458">pdf</a>, <a href="https://arxiv.org/html/2304.08458v3" title="View HTML" id="html-2304.08458" aria-labelledby="html-2304.08458" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2304.08458" title="Other formats" id="oth-2304.08458" aria-labelledby="oth-2304.08458">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Performance Analysis of Indoor VLC Network with Secure Downlink NOMA for Body Blockage Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+T">Tianji Shen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yachongka,+V">Vamoua Yachongka</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hama,+Y">Yuto Hama</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ochiai,+H">Hideki Ochiai</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 13 figures. This work has been submitted to the IEEE for possible publication </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'> In this work, we investigate the performance of indoor visible light communication (VLC) networks based on power domain non-orthogonal multiple access (NOMA) for mobile devices, where multiple legitimate users are equipped with photodiodes (PDs). We propose a body blockage model for both the legitimate users and eavesdropper to address scenarios where the communication links from transmitting light-emitting diodes (LEDs) to receiving devices are blocked by the bodies of all parties. Furthermore, in order to improve the secrecy without requiring knowledge of the channel state information (CSI) of the eavesdropper, we propose a novel LED arrangement that reduces the overlapping areas covered by different LED units supporting distinct users. We also suggest two LED transmission strategies, i.e., simple and smart LED linking, and compare their performance with the conventional broadcasting in terms of transmission sum rate and secrecy sum rate. Through computer simulations, we demonstrate the superiority of our proposed strategies to the conventional approach. </p> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2311.10270" title="Abstract" id="2311.10270"> arXiv:2311.10270 </a> (replaced) [<a href="/pdf/2311.10270" title="Download PDF" id="pdf-2311.10270" aria-labelledby="pdf-2311.10270">pdf</a>, <a href="https://arxiv.org/html/2311.10270v5" title="View HTML" id="html-2311.10270" aria-labelledby="html-2311.10270" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2311.10270" title="Other formats" id="oth-2311.10270" aria-labelledby="oth-2311.10270">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multiscale Hodge Scattering Networks for Data Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saito,+N">Naoki Saito</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schonsheck,+S+C">Stefan C. Schonsheck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shvarts,+E">Eugene Shvarts</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 20 Pages, Comments Welcome </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Social and Information Networks (cs.SI); Signal Processing (eess.SP); Numerical Analysis (math.NA); Machine Learning (stat.ML) </div> <p class='mathjax'> We propose new scattering networks for signals measured on simplicial complexes, which we call \emph{Multiscale Hodge Scattering Networks} (MHSNs). Our construction is based on multiscale basis dictionaries on simplicial complexes, i.e., the $\kappa$-GHWT and $\kappa$-HGLET, which we recently developed for simplices of dimension $\kappa \in \mathbb{N}$ in a given simplicial complex by generalizing the node-based Generalized Haar-Walsh Transform (GHWT) and Hierarchical Graph Laplacian Eigen Transform (HGLET). The $\kappa$-GHWT and the $\kappa$-HGLET both form redundant sets (i.e., dictionaries) of multiscale basis vectors and the corresponding expansion coefficients of a given signal. Our MHSNs use a layered structure analogous to a convolutional neural network (CNN) to cascade the moments of the modulus of the dictionary coefficients. The resulting features are invariant to reordering of the simplices (i.e., node permutation of the underlying graphs). Importantly, the use of multiscale basis dictionaries in our MHSNs admits a natural pooling operation that is akin to local pooling in CNNs, and which may be performed either locally or per-scale. These pooling operations are harder to define in both traditional scattering networks based on Morlet wavelets, and geometric scattering networks based on Diffusion Wavelets. As a result, we are able to extract a rich set of descriptive yet robust features that can be used along with very simple machine learning methods (i.e., logistic regression or support vector machines) to achieve high-accuracy classification systems with far fewer parameters to train than most modern graph neural networks. Finally, we demonstrate the usefulness of our MHSNs in three distinct types of problems: signal classification, domain (i.e., graph/simplex) classification, and molecular dynamics prediction. </p> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/2312.02826" title="Abstract" id="2312.02826"> arXiv:2312.02826 </a> (replaced) [<a href="/pdf/2312.02826" title="Download PDF" id="pdf-2312.02826" aria-labelledby="pdf-2312.02826">pdf</a>, <a href="https://arxiv.org/html/2312.02826v2" title="View HTML" id="html-2312.02826" aria-labelledby="html-2312.02826" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2312.02826" title="Other formats" id="oth-2312.02826" aria-labelledby="oth-2312.02826">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Forest,+F">Florent Forest</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fink,+O">Olga Fink</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted for publication in Sensors. 24 pages </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Sensors, 24(23) 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Machine Learning (stat.ML) </div> <p class='mathjax'> Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various applications. In IFD, they achieve high classification performance from signals in an end-to-end manner, without requiring extensive domain knowledge. However, deep learning models usually only perform well on the data distribution they have been trained on. When applied to a different distribution, they may experience performance drops. This is also observed in IFD, where assets are often operated in working conditions different from those in which labeled data have been collected. Unsupervised domain adaptation (UDA) deals with the scenario where labeled data are available in a source domain, and only unlabeled data are available in a target domain, where domains may correspond to operating conditions. Recent methods rely on training with confident pseudo-labels for target samples. However, the confidence-based selection of pseudo-labels is hindered by poorly calibrated confidence estimates in the target domain, primarily due to over-confident predictions, which limits the quality of pseudo-labels and leads to error accumulation. In this paper, we propose a novel UDA method called Calibrated Adaptive Teacher (CAT), where we propose to calibrate the predictions of the teacher network throughout the self-training process, leveraging post-hoc calibration techniques. We evaluate CAT on domain-adaptive IFD and perform extensive experiments on the Paderborn benchmark for bearing fault diagnosis under varying operating conditions. Our proposed method achieves state-of-the-art performance on most transfer tasks. </p> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2407.04127" title="Abstract" id="2407.04127"> arXiv:2407.04127 </a> (replaced) [<a href="/pdf/2407.04127" title="Download PDF" id="pdf-2407.04127" aria-labelledby="pdf-2407.04127">pdf</a>, <a href="https://arxiv.org/html/2407.04127v3" title="View HTML" id="html-2407.04127" aria-labelledby="html-2407.04127" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2407.04127" title="Other formats" id="oth-2407.04127" aria-labelledby="oth-2407.04127">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Z">Zhaodong Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+X">Xiaobai Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Komulainen,+J">Jukka Komulainen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+G">Guoying Zhao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> accepted by IJCB 2024, Best Paper Runner-Up Award </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP) </div> <p class='mathjax'> Remote photoplethysmography (rPPG) is a non-contact method for measuring cardiac signals from facial videos, offering a convenient alternative to contact photoplethysmography (cPPG) obtained from contact sensors. Recent studies have shown that each individual possesses a unique cPPG signal morphology that can be utilized as a biometric identifier, which has inspired us to utilize the morphology of rPPG signals extracted from facial videos for person authentication. Since the facial appearance and rPPG are mixed in the facial videos, we first de-identify facial videos to remove facial appearance while preserving the rPPG information, which protects facial privacy and guarantees that only rPPG is used for authentication. The de-identified videos are fed into an rPPG model to get the rPPG signal morphology for authentication. In the first training stage, unsupervised rPPG training is performed to get coarse rPPG signals. In the second training stage, an rPPG-cPPG hybrid training is performed by incorporating external cPPG datasets to achieve rPPG biometric authentication and enhance rPPG signal morphology. Our approach needs only de-identified facial videos with subject IDs to train rPPG authentication models. The experimental results demonstrate that rPPG signal morphology hidden in facial videos can be used for biometric authentication. The code is available at <a href="https://github.com/zhaodongsun/rppg_biometrics" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2407.11837" title="Abstract" id="2407.11837"> arXiv:2407.11837 </a> (replaced) [<a href="/pdf/2407.11837" title="Download PDF" id="pdf-2407.11837" aria-labelledby="pdf-2407.11837">pdf</a>, <a href="/format/2407.11837" title="Other formats" id="oth-2407.11837" aria-labelledby="oth-2407.11837">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Patchkeeper: An Integrated Wearable Electronic Stethoscope with Multiple Sensors </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Hongwei Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Radivojevic,+Z">Zoran Radivojevic</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Eggleston,+M+S">Michael S. Eggleston</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To be published for IEEE Sensors Conference 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Signal Processing (eess.SP) </div> <p class='mathjax'> Many parts of human body generate internal sound during biological processes, which are rich sources of information for understanding health and wellbeing. Despite a long history of development and usage of stethoscopes, there is still a lack of proper tools for recording internal body sound together with complementary sensors for long term monitoring. In this paper, we show our development of a wearable electronic stethoscope, coined Patchkeeper (PK), that can be used for internal body sound recording over long periods of time. Patchkeeper also integrates several state-of-the-art biological sensors, including electrocardiogram (ECG), photoplethysmography (PPG), and inertial measurement unit (IMU) sensors. As a wearable device, Patchkeeper can be placed on various parts of the body to collect sound from particular organs, including heart, lung, stomach, and joints etc. We show in this paper that several vital signals can be recorded simultaneously with high quality. As Patchkeeper can be operated directly by the user, e.g. without involving health care professionals, we believe it could be a useful tool for telemedicine and remote diagnostics. </p> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2409.19884" title="Abstract" id="2409.19884"> arXiv:2409.19884 </a> (replaced) [<a href="/pdf/2409.19884" title="Download PDF" id="pdf-2409.19884" aria-labelledby="pdf-2409.19884">pdf</a>, <a href="https://arxiv.org/html/2409.19884v2" title="View HTML" id="html-2409.19884" aria-labelledby="html-2409.19884" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.19884" title="Other formats" id="oth-2409.19884" aria-labelledby="oth-2409.19884">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+Z">Ziyang Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Thwaites,+A">Andrew Thwaites</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Woolgar,+A">Alexandra Woolgar</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Moore,+B">Brian Moore</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+C">Chao Zhang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> accepted by SLT 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Audio and Speech Processing (eess.AS)</span>; Artificial Intelligence (cs.AI); Sound (cs.SD); Signal Processing (eess.SP) </div> <p class='mathjax'> In complex auditory environments, the human auditory system possesses the remarkable ability to focus on a specific speaker while disregarding others. In this study, a new model named SWIM, a short-window convolution neural network (CNN) integrated with Mamba, is proposed for identifying the locus of auditory attention (left or right) from electroencephalography (EEG) signals without relying on speech envelopes. SWIM consists of two parts. The first is a short-window CNN (SW$_\text{CNN}$), which acts as a short-term EEG feature extractor and achieves a final accuracy of 84.9% in the leave-one-speaker-out setup on the widely used KUL dataset. This improvement is due to the use of an improved CNN structure, data augmentation, multitask training, and model combination. The second part, Mamba, is a sequence model first applied to auditory spatial attention decoding to leverage the long-term dependency from previous SW$_\text{CNN}$ time steps. By joint training SW$_\text{CNN}$ and Mamba, the proposed SWIM structure uses both short-term and long-term information and achieves an accuracy of 86.2%, which reduces the classification errors by a relative 31.0% compared to the previous state-of-the-art result. The source code is available at <a href="https://github.com/windowso/SWIM-ASAD" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2410.06767" title="Abstract" id="2410.06767"> arXiv:2410.06767 </a> (replaced) [<a href="/pdf/2410.06767" title="Download PDF" id="pdf-2410.06767" aria-labelledby="pdf-2410.06767">pdf</a>, <a href="https://arxiv.org/html/2410.06767v2" title="View HTML" id="html-2410.06767" aria-labelledby="html-2410.06767" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.06767" title="Other formats" id="oth-2410.06767" aria-labelledby="oth-2410.06767">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On the Achievable Error Rate Performance of Pilot-Aided Simultaneous Communication and Localisation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+S">Shuaishuai Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alsusa,+E">Emad Alsusa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Al-Jarrah,+M+A">Mohammad Ahmad Al-Jarrah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=AlaaEldin,+M">Mahmoud AlaaEldin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages, 10 figures </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 investigates the symbol error rate (SER) performance of the pilot-aided simultaneous communication and localisation (PASCAL) system. A scenario where multiple drones transmit communication signals to a base station (BS), which needs to simultaneously decode the signals and continuously locate the drones' positions during the communication session, is considered. The BS operates in two stages: first, it estimates the drones' location parameters using pilot signals; second, it performs data detection by reconstructing the channel response based on the estimated location parameters. The theoretical analysis presented demonstrates that the estimated location parameters follow Gaussian distributions with means equal to the actual values and variances determined by the root mean square error (RMSE) of the estimator. Using these distributions, the average SER is derived to quantify the impact of localisation errors on decoding performance. This analysis highlights the synergy between communication and localisation, providing valuable insights into the influence of localisation inaccuracies on the performance of location-aware communication systems. Simulations are conducted to validate the theoretical derivations. </p> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2411.07806" title="Abstract" id="2411.07806"> arXiv:2411.07806 </a> (replaced) [<a href="/pdf/2411.07806" title="Download PDF" id="pdf-2411.07806" aria-labelledby="pdf-2411.07806">pdf</a>, <a href="https://arxiv.org/html/2411.07806v2" title="View HTML" id="html-2411.07806" aria-labelledby="html-2411.07806" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.07806" title="Other formats" id="oth-2411.07806" aria-labelledby="oth-2411.07806">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kang,+T">Tianqu Kang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zixin Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=He,+H">Hengtao He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J">Jun Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+S">Shenghui Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Letaief,+K+B">Khaled B. Letaief</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 3 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR); Signal Processing (eess.SP) </div> <p class='mathjax'> Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on resource-limited devices, combining low-rank adaptation (LoRA) with federated learning enables parameter-efficient fine-tuning. Additionally, the split FedFT architecture partitions an FM between edge devices and a central server, reducing the necessity for complete model deployment on individual devices. However, the risk of privacy eavesdropping attacks in FedFT remains a concern, particularly in sensitive areas such as healthcare and finance. In this paper, we propose a split FedFT framework with differential privacy (DP) over wireless networks, where the inherent wireless channel noise in the uplink transmission is utilized to achieve DP guarantees without adding an extra artificial noise. We shall investigate the impact of the wireless noise on convergence performance of the proposed framework. We will also show that by updating only one of the low-rank matrices in the split FedFT with DP, the proposed method can mitigate the noise amplification effect. Simulation results will demonstrate that the proposed framework achieves higher accuracy under strict privacy budgets compared to baseline methods. </p> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2411.17056" title="Abstract" id="2411.17056"> arXiv:2411.17056 </a> (replaced) [<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/2411.17056v2" 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 networks. Then 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 scenario. To 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 SDR. Numerical 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> </dl> <div class='paging'>Total of 47 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/eess.SP/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; 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