IEEE Communications Letters最新文献_第5页

RIS-Aided XL-MIMO Channel Estimation Based on Expectation-Maximization

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-08 DOI: 10.1109/LCOMM.2024.3476348

Xiao Zhang;Hua Shao;Wenyu Zhang;Zhiwei Xie;Xianze Yang;Wenpeng Jing

引用次数: 0

A Generalized STAR-RIS-Empowered Ambient Backscatter Short-Packet Communication Systems With Partial NOMA

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-07 DOI: 10.1109/LCOMM.2024.3475569

Tien-Hoa Nguyen;Thai-Hoc Vu

引用次数: 0

Boosting Ordered Statistics Decoding of Short LDPC Codes With Simple Neural Network Models

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-07 DOI: 10.1109/LCOMM.2024.3475874

Guangwen Li;Xiao Yu

引用次数: 0

Thermal Noise Modulation: Optimal Detection and Performance Analysis

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-07 DOI: 10.1109/LCOMM.2024.3475747

Mustafa K. Alshawaqfeh;Osamah S. Badarneh;Yazan H. Al-Badarneh;Mohammad T. Dabiri;Mazen O. Hasna

引用次数: 0

A Matrix-Inverse-Free WMMSE Algorithm to MISO Beamforming Based on Quasi-Newton

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-04 DOI: 10.1109/LCOMM.2024.3474251

Mingjun Sun;Zeng Li;Shaochuan Wu;Ruofei Ma;Litong Jiang

引用次数: 0

Dual-Diagonal QC-LDPC Codes Free of Small Cycles From GCD Framework via Negative Coefficients

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-04 DOI: 10.1109/LCOMM.2024.3474209

Guohua Zhang;Mengjuan Lou;Aijing Sun;Yi Fang

{"title":"Dual-Diagonal QC-LDPC Codes Free of Small Cycles From GCD Framework via Negative Coefficients","authors":"Guohua Zhang;Mengjuan Lou;Aijing Sun;Yi Fang","doi":"10.1109/LCOMM.2024.3474209","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3474209","url":null,"abstract":"A novel class of dual-diagonal (DD) quasi-cyclic (QC) low-density parity-check (LDPC) codes with girth eight is proposed. For the exponent matrix of such a DD-QC-LDPC code, the left sub-matrix is explicitly generated via new formulae which are motivated by a search algorithm based on the greatest-common-divisor (GCD) framework and a new strategy allowing negative coefficients. The right sub-matrix (DD matrix) is designed by modifying the existing DD matrix while preserving the simple encoding property directly through the parity-check matrix. Performance simulations indicate that the proposed DD-QC-LDPC codes significantly outperform several existing counterparts with or without DD structure. Unlike existing formulae which involve only non-negative coefficients, the new formulae motivated by proposed search algorithm allow for negative coefficients, which lead to significantly improved lower bounds on circulant sizes for column weights from five to seven. As a result, the new DD-QC-LDPC codes not only enable simple encoding and low description complexity, but also possess consecutive and reasonably small circulant sizes, flexible sizes for exponent matrices as well as good decoding performance.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 12","pages":"2724-2728"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAMC—Optimal on Accuracy and Efficiency for Automatic Modulation Classification With Extended Signal Length

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-04 DOI: 10.1109/LCOMM.2024.3474519

Yezhuo Zhang;Zinan Zhou;Yichao Cao;Guangyu Li;Xuanpeng Li

{"title":"MAMC—Optimal on Accuracy and Efficiency for Automatic Modulation Classification With Extended Signal Length","authors":"Yezhuo Zhang;Zinan Zhou;Yichao Cao;Guangyu Li;Xuanpeng Li","doi":"10.1109/LCOMM.2024.3474519","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3474519","url":null,"abstract":"In Automatic Modulation Classification (AMC), extended signal lengths offer a bounty of information, yet impede the model’s adaptability, introduce more noise interference, extend the training and inference time, and increase memory usage. To bridge the gap between these requirements, we propose a novel AMC framework, designated as the Mamba-based Automatic Modulation Classification (MAMC), which addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model (Mamba), which enhances the model’s capabilities in long-term memory and information selection, and reduces computational complexity and spatial overhead. We further design a denoising unit to filter out effective semantic information to improve accuracy. Rigorous experimental evaluations on the publicly available dataset RML2016.10 and TorchSig affirm that MAMC delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available on \u0000<uri>https://github.com/ZhangYezhuo/MAMC</uri>\u0000.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 12","pages":"2864-2868"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-03 DOI: 10.1109/LCOMM.2024.3473311

Pouya Mehdizadeh;Mohammadreza Dibaj;Hamzeh Beyranvand;Farhad Arpanaei

{"title":"ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs","authors":"Pouya Mehdizadeh;Mohammadreza Dibaj;Hamzeh Beyranvand;Farhad Arpanaei","doi":"10.1109/LCOMM.2024.3473311","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3473311","url":null,"abstract":"Quantum key distribution (QKD) is a cutting-edge technology that guarantees unbreakable security. Multi-band transmission across the O+E+S+C+L bands offers a viable solution for the coexistence of quantum and classical signals over existing fiber infrastructure. However, the secure key rate (SKR) achievable in quantum channels (QChs) is influenced by variations in classical traffic load and its spectrum usage patterns. To support dynamic and time-varying classical traffic, it is essential to estimate the achievable SKR for each QCh in real-time, enabling the selection of the optimal frequency that maximizes SKR. Conventional methods rely on solving complex integral noise equations to estimate SKR, but their computational complexity makes them unsuitable for real-time operations. In this letter, we propose a machine learning (ML) algorithm to evaluate the SKR of QChs, taking into account the time-varying behavior of classical traffic, and to select the optimal frequency for QChs. We implement three ML algorithms across various fiber intervals, all of which estimate the optimal frequency for QChs with 99% accuracy and perform computations in an average of 0.09 seconds— significantly faster than the conventional method, which has a mean computation time of 637 seconds.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 12","pages":"2794-2798"},"PeriodicalIF":3.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low Complex Modulation Classification in NOMA Systems Using Weight Maximization Algorithm

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-02 DOI: 10.1109/LCOMM.2024.3470308

V. C. Abdul Rahim;S. Chris Prema

{"title":"Low Complex Modulation Classification in NOMA Systems Using Weight Maximization Algorithm","authors":"V. C. Abdul Rahim;S. Chris Prema","doi":"10.1109/LCOMM.2024.3470308","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3470308","url":null,"abstract":"In non-orthogonal multiple access (NOMA) systems, the modulation of the interfering users must be known for successive interference cancellation. Automatic modulation classification (AMC) techniques are employed in NOMA to reduce the signal processing overhead required to demodulate interfering signals. However, the existing feature-based approach is highly complex due to the covariance matrix computation in probability density function estimation. This letter presents a low-complexity feature-based approach to classify modulation schemes in three-user NOMA systems. We propose a weight maximization algorithm at the near user (NU) and intermediate user (IU) receivers, which effectively utilizes a weight factor computed using higher-order cumulants of the received superposed signal to classify the far user’s (FU) modulation scheme. Our algorithm achieved 95% classification accuracy at a signal to noise ratio (SNR) of 5 dB and 100% accuracy at a SNR of 12 dB for 800 symbols, with a power allocation factor of 8. Computational analysis showed a reduction of 89.5% in complex addition and 91.8% in complex multiplication operations compared to the state-of-the-art technique.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 12","pages":"2759-2763"},"PeriodicalIF":3.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Decentralized and Scalable Resource Management for Wireless Powered Communication Networks 为无线供电通信网络学习分散式可扩展资源管理

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2024-10-02 DOI: 10.1109/LCOMM.2024.3472067

Kobuljon Ismanov Abdurakhmonovich;Doyun Lee;Seung-Eun Hong;Jaewook Lee;Hoon Lee

{"title":"Learning Decentralized and Scalable Resource Management for Wireless Powered Communication Networks","authors":"Kobuljon Ismanov Abdurakhmonovich;Doyun Lee;Seung-Eun Hong;Jaewook Lee;Hoon Lee","doi":"10.1109/LCOMM.2024.3472067","DOIUrl":"https://doi.org/10.1109/LCOMM.2024.3472067","url":null,"abstract":"This letter presents deep learning approaches for addressing resource allocation problems in wireless-powered communication networks. Conventional deep neural network (DNN) methods require the global channel state information (CSI), invoking impractical centralized operations. Also, their computations depend on the user population, which lacks the scalability of the network size. To this end, we propose decentralized and scalable DNN architectures. We interpret the ideal centralized DNN as a nomographic function that can be decomposed into multiple component DNNs. Each of these is dedicated to processing the local CSI of individual users, thereby leading to the decentralized architecture. To reduce coordination overheads among the H-AP and users, individual users leverage a DNN that compresses local CSI into low-dimensional messages shared with the H-AP. Since these DNN modules are designed to share identical trainable parameters, the proposed learning architecture can be universally applied to various configurations with arbitrary user populations. Numerical results show that the proposed decentralized method achieves almost identical performance to centralized schemes with reduced complexity.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 11","pages":"2563-2567"},"PeriodicalIF":3.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0