IEEE Communications Letters最新文献_第4页

Uplink Outage Probability Analysis of AAV and Intelligent Connected Vehicle Cooperative Communication Using Full-Duplex MIMO 基于全双工MIMO的AAV与智能网联车协同通信上行中断概率分析

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-02 DOI: 10.1109/LCOMM.2025.3585337

Yixin He;Fanghui Huang;Dawei Wang;Xingchen Zhou;Ruonan Zhang

{"title":"Uplink Outage Probability Analysis of AAV and Intelligent Connected Vehicle Cooperative Communication Using Full-Duplex MIMO","authors":"Yixin He;Fanghui Huang;Dawei Wang;Xingchen Zhou;Ruonan Zhang","doi":"10.1109/LCOMM.2025.3585337","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3585337","url":null,"abstract":"The rapid advancement of intelligent transportation systems necessitates reliable vehicular communication, yet traditional infrastructure struggles with coverage limitations in dynamic environments. To address the above issue, this letter investigates the uplink outage probability in cooperative communication between the autonomous aerial vehicle (AAV) and intelligent connected vehicles (ICVs) using the full-duplex multiple-input multiple-output (MIMO) technique. First, for non-static MIMO scenarios, we develop a joint effect model by jointly considering the self-interference, co-channel interference and noise, aiming to maximize channel gain. Next, we derive a closed-form expression for the uplink outage probability by employing the Laplace transform of the interference. To reduce complexity, an approximate expression for the uplink outage probability is proposed according to a dynamic truncation criterion. Based on this, we derive a theoretical upper bound for the approximation error. Finally, the simulation results demonstrate that the proposed scheme significantly reduces the outage probability compared to state-of-the-art schemes, with the approximate solution showing less than 6% error and requiring only 7% of the computational time of the closed-form solution. Additionally, the impact of transmission power, antenna configurations, and retained higher-order terms on the outage probability is thoroughly analyzed.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2068-2072"},"PeriodicalIF":4.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100368","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

Power Minimization for Movable Antenna Enhanced Downlink NOMA 移动天线增强型下行NOMA的功率最小化

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-02 DOI: 10.1109/LCOMM.2025.3585181

Lei Yan;Hong Wang;Zheng Shi

引用次数: 0

Hybrid Statistical and Machine Learning Models for Atmospheric Refractivity Prediction in Wireless Channels 无线信道大气折射率预测的混合统计和机器学习模型

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-01 DOI: 10.1109/LCOMM.2025.3585050

Mohammed Banafaa;Saleh Alawsh;Ali Muqaibel;Mohammad Alhassoun

{"title":"Hybrid Statistical and Machine Learning Models for Atmospheric Refractivity Prediction in Wireless Channels","authors":"Mohammed Banafaa;Saleh Alawsh;Ali Muqaibel;Mohammad Alhassoun","doi":"10.1109/LCOMM.2025.3585050","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3585050","url":null,"abstract":"Atmospheric ducts, formed by sharp refractive index gradients in the lower atmosphere, play a crucial role in signal propagation, particularly for next-generation communication systems operating in high-frequency bands (sub-6 GHz and beyond), where ducts can significantly modify signal paths. This study refines the standard ITU refractivity model through a region-specific correction and subsequently develops a multiple linear regression (MLR) framework to adapt the model using real meteorological data. The MLR model evaluates predictor significance, addresses multicollinearity, and applies statistical criteria to produce a localized refractivity estimate. To address residual nonlinear patterns not captured by MLR, a machine learning (ML) model is trained on the residuals and integrated with the statistical output to form a hybrid prediction. The region-specific (RS) model shows a systematic deviation from the ITU-based modified refractivity profile, with a mean difference of 4.67 M-units, a standard deviation of 3.49 M-units, and an RMSE of 5.83 M-units. These differences suggest that the ITU profile does not adequately represent local atmospheric conditions. Furthermore, the hybrid model reduces residual error relative to MLR alone. The results highlight the importance of incorporating RS adjustments and data-driven methods in modeling atmospheric ducts for propagation analysis.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2048-2052"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100494","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

Modeling and Analysis of Hybrid GEO-LEO Satellite Networks GEO-LEO混合卫星网络建模与分析

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-01 DOI: 10.1109/LCOMM.2025.3585000

Dong-Hyun Jung;Hongjae Nam;Junil Choi;David J. Love

引用次数: 0

A Learning-Based Flexible Beamforming Method for Movable Antenna-Enabled Integrated Sensing, Communication, and Power Transmission System 可移动天线集成传感、通信和输电系统中基于学习的柔性波束形成方法

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-01 DOI: 10.1109/LCOMM.2025.3584722

Chenfei Xie;Yonghui Li;Qingqing Tu;Yue Xiu;Songjie Yang;Zhenzhen Hu;Jing Jin;Zhongpei Zhang

引用次数: 0

Combining OMP With a Superimposed Pilot Sparse Channel Estimation Method for Underwater Acoustic OCDM Systems 结合OMP和叠加导频稀疏信道估计的水声OCDM系统

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-07-01 DOI: 10.1109/LCOMM.2025.3584761

Rui Xue;Shuaichuang Wang

引用次数: 0

Carrier Frequency Offset Estimation Based on Signal Denoising and Dual-Channel Convolutional Attention Network Under Low Signal-to-Noise Ratio 低信噪比下基于信号去噪和双通道卷积注意网络的载波频偏估计

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-30 DOI: 10.1109/LCOMM.2025.3584405

Yunwei Zhang;Lingxin Zeng;Xiaohong Wang;Yong Gao

{"title":"Carrier Frequency Offset Estimation Based on Signal Denoising and Dual-Channel Convolutional Attention Network Under Low Signal-to-Noise Ratio","authors":"Yunwei Zhang;Lingxin Zeng;Xiaohong Wang;Yong Gao","doi":"10.1109/LCOMM.2025.3584405","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3584405","url":null,"abstract":"In non-cooperative wireless communications, carrier frequency offset (CFO) arises from oscillator mismatch, doppler effect and time-varying channels. Along with noise, they can degrade signal quality and hampers subsequent signal processing. In this letter, we propose a robust carrier frequency offset estimation (CFOE) method under low signal-to-noise ratio (SNR) by leveraging hybrid denoising scheme and dual-channel convolutional attention network (DCA). To enhance noise robustness, we develop a hybrid denoising scheme combining adaptive dual-tree complex wavelet transform with phase-space reconstruction. The designed DCA model can extract deep features from the multi-domain features of the denoised signal. It employs an attention-based parallel dual-pooling mechanism to preserve local-global feature correlation during downsampling. In addition, the composite loss function is used to improve generalization ability and reduce overfitting. Compared with existing blind estimation methods, the experimental results demonstrate the effectiveness and robustness of the proposed CFOE method.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2033-2037"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100313","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

Digital Predistortion of Quadrature Digital Power Amplifiers Using RVRTCNN: Real-Valued Residual Temporal Convolutional Neural Network 基于RVRTCNN的正交数字功率放大器的数字预失真：实值残差时间卷积神经网络

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-30 DOI: 10.1109/LCOMM.2025.3584212

Jiayu Yang;Wending Zhao;Yicheng Li;Wang Wang;Zixu Li;Manni Li;Zijian Huang;Yinyin Lin;Yun Yin;Hongtao Xu

{"title":"Digital Predistortion of Quadrature Digital Power Amplifiers Using RVRTCNN: Real-Valued Residual Temporal Convolutional Neural Network","authors":"Jiayu Yang;Wending Zhao;Yicheng Li;Wang Wang;Zixu Li;Manni Li;Zijian Huang;Yinyin Lin;Yun Yin;Hongtao Xu","doi":"10.1109/LCOMM.2025.3584212","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3584212","url":null,"abstract":"Deep neural networks (DNNs) predistortion models of radio frequency (RF) power amplifiers (PAs), while offering excellent performance, typically suffer from high parameter counts and computational complexity. Convolutional NNs (CNNs) have been introduced to reduce model complexity due to their weight-sharing characteristic. However, the inherent calculation mode of traditional convolutional structures limits their ability to effectively capture temporal dependencies within the data, hindering their effectiveness in addressing memory effects in PAs. In this letter, we propose an enhanced digital predistortion (DPD) model based on a real-valued residual temporal convolutional neural network (RVRTCNN) for quadrature digital PAs (QDPAs). The proposed model incorporates dilated convolutions to extract features across multiple time steps and capture complex temporal dependencies, thereby enhancing its ability to address the dynamic nonlinearity of PAs. A 15-bit transformer-based QDPA chip, integrating Class-G and IQ-cell-sharing techniques, was fabricated by 28 nm CMOS process to validate our proposed method. Experimental results demonstrate that the proposed model achieves superior linearization performance with significantly fewer parameters and lower computational complexity compared to state-of-the-art (SOTA) models, improving both adjacent channel power ratio (ACPR) and error vector magnitude (EVM) by over 10 dB for the 802.11ax 40 MHz 64-QAM signal.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2028-2032"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100412","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

Sensing and Channel Estimation for OTFS-ISAC System With Reduced PAPR 减小PAPR的OTFS-ISAC系统传感与信道估计

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-30 DOI: 10.1109/LCOMM.2025.3584204

Ajay Kumar;Sudhan Majhi;Husheng Li

引用次数: 0

Sneak Path-Aware Reliability-Based Iterative Majority-Logic Decoding Algorithms for LDPC Codes in ReRAM Systems 基于潜行路径感知可靠性的rram系统LDPC码迭代多数逻辑解码算法

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-30 DOI: 10.1109/LCOMM.2025.3584075

Lingjun Kong;Yingnan Qi;Haiyang Liu;Chao Meng

{"title":"Sneak Path-Aware Reliability-Based Iterative Majority-Logic Decoding Algorithms for LDPC Codes in ReRAM Systems","authors":"Lingjun Kong;Yingnan Qi;Haiyang Liu;Chao Meng","doi":"10.1109/LCOMM.2025.3584075","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3584075","url":null,"abstract":"The performance of classical channel decoding algorithms in resistive random access memory (ReRAM) channels is severely degraded due to the sneak path interference (SPI) caused by the crossbar array structure. In this letter, we first propose a sneak path-aware reliability-based iterative majority-logic decoding (SPR-IMLGD) algorithm for low-density parity-check (LDPC) codes by integrating the sneak path information into reliability metrics. In particular, we introduce the sneak path distance, derived from the associated sneak path probability, which provides a desirable characterization of the presence and extent of sneak paths within a given memory cell. Moreover, we present an enhanced version of the SPR-IMLGD algorithm (ESPR-IMLGD) that further improves the error correction performance. We also optimize the calculation of sneak path distance, ensuring that the SPR-IMLGD and ESPR-IMLGD algorithms have the same level of computational complexity. Simulation results demonstrate that the SPR-IMLGD and ESPR-IMLGD algorithms can achieve better performance with lower computational cost compared with the existing methods.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2018-2022"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100457","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