IEEE Communications Letters最新文献

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IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-10 DOI: 10.1109/LCOMM.2025.3552242

引用次数: 0

Dynamic Convolution and Transformer Based Dual-Branch Coding in Semantic Communication System 语义通信系统中基于动态卷积和变压器的双支路编码

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-03 DOI: 10.1109/LCOMM.2025.3557421

Jiaxin Yang;Zhiquan Bai;Xiaodong Xu;Xuchao Teng;Mengying Sun;KyungSup Kwak

引用次数: 0

Wavelet-Based Three-Dimensional OTFS With Dual-Mode Index Modulation 基于双模折射率调制的小波三维OTFS

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-02 DOI: 10.1109/LCOMM.2025.3557036

Zhenxing Chen;Si Ouyang

引用次数: 0

Physical IoU-Based YOLO Network for Shortwave Communication Burst Signal Recognition 基于物理io的YOLO网络短波通信突发信号识别

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-02 DOI: 10.1109/LCOMM.2025.3557043

Xiaojun Zhu;Yinsen Luan;Paihang Zhao;Tao Tang;Zhidong Wu

{"title":"Physical IoU-Based YOLO Network for Shortwave Communication Burst Signal Recognition","authors":"Xiaojun Zhu;Yinsen Luan;Paihang Zhao;Tao Tang;Zhidong Wu","doi":"10.1109/LCOMM.2025.3557043","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3557043","url":null,"abstract":"Shortwave communication plays a critical role in military and emergency applications. However, the complex nature of shortwave transmission channels makes automatic burst signal recognition particularly challenging, especially in non-cooperative scenarios. This letter presents an optimized recognition method based on the You Only Look Once (YOLO) network, which accurately identifies signal types while improving the precision of burst time detection. A Physical Intersection over Union (PIoU) is proposed by leveraging the bounding box’s left edge, corresponding to signal burst time, to refine bounding box regression and resolve ambiguities in conventional IoUs. Experiments show the proposed PIoU improves the accuracy of burst time detection by 10.44% under Signal-to-Noise Ratios (SNRs) from -20dB to 18dB, compared to the state-of-the-art IoU methods. Further tests across 8 shortwave communication channels confirm consistent improvements of burst time detection, while other metrics such as Precision, Recall, mean Average Precision at 50% IoU(mAP50), and mAP95 are maintained. These advancements significantly benefit signal decoding and information restoration.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1156-1160"},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931314","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

Deep Joint Source-Channel Coding of Underwater Image Enhancement in AUV Swarms 水下航行器群水下图像增强的深度联合信源信道编码

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-01 DOI: 10.1109/LCOMM.2025.3556673

Zhenguo Zhang;Guang Liu;Minghui Wang;Bo Chen;Zesheng Liu;Xiaojie Zhang

引用次数: 0

FSIM-Net: Neural Network-Based Symbol Detector for Filter Shape Index Modulation FSIM-Net：基于神经网络的滤波器形状指数调制符号检测器

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-03-31 DOI: 10.1109/LCOMM.2025.3556431

Shubham Anand;Haïfa Farès;Majed Saad;Preetam Kumar

引用次数: 0

Optimized Qubit-Based Synchronization in Quantum Key Distribution 量子密钥分发中基于量子比特的优化同步

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-03-28 DOI: 10.1109/LCOMM.2025.3554260

Hao-Bing Sun;Jian Li;Xing-Wang Chu;Jing-Yang Liu;Hua-Jian Ding;Qin Wang

引用次数: 0

Space-to-Ground Optical Communication Downlink Scheduling Under Uncertainty of Link Availability 链路可用性不确定条件下的空地光通信下行调度

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-03-28 DOI: 10.1109/LCOMM.2025.3555945

Pei Lyu;Kanglian Zhao;Shihao Wang;Hangsheng Zhao

引用次数: 0

Service Association and Subcarrier Allocation in Vehicular Slicing Networks With Dynamic Multi-Service 动态多业务车辆切片网络中的业务关联与子载波分配

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-03-28 DOI: 10.1109/LCOMM.2025.3555768

Yijian Hou;Kaisa Zhang;Gang Chuai;Weidong Gao;Xiangyu Chen;Siqi Liu

{"title":"Service Association and Subcarrier Allocation in Vehicular Slicing Networks With Dynamic Multi-Service","authors":"Yijian Hou;Kaisa Zhang;Gang Chuai;Weidong Gao;Xiangyu Chen;Siqi Liu","doi":"10.1109/LCOMM.2025.3555768","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3555768","url":null,"abstract":"The vehicular slicing network with dynamic multi-service (VSN-DMS) enables vehicles to support multiple services with dynamically changing types. In this architecture, vehicles can access multiple base stations (BSs) and leverage different network slices (NSs) to meet the requirements of various services. However, frequent service variations and vehicle mobility introduce high handover (HO) costs and inefficient decision making. This letter proposes a novel two-timescale optimization framework to jointly optimize vehicle service (VS) association and subcarrier (SC) allocation. Specifically, in the long-timescale, we propose a federated deep reinforcement learning (FDRL) algorithm to optimize VS association. Unlike conventional reinforcement learning approaches that require centralized data collection, FDRL enables distributed model training across vehicles while aggregating parameters in the cloud. This mitigates the issue of insufficient training data caused by frequent VS changes, while also ensuring vehicle data privacy. In the short-timescale, we propose a versatile alternating coalitional game and swap-matching game algorithm (ACGSM) to optimize SC allocation. By iteratively executing coalitional formation and swap-matching mechanisms, ACGSM efficiently improves the quality of the solution while adapting to dynamic network conditions. Simulation results demonstrate the convergence of FDRL and show that the proposal outperforms benchmarks in improving long-term QoS and reducing the number of HOs.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 5","pages":"1131-1135"},"PeriodicalIF":3.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932261","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

A Sparsity-Agnostic SL0 Channel Estimation Approach for OTFS Systems 一种OTFS系统稀疏不可知的SL0信道估计方法

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-03-26 DOI: 10.1109/LCOMM.2025.3554379

Xin Wang;Can Zheng;Pengjiang Hu;Junan Yang;Chung G. Kang

引用次数: 0