IEEE Communications Letters最新文献_第6页

CGAN-Based Data Augmentation for Enhanced Channel Prediction in Massive MIMO Under Subway Tunnels 基于cgan的地铁隧道大规模MIMO信道预测增强数据增强

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-07 DOI: 10.1109/LCOMM.2025.3558561

Yating Wu;Yifeng Li;Rubin Liang;Guoxin Zheng;Xiaoyong Wang

{"title":"CGAN-Based Data Augmentation for Enhanced Channel Prediction in Massive MIMO Under Subway Tunnels","authors":"Yating Wu;Yifeng Li;Rubin Liang;Guoxin Zheng;Xiaoyong Wang","doi":"10.1109/LCOMM.2025.3558561","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3558561","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) channel modeling poses a crucial yet challenging task for wireless communication systems under subway tunnel scenarios. Due to the high cost associated with on-site measurement campaigns, the amount of channel data is often limited and insufficient. This letter proposes a conditional generative adversarial network (cGAN)-based channel model that can learn from the limited measurement data and generate realistic samples of channel impulse response (CIR) matrices specific to given conditions. The proposed channel model provides a data augmentation approach to enhance the training of neural networks for channel prediction, overcoming the prediction performance loss caused by insufficient number of training samples. Simulation results validate that the generated data exhibits high consistency with the actual channel and accurately captures the statistical properties in both delay and spatial domains. By expanding the available training dataset effectively, it is shown that the accuracy of neural network-based channel prediction has improved significantly with the proposed channel data augmentation.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 6","pages":"1255-1259"},"PeriodicalIF":3.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272910","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

GNSS-Independent Uplink LEO Satellite Synchronization 与gnss无关的LEO卫星上行同步

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-07 DOI: 10.1109/LCOMM.2025.3558531

Fredrik Berggren;Branislav M. Popović

引用次数: 0

Nested Grassmannian Codebooks for Quantizing MIMO Precoders on the Flag Manifold 旗形上量化MIMO预编码器的嵌套Grassmannian码本

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-04 DOI: 10.1109/LCOMM.2025.3557002

Naresh Kolla;Parth Mehta;Kumar Appaiah;Debasattam Pal

引用次数: 0

Power Utilization in Open RAN: Key Findings From a USA Testbed 开放式RAN的功率利用率：来自美国试验台的主要发现

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-04 DOI: 10.1109/LCOMM.2025.3557715

Saish Urumkar;Byrav Ramamurthy;Seshu Tirupathi;Sachin Sharma

{"title":"Power Utilization in Open RAN: Key Findings From a USA Testbed","authors":"Saish Urumkar;Byrav Ramamurthy;Seshu Tirupathi;Sachin Sharma","doi":"10.1109/LCOMM.2025.3557715","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3557715","url":null,"abstract":"Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single high-powered device and (2) a physical hardware deployment using the separate low-power device for Open RAN. We present key findings from simulated and physical NodeB experiments conducted on the USA testbed, with real hardware. The findings include the relationship between power utilization and forced throughput, the impact of an increasing number of UEs on power utilization, the effects of different UE connection states (active, idle, or not connected), and the behavior of uplink and downlink throughput in both simulated and physical setups. Additionally, this letter introduces and validates power-law equations for power utilization in both setups, offering valuable insights into the effectiveness of UE connection state switching on NodeB CPU power utilization pattern. These findings contribute to the development of strategies for achieving energy-saving objectives in Open RAN.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 6","pages":"1225-1229"},"PeriodicalIF":3.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Robust Rate-Splitting-Based Precoding for Cell-Free MU-MIMO Systems 无小区MU-MIMO系统的鲁棒预编码

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-03 DOI: 10.1109/LCOMM.2025.3557743

André R. Flores;Rodrigo C. de Lamare

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

MASSFormer: Mobility-Aware Spectrum Sensing Using Transformer-Driven Tiered Structure MASSFormer：使用变压器驱动的分层结构的移动感知频谱传感

IF 3.7 3区计算机科学

IEEE Communications Letters Pub Date : 2025-04-02 DOI: 10.1109/LCOMM.2025.3557217

Dimpal Janu;Faisel Mushtaq;Sandeep Mandia;Kuldeep Singh;Sandeep Kumar

{"title":"MASSFormer: Mobility-Aware Spectrum Sensing Using Transformer-Driven Tiered Structure","authors":"Dimpal Janu;Faisel Mushtaq;Sandeep Mandia;Kuldeep Singh;Sandeep Kumar","doi":"10.1109/LCOMM.2025.3557217","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3557217","url":null,"abstract":"In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability <inline-formula> <tex-math>$P_{d}$ </tex-math></inline-formula>, sensing error, and classification accuracy (CA).","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 6","pages":"1220-1224"},"PeriodicalIF":3.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272796","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

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