IEEE Communications Letters最新文献_第5页

Multi-In-Multi-Out Neural Network for Joint DOA Estimation and Automatic Modulation Classification 多进多出神经网络联合DOA估计与自动调制分类

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-27 DOI: 10.1109/LCOMM.2025.3583717

Van-Sang Doan;Ha-Khanh Le;Van-Phuc Hoang

{"title":"Multi-In-Multi-Out Neural Network for Joint DOA Estimation and Automatic Modulation Classification","authors":"Van-Sang Doan;Ha-Khanh Le;Van-Phuc Hoang","doi":"10.1109/LCOMM.2025.3583717","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3583717","url":null,"abstract":"Direction of arrival (DOA) estimation and automatic modulation classification (AMC) of radio frequency (RF) signals are two crucial tasks in electronic intelligence systems. These two tasks are traditionally performed in separate individual processes that result in slow latency and computational complexity. In order to mitigate the mentioned issue, a multi-in-multi-out deep neural network (namely MIMONet), which has three inputs and two outputs, is proposed in this letter for joint DOA estimation and AMC applied for uniform circular array. The three inputs are designated for raw in-phase and quadrature-phase signals, Fourier transform data, and covariance matrix. The two outputs are assigned in turn for DOA estimation and AMC. The MIMONet model is analyzed with different hyperparameter options to find the best performance trade-off between DOA estimation and AMC accuracy, computational complexity, and execution time. As a result, the MIMONet model of 32 filters with a size of <inline-formula> <tex-math>$3times 3$ </tex-math></inline-formula> has achieved the best performance with AMC accuracy higher than 95%, root mean square error of DOA estimation below 0.1°, and execution time of <inline-formula> <tex-math>$0.74pm 0.02$ </tex-math></inline-formula> ms for SNRs greater than 10 dB. In comparison, the proposed model has outperformed some other state-of-the-art models in the same experimental scenario.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 8","pages":"1993-1997"},"PeriodicalIF":4.4,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831792","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

Uplink RSMA-Assisted Slotted ALOHA With Adaptive Traffic Load for Massive IoT 面向大规模物联网的上行rsma辅助开槽ALOHA自适应流量负载

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-26 DOI: 10.1109/LCOMM.2025.3583590

I Nyoman Apraz Ramatryana;Gandeva Bayu Satrya;Nyoman Bogi Aditya Karna;Made Adi Paramartha Putra

引用次数: 0

Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach 边缘认知无线电频谱感知：量化感知深度学习方法

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-24 DOI: 10.1109/LCOMM.2025.3582680

Hamza A. Abushahla;Dara Varam;Mohamed I. AlHajri

引用次数: 0

Two-Level Estimation Enabled Online Congestion Control for Massive IoT Networks 大规模物联网网络的两级估计在线拥塞控制

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-24 DOI: 10.1109/LCOMM.2025.3581943

Shilun Song;Jie Liu;Han Seung Jang;Hu Jin

引用次数: 0

Multi-Length CSI Feedback With Ordered Finite Scalar Quantization 有序有限标量量化的多长度CSI反馈

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-23 DOI: 10.1109/LCOMM.2025.3581951

Kosmas Liotopoulos;Nikos A. Mitsiou;Panagiotis G. Sarigiannidis;George K. Karagiannidis

{"title":"Multi-Length CSI Feedback With Ordered Finite Scalar Quantization","authors":"Kosmas Liotopoulos;Nikos A. Mitsiou;Panagiotis G. Sarigiannidis;George K. Karagiannidis","doi":"10.1109/LCOMM.2025.3581951","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3581951","url":null,"abstract":"We propose a novel, lightweight, deep-learning based model, which enables fast, multi-length channel state information (CSI) feedback. The proposed method harnesses the advantages of finite scalar quantization and ordered representation learning, to create the ordered finite scalar quantization (OFSQ) scheme, which has a simple structure, with significantly reduced complexity, while demonstrating solid CSI reconstruction ability for any desired feedback bitstream length. Our method reshapes latent vectors into sub-vectors, applies a hyperparameter-based and bounded scalar quantization, while it integrates a nested dropout layer to prioritize sub-vectors based on their importance to CSI retrieval. Simulation results confirm that the proposed scheme significantly reduces the computational complexity, as it avoids to exhaustively search the quantization codebook, while it shows an improved CSI reconstruction ability compared to state-of-the-art multi-length CSI feedback models. Therefore, OFSQ is a promising plug-in architecture, which can be paired with any autoencoder for use in wireless communication systems.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 8","pages":"1973-1977"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831703","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

Effective Application of Normalized Min-Sum Decoding for Short BCH Codes 归一化最小和译码在短BCH码中的有效应用

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-23 DOI: 10.1109/LCOMM.2025.3582300

Guangwen Li;Xiao Yu

{"title":"Effective Application of Normalized Min-Sum Decoding for Short BCH Codes","authors":"Guangwen Li;Xiao Yu","doi":"10.1109/LCOMM.2025.3582300","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3582300","url":null,"abstract":"This letter introduces an enhanced normalized min-sum decoder designed to address the performance and complexity challenges associated with developing parallelizable decoders for short BCH codes in high-throughput applications. The decoder optimizes the standard parity-check matrix using heuristic binary summation and random cyclic row shifts, resulting in a Tanner graph with low density, controlled redundancy, and minimized length-4 cycles. The impact of row redundancy and rank deficiency in the dual code’s minimum-weight codewords on decoding performance is analyzed. To improve convergence, three random automorphisms are applied simultaneously to the inputs, with the resulting messages merged at the end of each iteration. Extensive simulations demonstrate that, for BCH codes with block lengths of 63 and 127, the enhanced normalized min-sum decoder achieves a 1–2 dB performance gain and <inline-formula> <tex-math>$100times $ </tex-math></inline-formula> faster convergence compared to existing parallel and iterative decoders. Additionally, a hybrid decoding scheme is proposed, which selectively activates order statistics decoding when the enhanced normalized min-sum decoder fails. This hybrid approach is shown to approach maximum-likelihood performance while retaining the advantages of the normalized min-sum decoder across a broad SNR range.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 8","pages":"1983-1987"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831834","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

MAE-Based Radio Map Construction for Wi-Fi Fingerprint Indoor Localization 基于mae的无线地图构建Wi-Fi指纹室内定位

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-23 DOI: 10.1109/LCOMM.2025.3582075

Yishuo Cheng;Liye Zhang

{"title":"MAE-Based Radio Map Construction for Wi-Fi Fingerprint Indoor Localization","authors":"Yishuo Cheng;Liye Zhang","doi":"10.1109/LCOMM.2025.3582075","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3582075","url":null,"abstract":"This letter proposes a method for constructing a radio map using the Masked Autoencoder (MAE) model, with a focus on enabling accurate indoor fingerprint localization by constructing a complete radio map from minimal Received Signal Strength (RSS) data. Traditional fingerprint localization systems typically require significant time and labor for manual data collection and face challenges in maintaining data completeness. To address these challenges, this letter employs the MAE model with a high-masking strategy to simulate the missing RSS data. The model learns the spatial relationship between signal strength and location, allowing for accurate reconstruction of the radio map despite missing data. Improvements to the MAE model include adjusting input dimensions, modifying position encoding functions, and designing a loss function that targets masked regions. These enhancements help capture the spatial correlation between reference points (RPs) and access points (APs), improving reconstruction accuracy. Experiments results show that the MAE model outperforms traditional methods in effectively filling missing data. With 40% missing data, the error probability within 2 meters is about 95%, and with 80% missing data, it still remains around 93%. Moreover, the MAE model significantly reduces data collection time and requires less data to complete the radio map reconstruction, while saving over 85% of computational and human resources, all while ensuring localization accuracy.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2008-2012"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100351","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

Machine Learning-Based Physical Layer Security for Detecting Active Eavesdropping Attacks 基于机器学习的物理层安全检测主动窃听攻击

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-23 DOI: 10.1109/LCOMM.2025.3582157

Cheng Yin;Pei Xiao;Vishal Sharma;Zheng Chu;Emiliano Garcia-Palacios

引用次数: 0

Dynamic-Confined Iterative Guessing Codeword Decoding for Product Codes 产品码的动态受限迭代猜测码字解码

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-20 DOI: 10.1109/LCOMM.2025.3581563

Yile Peng;Shancheng Zhao

引用次数: 0

Latency Fairness for MEC-Enabled Cell-Free Massive MIMO: ICA- and AI-Based Approaches mec支持的无蜂窝大规模MIMO的延迟公平性：基于ICA和ai的方法

IF 4.4 3区计算机科学

IEEE Communications Letters Pub Date : 2025-06-20 DOI: 10.1109/LCOMM.2025.3581730

Hieu V. Nguyen;Van-Phuc Bui;Mai T. P. Le;Vien Nguyen-Duy-Nhat;Hung Nguyen-Le;Nghi H. Tran

引用次数: 0