IEEE Wireless Communications Letters最新文献_第3页

A Target Parameters Estimation Method for CP-OTFS Based Terrestrial Radar System 基于 CP-OTFS 的地面雷达系统的目标参数估计方法

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-11-14 DOI: 10.1109/lwc.2024.3497959

Qilong Miao, Jing Liang, Ge Zhang, Jiachen Han

引用次数: 0

IEEE Wireless Communications Letters Publication Information IEEE Wireless Communications Letters 出版信息

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-11-07 DOI: 10.1109/LWC.2024.3485154

引用次数: 0

IEEE Communications Society Information IEEE 通信学会信息

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-11-07 DOI: 10.1109/LWC.2024.3485156

引用次数: 0

Corrections to “Deep Learning Enabled Multicast Beamforming With Movable Antenna Array” 对 "利用可移动天线阵列的深度学习多播波束成形 "的更正

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-11-07 DOI: 10.1109/LWC.2024.3464188

Jae-Mo Kang

引用次数: 0

IEEE Wireless Communications Letters Publication Information IEEE Wireless Communications Letters 出版信息

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-10-08 DOI: 10.1109/LWC.2024.3464171

引用次数: 0

IEEE Communications Society Information IEEE 通信学会信息

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-10-08 DOI: 10.1109/LWC.2024.3464173

引用次数: 0

Perfect UE Grouping for Frequency Sharing in Full Duplex Cellular Networks 全双工蜂窝网络中频率共享的完美 UE 分组

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-09-30 DOI: 10.1109/LWC.2024.3471248

Parthiban Annamalai;Pradeesha Ashok;Rajath Rao;Divyanshi Rajput;Jyotsna Bapat;Debabrata Das

引用次数: 0

Deep Deterministic Policy Gradient-Based Physical Layer Authentication Scheme Under Unknown Attacking Environment 未知攻击环境下基于深度确定性策略梯度的物理层验证方案

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-09-20 DOI: 10.1109/LWC.2024.3464858

Dichen Jiu;Yichen Wang;Moqi Liu;Julian Cheng

{"title":"Deep Deterministic Policy Gradient-Based Physical Layer Authentication Scheme Under Unknown Attacking Environment","authors":"Dichen Jiu;Yichen Wang;Moqi Liu;Julian Cheng","doi":"10.1109/LWC.2024.3464858","DOIUrl":"10.1109/LWC.2024.3464858","url":null,"abstract":"Physical layer authentication (PLA) is considered as a promising method to resist spoofing attacks, where the stochastic features of wireless channels are used to detect attackers. Most of the existing PLA schemes assume that the prior information of attackers is known by the receiver, which might not be realized in realistic networks. To address this issue, we propose a deep deterministic policy gradient based PLA (DPLA) scheme to identify legitimate transmitters and attackers under unknown attacking environment. Specifically, the deep deterministic policy gradient approach is employed in the proposed DPLA scheme, where two types of deep neural networks are used to adaptively adjust the authentication strategy in continuous action space by learning both the policy and the state-action value. Moreover, the double-Q approach and the delayed update policy network are integrated into the proposed scheme to reduce the overestimation bias in the value estimation process and ensure the stability of the policy learning. Simulation results show that the proposed scheme can achieve a substantial performance gain over several reference schemes.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"13 11","pages":"3247-3251"},"PeriodicalIF":4.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275581","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

Contrastive Language-Signal Prediction for Automatic Modulation Recognition 用于自动调制识别的对比语言信号预测

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-09-19 DOI: 10.1109/LWC.2024.3464232

Yunpeng Qu;Zhilin Lu;Bingyu Hui;Jintao Wang;Jian Wang

{"title":"Contrastive Language-Signal Prediction for Automatic Modulation Recognition","authors":"Yunpeng Qu;Zhilin Lu;Bingyu Hui;Jintao Wang;Jian Wang","doi":"10.1109/LWC.2024.3464232","DOIUrl":"10.1109/LWC.2024.3464232","url":null,"abstract":"Automatic Modulation Recognition (AMR) enables intelligent communication and is a critical component of wireless communication systems. Deep learning-based AMR approaches have made significant strides in recent years. These approaches involve inputting signals in the form of images or embeddings into a network, which maps them into high-dimensional feature vectors for subsequent classification. However, radio frequency (RF) signals exhibit significant differences within the same class due to noise or wireless channels. Performing classification based on high-dimensional features may be challenging in capturing robust discriminative features, thereby compromising the model’s generalization ability. To address this limitation, we introduce a novel framework named CLASP, which incorporates language models through contrastive learning, coupling AMR with human language priors to extract robust discriminative features between different categories. Additionally, we treat the prediction of SNR levels as a subtask to acquire auxiliary priors that represent the impact of noise. Extensive results on widely-used datasets demonstrate that CLASP achieves state-of-the-art (SOTA) performance compared to other baselines. As a framework, CLASP exhibits universality and demonstrates superior performance compared to the linear-probe approach across different backbones.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"13 11","pages":"3242-3246"},"PeriodicalIF":4.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275515","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

Imbalanced Domain Adaptation for Automatic Modulation Classification 用于自动调制分类的不平衡域自适应技术

IF 4.6 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-09-19 DOI: 10.1109/LWC.2024.3457741

Luyang Mei;Shuang Wang;Hantong Xing;Chenxu Wang;Yi Xu;Huaji Zhou

{"title":"Imbalanced Domain Adaptation for Automatic Modulation Classification","authors":"Luyang Mei;Shuang Wang;Hantong Xing;Chenxu Wang;Yi Xu;Huaji Zhou","doi":"10.1109/LWC.2024.3457741","DOIUrl":"10.1109/LWC.2024.3457741","url":null,"abstract":"In non-cooperative communication, complex channel conditions can cause data distribution shift (DDS), which can significantly degrade the performance of existing automatic modulation classification (AMC) models. Initial attempts have explored the use of unsupervised domain adaptation (UDA) to mitigate this issue. However, the considerations for these works are still limited as they assume that the label distribution is domain invariant. In real-world scenarios, significant variations in the usage frequencies of different modulation types often result in imbalanced data volumes and inconsistent label distributions. This challenge, known as label distribution shift (LDS), poses a substantial challenge for cross-domain alignment. To address this limitation, this letter considers AMC in imbalanced domain adaptation (IDA) scenarios, which handle the DDS and LDS simultaneously. We propose a novel method called Pseudo label-based Imbalanced Alignment (PIA). Specifically, our PIA leverages pseudo labels in the target domain to achieve implicit and explicit conditional feature alignment, through re-weighting self-training and centroid feature alignment, respectively. We construct various IDA scenarios for experiments using custom and public datasets, and the results prove the effectiveness and superiority of PIA.","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"13 11","pages":"3172-3176"},"PeriodicalIF":4.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275516","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