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IEEE Communications Society Information IEEE通信学会信息
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2025-03-21 DOI: 10.1109/TGCN.2025.3570064
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引用次数: 0
IEEE Transactions on Green Communications and Networking IEEE绿色通信与网络学报
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2025-03-21 DOI: 10.1109/TGCN.2025.3570062
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引用次数: 0
IEEE Transactions on Green Communications and Networking IEEE绿色通信与网络学报
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2025-02-13 DOI: 10.1109/TGCN.2025.3539796
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引用次数: 0
IEEE Communications Society Information IEEE通信学会信息
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2025-02-13 DOI: 10.1109/TGCN.2025.3539798
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引用次数: 0
2024 Index IEEE Transactions on Green Communications and Networking Vol. 8 2024 Index IEEE Transactions on Green Communications and Networking Vol.
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-11-26 DOI: 10.1109/TGCN.2024.3506153
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引用次数: 0
Guest Editorial Special Issue on Rate-Splitting Multiple Access for Future Green Communication Networks 特邀编辑特刊：未来绿色通信网络的速率分割多重接入
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-11-19 DOI: 10.1109/TGCN.2024.3454935
Yijie Mao;Bruno Clerckx;Derrick Wing Kwan Ng;Wolfgang Utschick;Ying Cui;Timothy N. Davidson
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引用次数: 0
IEEE Transactions on Green Communications and Networking 电气和电子工程师学会绿色通信与网络论文集
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-11-19 DOI: 10.1109/TGCN.2024.3494575
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引用次数: 0
IEEE Communications Society Information IEEE 通信学会信息
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-11-19 DOI: 10.1109/TGCN.2024.3494577
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TGCN.2024.3494577","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3494577","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Green Differentially Private Coded Distributed Learning Over Near-Field MIMO Systems 近场MIMO系统的绿色差分私有编码分布式学习
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-11-12 DOI: 10.1109/TGCN.2024.3496556
Yilei Xue;Jun Wu;Jianhua Li;Shahid Mumtaz;Bolin Liao
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引用次数: 0
STAR-RIS Assisted Secrecy Communication With Deep Reinforcement Learning STAR-RIS辅助保密通信与深度强化学习
IF 5.3 2区 计算机科学
IEEE Transactions on Green Communications and Networking Pub Date : 2024-09-23 DOI: 10.1109/TGCN.2024.3466189
Miao Zhang;Xuran Ding;Yanqun Tang;Shixun Wu;Kai Xu
{"title":"STAR-RIS Assisted Secrecy Communication With Deep Reinforcement Learning","authors":"Miao Zhang;Xuran Ding;Yanqun Tang;Shixun Wu;Kai Xu","doi":"10.1109/TGCN.2024.3466189","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3466189","url":null,"abstract":"In this paper, we investigate the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted down link multiple-input single-output (MISO) wireless network. The secrecy rate is maximized by the joint design of the transmit beamforming, the transmission and reflection coefficients of the STAR-RIS, while satisfying the electromagnetic property of the STAR-RIS and transmit power limit of the base station. Since this communication network is in a dynamic environment, the optimization problem is non-convex and mathematically difficult to solve. To address this issue, two deep reinforcement learning (DRL)-based algorithms, namely soft actor-critic (SAC) algorithm and soft actor-critic based on loss-adjusted approximate actor prioritized experience replay (L3APER-SAC) are proposed to obtain the maximum reward by constantly interacting with and learning from the dynamic environment. Moreover, for the L3APER-SAC algorithm, to achieve higher performance and stability, we introduce two experience replay buffers—one is regular experience replay and the other is prioritized experience replay. Simulation results comprehensively assess the performance of two DRL algorithms and indicate that both proposed algorithms outperform benchmark approaches. Particularly, L3APER-SAC, exhibits superior performance, albeit with an associated increase in computational complexity.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 2","pages":"739-753"},"PeriodicalIF":5.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
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