IEEE Communications Surveys and Tutorials最新文献

Editorial Third Bi-Monthly 2025 IEEE Communications Surveys and Tutorials 编辑第三双月刊2025 IEEE通信调查和教程

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2025-06-12 DOI: 10.1109/COMST.2025.3572237

Trung Q. Duong

引用次数: 0

Editorial Second Bi-Monthly 2025 IEEE Communications Surveys & Tutorials 编辑第二双月刊2025年IEEE通信调查和教程

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2025-03-01 DOI: 10.1109/COMST.2025.3560017

Trung Q. Duong

引用次数: 0

Editorial First Bi-Monthly 2025 IEEE Communications Surveys and Tutorials 编辑第一双月刊2025年IEEE通信调查和教程

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2025-02-14 DOI: 10.1109/COMST.2025.3534456

Trung Q. Duong

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Editorial: Fourth Quarter 2024 IEEE Communications Surveys and Tutorials 编辑：2024 年第 4 季度《IEEE 通信概览与教程

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-11-21 DOI: 10.1109/COMST.2024.3464708

Dusit Niyato

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Game Theory and Reinforcement Learning for Anti-Jamming Defense in Wireless Communications: Current Research, Challenges, and Solutions 博弈论和强化学习用于无线通信中的抗干扰防御：当前研究、挑战和解决方案

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-24 DOI: 10.1109/COMST.2024.3482973

Luliang Jia;Nan Qi;Zhe Su;Feihuang Chu;Shengliang Fang;Kai-Kit Wong;Chan-Byoung Chae

{"title":"Game Theory and Reinforcement Learning for Anti-Jamming Defense in Wireless Communications: Current Research, Challenges, and Solutions","authors":"Luliang Jia;Nan Qi;Zhe Su;Feihuang Chu;Shengliang Fang;Kai-Kit Wong;Chan-Byoung Chae","doi":"10.1109/COMST.2024.3482973","DOIUrl":"10.1109/COMST.2024.3482973","url":null,"abstract":"Due to the inherently open and shared nature of the wireless channels, wireless communication networks are vulnerable to jamming attacks, and effective anti-jamming measures are of utmost importance to realize reliable communications. Game theory and reinforcement learning (RL) are powerful mathematical tools in anti-jamming field. This article investigates the anti-jamming problem from the perspective of game theory and RL. First, different anti-jamming domains and anti-jamming strategies are discussed, and technological challenges are globally analyzed from different perspectives. Second, an in-depth systematic and comprehensive survey of each kind of anti-jamming solutions (i.e., game theory and RL) is presented. To be specific, some game models are discussed for game theory based solutions, including Bayesian anti-jamming game, Stackelberg anti-jamming game, stochastic anti-jamming game, zero-sum anti-jamming game, graphical/hypergraphical anti-jamming game, etc. For RL-based anti-jamming solutions, different kinds of RL are given, including Q-learning, multi-armed bandit, deep RL and transfer RL. Third, the strengths and limitations are analyzed for each type of anti-jamming solutions. Finally, we discuss the deep integration of the game theory and RL in solving anti-jamming problems, and a few future research directions are illustrated.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1798-1838"},"PeriodicalIF":34.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems 社会化学习：网络系统边缘智能范式转变概览

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-17 DOI: 10.1109/COMST.2024.3482978

Xiaofei Wang;Yunfeng Zhao;Chao Qiu;Qinghua Hu;Victor C. M. Leung

{"title":"Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems","authors":"Xiaofei Wang;Yunfeng Zhao;Chao Qiu;Qinghua Hu;Victor C. M. Leung","doi":"10.1109/COMST.2024.3482978","DOIUrl":"10.1109/COMST.2024.3482978","url":null,"abstract":"Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system’s adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"2085-2128"},"PeriodicalIF":34.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Near-Field Communications: A Comprehensive Survey 近场通信：全面调查

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-14 DOI: 10.1109/COMST.2024.3475884

Yuanwei Liu;Chongjun Ouyang;Zhaolin Wang;Jiaqi Xu;Xidong Mu;A. Lee Swindlehurst

{"title":"Near-Field Communications: A Comprehensive Survey","authors":"Yuanwei Liu;Chongjun Ouyang;Zhaolin Wang;Jiaqi Xu;Xidong Mu;A. Lee Swindlehurst","doi":"10.1109/COMST.2024.3475884","DOIUrl":"10.1109/COMST.2024.3475884","url":null,"abstract":"Multiple-antenna technologies are evolving towards larger aperture sizes, extremely high frequencies, and innovative antenna types. This evolution is fostering the emergence of near-field communications (NFC) in future wireless systems. Considerable attention has been directed towards this cutting-edge technology due to its potential to enhance the capacity of wireless networks by introducing increased spatial degrees of freedom (DoFs) in the range domain. Within this context, a comprehensive review of the state of the art on NFC is presented, with a specific focus on its i) fundamental operating principles, ii) channel modeling, iii) performance analysis, iv) signal processing techniques, and v) integration with other emerging applications. Specifically, i) the basic principles of NFC are characterized from both physics and communications perspectives, unveiling its unique properties in contrast to far-field communications. ii) Building on these principles, deterministic and stochastic near-field channel models are explored for spatially-discrete (SPD) and continuous-aperture (CAP) arrays. iii) Based on these models, existing contributions to near-field performance analysis are reviewed in terms of DoFs/effective DoFs (EDoFs), the power scaling law, and transmission rate. iv) Existing signal processing techniques for NFC are systematically surveyed, which include channel estimation, beamforming design, and low-complexity beam training. v) Major issues and research opportunities in incorporating near-field models into other promising technologies are identified to advance NFC’s deployment in next-generation networks. Throughout this paper, promising directions are highlighted to inspire future research endeavors in the realm of NFC, underscoring its significance in the advancement of wireless communication technologies.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1687-1728"},"PeriodicalIF":34.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey 语义通信中的资源管理、安全和隐私问题：调查

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-03 DOI: 10.1109/COMST.2024.3471685

Dongwook Won;Geeranuch Woraphonbenjakul;Ayalneh Bitew Wondmagegn;Anh-Tien Tran;Donghyun Lee;Demeke Shumeye Lakew;Sungrae Cho

引用次数: 0

A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges 室内可见光定位系统概览：基本原理、应用和挑战

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-01 DOI: 10.1109/COMST.2024.3471950

Zhiyu Zhu;Yang Yang;Mingzhe Chen;Caili Guo;Julian Cheng;Shuguang Cui

{"title":"A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges","authors":"Zhiyu Zhu;Yang Yang;Mingzhe Chen;Caili Guo;Julian Cheng;Shuguang Cui","doi":"10.1109/COMST.2024.3471950","DOIUrl":"10.1109/COMST.2024.3471950","url":null,"abstract":"The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1656-1686"},"PeriodicalIF":34.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Trajectory-Prediction Techniques for Unmanned Aerial Vehicles (UAVs): A Comprehensive Survey 无人驾驶飞行器（UAV）的轨迹预测技术：全面调查

IF 34.4 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2024-10-01 DOI: 10.1109/COMST.2024.3471671

Pushpak Shukla;Shailendra Shukla;Amit Kumar Singh

{"title":"Trajectory-Prediction Techniques for Unmanned Aerial Vehicles (UAVs): A Comprehensive Survey","authors":"Pushpak Shukla;Shailendra Shukla;Amit Kumar Singh","doi":"10.1109/COMST.2024.3471671","DOIUrl":"10.1109/COMST.2024.3471671","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse sectors, ranging from environmental monitoring, infrastructure inspection, disaster response, wildlife conservation, surveillance, and reconnaissance missions. It is crucial to predict their future states to enable UAVs’ safe and efficient operation in dynamic environments. UAV trajectory planning is a crucial aspect of UAV operations, as it determines how the drone will navigate, perform tasks, and avoid obstacles. UAVs can be operated with varying degrees of autonomy, and they can be controlled by humans or autonomously via onboard autopilot software. While existing research has extensively focused on trajectory planning methodologies for UAVs, there is a noticeable gap in the literature concerning the integration of predictive capabilities into trajectory planning, highlighting the need for a comprehensive review of methodologies in UAV trajectory prediction connected with the associated realm of trajectory planning. This article provides a comprehensive and comparative analysis of trajectory prediction methods tailored for autonomous UAVs. Beginning with a precise problem definition and algorithm categorization, our study delves into evaluating methodologies rooted in conventional mathematical models, classical machine learning, deep learning, and reinforcement learning models.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1867-1910"},"PeriodicalIF":34.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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