IEEE Communications Surveys and Tutorials最新文献

{"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}

{"title":"Reinforcement-Learning-Based Intrusion Detection in Communication Networks: A Review","authors":"Hamza Kheddar;Diana W. Dawoud;Ali Ismail Awad;Yassine Himeur;Muhammad Khurram Khan","doi":"10.1109/COMST.2024.3484491","DOIUrl":"10.1109/COMST.2024.3484491","url":null,"abstract":"Modern communication networks have to meet the performance requirements of contemporary industrial control systems (ICSs), which are increasingly being connected to the external Internet. This connectivity exposes them to vulnerabilities that necessitate timely and effective protection measures. The integration of intrusion-detection systems (IDSs) into communication networks serves as a preventive mechanism to defend against malicious threats and hostile activities, ensuring secure operations within the broader industrial infrastructure. This review explores the cutting-edge artificial-intelligence techniques that are employed in the development of IDSs for diverse industrial control networks, emphasizing the application of deep reinforcement learning (DRL) within IDS-based systems across various communication networks. DRL has been successful in solving complex sequential decision-making problems in various domains, including robotics, game playing, and natural-language processing. The review examines a broad scope of publications, and these are categorized into three groups: DRL-only and IDS-only in the introduction and background, and DRL-based IDS papers in the core section of the review. This seeks to provide researchers with an overview of the current state of DRL approaches in IDSs for various network types. Through a meticulous comparative analysis with existing surveys, our review stands out, emphasizing its uniqueness and comprehensiveness. This inclusivity extends beyond traditional boundaries, encompassing a wide array of IDS techniques and environments, ranging from the Internet of Things to ICSs, smart grids, and other domains. Additionally, this review provides useful information such as the datasets used, types of DRL employed, pretrained networks, IDS techniques, evaluation metrics, and improvements gained. Furthermore, the algorithms and methods used in several studies are presented to illustrate the principles of each DRL-based IDS subcategory clearly and in depth. A detailed taxonomy is presented, providing nuanced insights into diverse applications with a triple focus on IDSs, deep-learning, and DRL techniques, which makes this review unique.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 4","pages":"2420-2469"},"PeriodicalIF":34.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10729241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"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}

{"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}

{"title":"Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey","authors":"Dongwook Won;Geeranuch Woraphonbenjakul;Ayalneh Bitew Wondmagegn;Anh-Tien Tran;Donghyun Lee;Demeke Shumeye Lakew;Sungrae Cho","doi":"10.1109/COMST.2024.3471685","DOIUrl":"10.1109/COMST.2024.3471685","url":null,"abstract":"Resource management, security, and privacy stand as fundamental pillars for the reliable and secure operation of efficient semantic communications (SC) system. By addressing these aspects, SC system can pave the way for efficient resource utilization, improved network efficiency, enhanced communication performance, and protection of sensitive information. In this study, we begin by presenting the background of SC and reviewing several existing studies in this field. Subsequently, we provide a comprehensive and exhaustive survey of resource management, security, and privacy in SC. We identify and highlight existing challenges and open research challenges related to resource management, security, and privacy in SC in order to spur further investigation in these areas.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1758-1797"},"PeriodicalIF":34.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"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}

{"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}

{"title":"Blind Carrier Frequency Offset Estimation Techniques for Next-Generation Multicarrier Communication Systems: Challenges, Comparative Analysis, and Future Prospects","authors":"Shivani Singh;Sushant Kumar;Sudhan Majhi;Udit Satija;Chau Yuen","doi":"10.1109/COMST.2024.3472109","DOIUrl":"10.1109/COMST.2024.3472109","url":null,"abstract":"Frequency synchronization is essential to achieving the intended performance for single and multicarrier wireless systems. Blind techniques, which don’t require prior channel knowledge or pilot symbols, are crucial for dynamic environments where self-adaptive synchronization is needed. A key objective of this paper is to provide the readers as well as the industry’s professionals with a comprehensive understanding of the carrier frequency offset (CFO) problem in multicarrier communication systems like orthogonal frequency division multiplexing (OFDM), single carrier-frequency division multiple access (SC-FDMA), multiple input multiple output (MIMO)-OFDM, and MIMO-SC-FDMA. These waveforms are used in today’s and future wireless communication systems such as wireless-fidelity (Wi-Fi), fifth-generation, and sixth-generation. Moreover, this paper also develops a taxonomy of the available solutions to address the CFO issue. We study blind techniques for CFO estimation presented in the recent literature and give potential future directions. We summarize various statistical methods and deep learning algorithms for CFO estimation and emphasize their advantages and limitations. We also incorporate the CFO impact on next-generation wireless systems such as orthogonal time frequency space and reconfigurable intelligent surface-assisted communication systems and provide a broader and deeper knowledge of the area. We provide simulation results of some existing estimators and their performance comparison in terms of mean square error for better understanding. Therefore, this paper is perfectly adapted to provide a comprehensive information source on blind CFO estimation techniques.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 1","pages":"1-36"},"PeriodicalIF":34.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362839","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}

{"title":"Resource Management in RIS-Assisted Rate Splitting Multiple Access for Next Generation (xG) Wireless Communications: Models, State-of-the-Art, and Future Directions","authors":"Ibrahim Aboumahmoud;Ekram Hossain;Amine Mezghani","doi":"10.1109/COMST.2024.3471453","DOIUrl":"10.1109/COMST.2024.3471453","url":null,"abstract":"Next Generation (xG) wireless networks require more stringent performance levels. New technologies such as Reconfigurable Intelligent Surfaces (RISs) and Rate Splitting Multiple Access (RSMA) are candidates for meeting some of the performance requirements, including higher user rates at reduced costs. RSMA provides a new way of mixing the messages of multiple users, and the RIS provides a controllable wireless environment. This paper provides a comprehensive survey on the various aspects of the synergy between RISs and RSMA for xG wireless communications systems. In particular, the paper studies more than 60 articles considering over 20 different system models where the RIS-aided RSMA system shows performance advantage (in terms of sum-rate or outage probability) over traditional RSMA models. These models include reflective RIS, Simultaneously Transmitting and Reflecting (STAR)-RIS, as well as transmissive surfaces. The state-of-the-art resource management methods for RIS-assisted RSMA communications employ traditional optimization techniques and/or machine learning techniques. We outline major research challenges and multiple future research directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1618-1655"},"PeriodicalIF":34.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360471","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}

{"title":"Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities","authors":"Hao Zhou;Chengming Hu;Ye Yuan;Yufei Cui;Yili Jin;Can Chen;Haolun Wu;Dun Yuan;Li Jiang;Di Wu;Xue Liu;Jianzhong Zhang;Xianbin Wang;Jiangchuan Liu","doi":"10.1109/COMST.2024.3465447","DOIUrl":"10.1109/COMST.2024.3465447","url":null,"abstract":"Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising opportunities to automate many tasks in the telecommunication (telecom) field. After pre-training and fine-tuning, LLMs can perform diverse downstream tasks based on human instructions, paving the way to artificial general intelligence (AGI)-enabled 6G. Given the great potential of LLM technologies, this work aims to provide a comprehensive overview of LLM-enabled telecom networks. In particular, we first present LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment. Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems. Specifically, the LLM-enabled generation applications include telecom domain knowledge, code, and network configuration generation. After that, the LLM-based classification applications involve network security, text, image, and traffic classification problems. Moreover, multiple LLM-enabled optimization techniques are introduced, such as automated reward function design for reinforcement learning and verbal reinforcement learning. Furthermore, for LLM-aided prediction problems, we discussed time-series prediction models and multi-modality prediction problems for telecom. Finally, we highlight the challenges and identify the future directions of LLM-enabled telecom networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 3","pages":"1955-2005"},"PeriodicalIF":34.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313819","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}