IEEE Communications Surveys and Tutorials最新文献_第8页

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues 无线通信机器学习模型的领域泛化:概念、最新技术和开放问题

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-10-20 DOI: 10.1109/COMST.2023.3326399

Mohamed Akrout;Amal Feriani;Faouzi Bellili;Amine Mezghani;Ekram Hossain

{"title":"Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues","authors":"Mohamed Akrout;Amal Feriani;Faouzi Bellili;Amine Mezghani;Ekram Hossain","doi":"10.1109/COMST.2023.3326399","DOIUrl":"10.1109/COMST.2023.3326399","url":null,"abstract":"Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generation wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"3014-3037"},"PeriodicalIF":35.6,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784599","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: 2

A Survey and Comparison of Post-Quantum and Quantum Blockchains 后量子区块链与量子区块链的调查与比较

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-10-19 DOI: 10.1109/COMST.2023.3325761

Zebo Yang;Haneen Alfauri;Behrooz Farkiani;Raj Jain;Roberto Di Pietro;Aiman Erbad

{"title":"A Survey and Comparison of Post-Quantum and Quantum Blockchains","authors":"Zebo Yang;Haneen Alfauri;Behrooz Farkiani;Raj Jain;Roberto Di Pietro;Aiman Erbad","doi":"10.1109/COMST.2023.3325761","DOIUrl":"10.1109/COMST.2023.3325761","url":null,"abstract":"Blockchains have gained substantial attention from academia and industry for their ability to facilitate decentralized trust and communications. However, the rapid progress of quantum computing poses a significant threat to the security of existing blockchain technologies. Notably, the emergence of Shor’s and Grover’s algorithms raises concerns regarding the compromise of the cryptographic systems underlying blockchains. Consequently, it is essential to develop methods that reinforce blockchain technology against quantum attacks. In response to this challenge, two distinct approaches have been proposed. The first approach involves post-quantum blockchains, which aim to utilize classical cryptographic algorithms resilient to quantum attacks. The second approach explores quantum blockchains, which leverage the power of quantum computers and networks to rebuild the foundations of blockchains. This paper aims to provide a comprehensive overview and comparison of post-quantum and quantum blockchains while exploring open questions and remaining challenges in these domains. It offers an in-depth introduction, examines differences in blockchain structure, security, privacy, and other key factors, and concludes by discussing current research trends.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 2","pages":"967-1002"},"PeriodicalIF":35.6,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10288193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135057264","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}

引用次数: 0

A Survey on X.509 Public-Key Infrastructure, Certificate Revocation, and Their Modern Implementation on Blockchain and Ledger Technologies X.509公钥基础设施、证书撤销及其在区块链和分类账技术上的现代实现的调查

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-10-13 DOI: 10.1109/COMST.2023.3323640

Salabat Khan;Fei Luo;Zijian Zhang;Farhan Ullah;Farhan Amin;Syed Furqan Qadri;Md Belal Bin Heyat;Rukhsana Ruby;Lu Wang;Shamsher Ullah;Meng Li;Victor C. M. Leung;Kaishun Wu

{"title":"A Survey on X.509 Public-Key Infrastructure, Certificate Revocation, and Their Modern Implementation on Blockchain and Ledger Technologies","authors":"Salabat Khan;Fei Luo;Zijian Zhang;Farhan Ullah;Farhan Amin;Syed Furqan Qadri;Md Belal Bin Heyat;Rukhsana Ruby;Lu Wang;Shamsher Ullah;Meng Li;Victor C. M. Leung;Kaishun Wu","doi":"10.1109/COMST.2023.3323640","DOIUrl":"10.1109/COMST.2023.3323640","url":null,"abstract":"Cyber-attacks are becoming more common against Internet users due to the increasing dependency on online communication in their daily lives. X.509 Public-Key Infrastructure (PKIX) is the most widely adopted and used system to secure online communications and digital identities. However, different attack vectors exist against the PKIX system, which attackers exploit to breach the security of the reliant protocols. Recently, various projects (e.g., Let’s Encrypt and Google Certificate Transparency) have been started to encrypt online communications, fix PKIX vulnerabilities, and guard Internet users against cyber-attacks. This survey focuses on classical PKIX proposals, certificate revocation proposals, and their implementation on blockchain as well as ledger technologies. First, we discuss the PKIX architecture, the history of the World Wide Web, the certificate issuance process, and possible attacks on the certificate issuance process. Second, a taxonomy of PKIX proposals, revocation proposals, and their modern implementation is provided. Then, a set of evaluation metrics is defined for comparison. Finally, the leading proposals are compared using 15 evaluation metrics and 13 cyber-attacks before presenting the lessons learned and suggesting future PKIX and revocation research.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"2529-2568"},"PeriodicalIF":35.6,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783336","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: 1

Toward Autonomous Multi-UAV Wireless Network: A Survey of Reinforcement Learning-Based Approaches 面向自主多无人机无线网络:基于强化学习的方法综述

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-10-12 DOI: 10.1109/COMST.2023.3323344

Yu Bai;Hui Zhao;Xin Zhang;Zheng Chang;Riku Jäntti;Kun Yang

{"title":"Toward Autonomous Multi-UAV Wireless Network: A Survey of Reinforcement Learning-Based Approaches","authors":"Yu Bai;Hui Zhao;Xin Zhang;Zheng Chang;Riku Jäntti;Kun Yang","doi":"10.1109/COMST.2023.3323344","DOIUrl":"10.1109/COMST.2023.3323344","url":null,"abstract":"Unmanned aerial vehicle (UAV)-based wireless networks have received increasing research interest in recent years and are gradually being utilized in various aspects of our society. The growing complexity of UAV applications such as disaster management, plant protection, and environment monitoring, has resulted in escalating and stringent requirements for UAV networks that a single UAV cannot fulfill. To address this, multi-UAV wireless networks (MUWNs) have emerged, offering enhanced resource-carrying capacity and enabling collaborative mission completion by multiple UAVs. However, the effective operation of MUWNs necessitates a higher level of autonomy and intelligence, particularly in decision-making and multi-objective optimization under diverse environmental conditions. Reinforcement Learning (RL), an intelligent and goal-oriented decision-making approach, has emerged as a promising solution for addressing the intricate tasks associated with MUWNs. As one may notice, the literature still lacks a comprehensive survey of recent advancements in RL-based MUWNs. Thus, this paper aims to bridge this gap by providing a comprehensive review of RL-based approaches in the context of autonomous MUWNs. We present an informative overview of RL and demonstrate its application within the framework of MUWNs. Specifically, we summarize various applications of RL in MUWNs, including data access, sensing and collection, resource allocation for wireless connectivity, UAV-assisted mobile edge computing, localization, trajectory planning, and network security. Furthermore, we identify and discuss several open challenges based on the insights gained from our review.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"3038-3067"},"PeriodicalIF":35.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783332","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}

引用次数: 1

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques 边缘视频分析:应用、系统和使能技术综述

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-10-10 DOI: 10.1109/COMST.2023.3323091

Renjie Xu;Saiedeh Razavi;Rong Zheng

{"title":"Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques","authors":"Renjie Xu;Saiedeh Razavi;Rong Zheng","doi":"10.1109/COMST.2023.3323091","DOIUrl":"10.1109/COMST.2023.3323091","url":null,"abstract":"Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. EVA systems and their enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"2951-2982"},"PeriodicalIF":35.6,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136206858","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

Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey 通过跨层优化实现资源高效的人工智能物联网系统：调查

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-09-27 DOI: 10.1109/COMST.2023.3319952

Sicong Liu;Bin Guo;Cheng Fang;Ziqi Wang;Shiyan Luo;Zimu Zhou;Zhiwen Yu

{"title":"Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey","authors":"Sicong Liu;Bin Guo;Cheng Fang;Ziqi Wang;Shiyan Luo;Zimu Zhou;Zhiwen Yu","doi":"10.1109/COMST.2023.3319952","DOIUrl":"10.1109/COMST.2023.3319952","url":null,"abstract":"The emerging field of artificial intelligence of things (AIoT, AI+IoT) is driven by the widespread use of intelligent infrastructures and the impressive success of deep learning (DL). With the deployment of DL on various intelligent infrastructures featuring rich sensors and weak DL computing capabilities, a diverse range of AIoT applications has become possible. However, DL models are notoriously resource-intensive. Existing research strives to realize near-/realtime inference of AIoT live data and low-cost training using AIoT datasets on resource-scare infrastructures. Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability. To this end, the algorithm-system co-design that jointly optimizes the resource-friendly DL models and model-adaptive system scheduling improves the runtime resource availability and thus pushes the performance boundary set by the standalone level. Unlike previous surveys on resource-friendly DL models or hand-crafted DL compilers/frameworks with partially fine-tuned components, this survey aims to provide a broader optimization space for more free resource-performance tradeoffs. The cross-level optimization landscape involves various granularity, including the DL model, computation graph, operator, memory schedule, and hardware instructor in both on-device and distributed paradigms. Furthermore, due to the dynamic nature of AIoT context, which includes heterogeneous hardware, agnostic sensing data, varying user-specified performance demands, and resource constraints, this survey explores the context-aware inter-/intra-device controllers for automatic cross-level adaptation. Additionally, we identify some potential directions for resource-efficient AIoT systems. By consolidating problems and techniques scattered over diverse levels, we aim to help readers understand their connections and stimulate further discussions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"389-427"},"PeriodicalIF":35.6,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135794555","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

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey 基于机器学习的通信系统和网络中的对抗性攻击和防御:当代调查

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-09-26 DOI: 10.1109/COMST.2023.3319492

Yulong Wang;Tong Sun;Shenghong Li;Xin Yuan;Wei Ni;Ekram Hossain;H. Vincent Poor

{"title":"Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey","authors":"Yulong Wang;Tong Sun;Shenghong Li;Xin Yuan;Wei Ni;Ekram Hossain;H. Vincent Poor","doi":"10.1109/COMST.2023.3319492","DOIUrl":"10.1109/COMST.2023.3319492","url":null,"abstract":"Adversarial attacks and defenses in machine learning and deep neural network (DNN) have been gaining significant attention due to the rapidly growing applications of deep learning in communication networks. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on DNN-based classification models for communication applications. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"2245-2298"},"PeriodicalIF":35.6,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784392","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

Models, Methods, and Solutions for Multicasting in 5G/6G mmWave and Sub-THz Systems 5G/6G 毫米波和 sub-THz 系统中的多播模型、方法和解决方案

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-09-26 DOI: 10.1109/COMST.2023.3319354

Nadezhda Chukhno;Olga Chukhno;Dmitri Moltchanov;Sara Pizzi;Anna Gaydamaka;Andrey Samuylov;Antonella Molinaro;Yevgeni Koucheryavy;Antonio Iera;Giuseppe Araniti

{"title":"Models, Methods, and Solutions for Multicasting in 5G/6G mmWave and Sub-THz Systems","authors":"Nadezhda Chukhno;Olga Chukhno;Dmitri Moltchanov;Sara Pizzi;Anna Gaydamaka;Andrey Samuylov;Antonella Molinaro;Yevgeni Koucheryavy;Antonio Iera;Giuseppe Araniti","doi":"10.1109/COMST.2023.3319354","DOIUrl":"10.1109/COMST.2023.3319354","url":null,"abstract":"Multicasting in wireless access networks is a functionality that, by leveraging group communications, turns out to be essential for reducing the amount of resources needed to serve users requesting the same content. The support of this functionality in the modern 5G New Radio (NR) and future sub-Terahertz (sub-THz) 6G systems faces critical challenges related to the utilization of massive antenna arrays forming directional radiation patterns, multi-beam functionality, and use of multiple Radio Access Technologys (RATs) having distinctively different coverage and technological specifics. As a result, optimal multicasting in these systems requires novel solutions. This article aims to provide an exhaustive treatment of performance optimization methods for 5G/6G mmWave/sub-THz systems and discuss the associated challenges and opportunities. We start by surveying 3rd Generation Partnership Project (3GPP) mechanisms to support multicasting at the NR radio interface and approaches to modeling the 5G/6G radio segment. Then, we illustrate optimal multicast solutions for different 5G NR deployments and antenna patterns, including single- and multi-beam antenna arrays and single- and multiple RAT deployments. Further, we survey new advanced functionalities for improving multicasting performance in 5G/6G systems, encompassing Reflective Intelligent Surfaces (RISs), NR-sidelink technology, and mobile edge enhancements, among many others. Finally, we outline perspectives of multicasting in future 6G networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"119-159"},"PeriodicalIF":35.6,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10263616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135750412","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}

引用次数: 0

Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions 结合联邦学习和边缘计算实现6G网络无处不在的智能:挑战、最新进展和未来方向

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-09-22 DOI: 10.1109/COMST.2023.3316615

Qiang Duan;Jun Huang;Shijing Hu;Ruijun Deng;Zhihui Lu;Shui Yu

{"title":"Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions","authors":"Qiang Duan;Jun Huang;Shijing Hu;Ruijun Deng;Zhihui Lu;Shui Yu","doi":"10.1109/COMST.2023.3316615","DOIUrl":"10.1109/COMST.2023.3316615","url":null,"abstract":"Full leverage of the huge volume of data generated on a large number of user devices for providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to developing UI lies in the involvement of the large number of network devices, which contribute their data to collaborative Machine Learning (ML) and provide their computational resources to support the learning process. Federated Learning (FL) is a new ML method that enables data owners to collaborate in model training without exposing private data, which allows user devices to contribute their data to developing UI. Edge computing deploys cloud-like capabilities at the network edge, which enables network devices to offer their computational resources for supporting FL. Therefore, a combination of FL and edge computing may greatly facilitate the development of ubiquitous intelligence in the 6G network. In this article, we present a comprehensive survey of the recent developments in technologies for combining FL and edge computing with a holistic vision across the fields of FL and edge computing. We conduct our survey from both the perspective of an FL framework deployed in an edge computing environment (FL in Edge) and the perspective of an edge computing system providing a platform for FL (Edge for FL). From the FL in Edge perspective, we first identify the main challenges to FL in edge computing and then survey the representative technical strategies for addressing the challenges. From the Edge for FL perspective, we first analyze the key requirements for edge computing to support FL and then review the recent advances in edge computing technologies that may be exploited to meet the requirements. Then we discuss open problems and identify some possible directions for future research on combining FL and edge computing, 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":"25 4","pages":"2892-2950"},"PeriodicalIF":35.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783298","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

A Comprehensive Survey on Full-Duplex Communication: Current Solutions, Future Trends, and Open Issues 全双工通信的综合调查:当前解决方案、未来趋势和开放问题

IF 35.6 1区计算机科学

IEEE Communications Surveys and Tutorials Pub Date : 2023-09-22 DOI: 10.1109/COMST.2023.3318198

Mohammadali Mohammadi;Zahra Mobini;Diluka Galappaththige;Chintha Tellambura

{"title":"A Comprehensive Survey on Full-Duplex Communication: Current Solutions, Future Trends, and Open Issues","authors":"Mohammadali Mohammadi;Zahra Mobini;Diluka Galappaththige;Chintha Tellambura","doi":"10.1109/COMST.2023.3318198","DOIUrl":"10.1109/COMST.2023.3318198","url":null,"abstract":"Full-duplex (FD) communication is a potential game changer for future wireless networks. It allows for simultaneous transmit and receive operations over the same frequency band, a doubling of the spectral efficiency. FD can also be a catalyst for supercharging other existing/emerging wireless technologies, including cooperative and cognitive communications, cellular networks, multiple-input multiple-output (MIMO), massive MIMO, non-orthogonal multiple access (NOMA), millimeter-wave (mmWave) communications, unmanned aerial vehicle (UAV)-aided communication, backscatter communication (BackCom), and reconfigurable intelligent surfaces (RISs). These integrated technologies can further improve spectral efficiency, enhance security, reduce latency, and boost the energy efficiency of future wireless networks. A comprehensive survey of such integration has thus far been lacking. This paper fills that need. Specifically, we first discuss the fundamentals, highlighting the FD transceiver structure and the self-interference (SI) cancellation techniques. Next, we discuss the coexistence of FD with the above-mentioned wireless technologies. We also provide case studies for some of the integration scenarios mentioned above and future research directions for each case. We further address the potential research directions, open challenges, and applications for future FD-assisted wireless, including cell-free massive MIMO, mmWave communications, UAV, BackCom, and RISs. Finally, potential applications and developments of other miscellaneous technologies, such as mixed radio-frequency/free-space optical, visible light communication, dual-functional radar-communication, underwater wireless communication, multi-user ultra-reliable low-latency communications, vehicle-to-everything communications, rate splitting multiple access, integrated sensing and communication, and age of information, are also highlighted.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"2190-2244"},"PeriodicalIF":35.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135599233","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