Multiple Access Techniques for Intelligent and Multifunctional 6G: Tutorial, Survey, and Outlook

被引:22
作者
Clerckx, Bruno [1 ]
Mao, Yijie [2 ]
Yang, Zhaohui [3 ]
Chen, Mingzhe [4 ,5 ]
Alkhateeb, Ahmed [6 ]
Liu, Liang [7 ]
Qiu, Min [8 ]
Yuan, Jinhong [8 ]
Wong, Vincent W. S. [9 ]
Montojo, Juan [10 ]
机构
[1] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England
[2] ShanghaiTech Univ, Sch Informat Sci & Technologyg, Shanghai 201210, Peoples R China
[3] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China
[4] Arizona State Univ, Dept Elect & Comp Engn, Coral Gables, FL 33146 USA
[5] Arizona State Univ, Frost Inst Data Sci & Comp, Coral Gables, FL 33146 USA
[6] Univ Miami, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA
[7] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong, Peoples R China
[8] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
[9] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1ZA, Canada
[10] Qualcomm Inc, Corp Res & Dev Grp, San Diego, CA 92121 USA
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会; 英国科研创新办公室; 澳大利亚研究理事会;
关键词
Sensors; Artificial intelligence; Wireless networks; Multiaccess communication; 6G mobile communication; NOMA; Wireless sensor networks; Augmented reality; Integrated sensing and communication; Internet of Things; Machine learning; Semantics; Space division multiplexing; 6G; artificial intelligence (AI); augmented reality (AR); code-domain multiple access (CD-MA); integrated sensing and communications (ISACs); Internet of Things (IoT); machine learning (ML); multiple access (MA); nonorthogonal multiple access (NOMA); orthogonal multiple access (OMA); rate-splitting multiple access (RSMA); reconfigurable intelligent surfaces (RISs); semantic communications (SeComs); space-division multiple access (SDMA); universal multiple access (UMA); MISO BROADCAST CHANNEL; COUPLING DATA-TRANSMISSION; DEEP LEARNING FRAMEWORK; UNSOURCED RANDOM-ACCESS; GRAPH NEURAL-NETWORKS; SUM-RATE MAXIMIZATION; MASSIVE MIMO; REFLECTING SURFACE; WIRELESS NETWORKS; RESOURCE-ALLOCATION;
D O I
10.1109/JPROC.2024.3409428
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.
引用
收藏
页码:832 / 879
页数:48
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