Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

被引:83
作者
Zhu, Guangxu [1 ]
Lyu, Zhonghao [1 ,2 ,3 ]
Jiao, Xiang [1 ,4 ]
Liu, Peixi [1 ,4 ]
Chen, Mingzhe [5 ,6 ]
Xu, Jie [2 ,3 ]
Cui, Shuguang [1 ,2 ,3 ,8 ]
Zhang, Ping [7 ,8 ]
机构
[1] Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
[2] Chinese Univ Hong Kong Shenzhen, Future Network Intelligence Inst FNii, Shenzhen 518172, Peoples R China
[3] Chinese Univ Hong Kong Shenzhen, Sch Sci & Engn SSE, Shenzhen 518172, Peoples R China
[4] Peking Univ, Sch Elect, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China
[5] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33146 USA
[6] Univ Miami, Inst Data Sci & Comp, Coral Gables, FL 33146 USA
[7] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[8] Peng Cheng Lab, Shenzhen 518066, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
sixth-generation (6G); edge intelligence; artificial intelligence of things (AIoT); integrated sensing; communication; and computation (ISCC); RECONFIGURABLE INTELLIGENT SURFACE; RESOURCE-ALLOCATION; POWER-CONTROL; JOINT RADAR; SYSTEMS; OPTIMIZATION; DESIGN; TRANSMISSION; CONVERGENCE; ALGORITHM;
D O I
10.1007/s11432-022-3652-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.
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页数:19
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