Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI

With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical latency in beyond 5G (B5G) networks, edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge. However, most existing design frameworks separate these designs incurring unnecessary signaling overhead and waste of energy, and it is therefore of paramount importance to advance fully integrated sensing, computation and communication (ISCC) to achieve ultra-reliable and low-latency edge intelligence acquisition. In this article, we provide an overview of principles of enabling ISCC technologies followed by two concrete use cases of edge AI tasks demonstrating the advantage of task-oriented ISCC, and pointed out some practical challenges in edge AI design with advanced ISCC solutions. H. Xing and H. Wen are with the Internet of Things (IoT) Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China. H. Xing is also affiliated with the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong (e-mails: hongxing@ust.hk, D. Liu is with the School of Computing Science, University of Glasgow, Glasgow G12 8RZ, United Kingdom (e-mail: dongzhu.liu@glasgow.ac.uk). K. Huang is with the Department of Electrical and Electronic Engineering (EEE), The University of Hong Kong, Hong Kong (e-mail: huangkb@eee.hku.hk).