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With the advent of emerging IoT applications such as autonomous driving,digital-twin and metaverse etc. featuring massive data sensing, analyzing andinference as well critical latency in beyond 5G (B5G) networks, edge artificialintelligence (AI) has been proposed to provide high-performance computation ofa conventional cloud down to the network edge. Recently, convergence ofwireless sensing, computation and communication (SC${}^2$) for specific edge AItasks, has aroused paradigm shift by enabling (partial) sharing of theradio-frequency (RF) transceivers and information processing pipelines amongthese three fundamental functionalities of IoT. However, most existing designframeworks separate these designs incurring unnecessary signaling overhead andwaste of energy, and it is therefore of paramount importance to advance fullyintegrated sensing, computation and communication (ISCC) to achieveultra-reliable and low-latency edge intelligence acquisition. In this article,we provide an overview of principles of enabling ISCC technologies followed bytwo concrete use cases of edge AI tasks demonstrating the advantage oftask-oriented ISCC, and pointed out some practical challenges in edge AI designwith advanced ISCC solutions.

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