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

With the advent of emerging IoT applications, such as autonomous driving, digital-twin, metaverse, etc., featuring massive data sensing, analyzing, inference, and 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. Recently, the convergence of wireless sensing, computation, and communication (SC²) for specific edge AI tasks, has aroused a paradigm shift by enabling (partial) sharing of the radio-frequency (RF) transceivers and information processing pipelines among these three fundamental functionalities of IoT. 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 that demonstrate the advantage of task-oriented ISCC, and point out some practical challenges in edge AI design with advanced ISCC solutions.