Task-Oriented Integrated Sensing, Computation and Communication for
  Wireless Edge AI
Hong Xing Guangxu Zhu Dongzhu Liu Haifeng Wen Kaibin Huang Kaishun Wu
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. Recently, convergence of
wireless sensing, computation and communication (SC${}^2$) for specific edge AI
tasks, has aroused 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 demonstrating the advantage of
task-oriented ISCC, and pointed out some practical challenges in edge AI design
with advanced ISCC solutions.