Task-oriented integrated sensing, computation and communication for wireless edge AI

Xing, H., Zhu, G., Liu, D. , Wen, H., Huang, K. and Wu, K. (2023) Task-oriented integrated sensing, computation and communication for wireless edge AI. IEEE Network, 37(4), pp. 135-144. (doi: 10.1109/MNET.011.2300046)

Abstract

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 (SC 2 ) 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.

• Item Type: Articles
• Additional Information: This work was supported in part by the Guangzhou Municipal Science and Technology Project under Grants 2023A03J0663 and 2023A03J0011, in part by the National Natural Science Foundation of China under Grant 62001310, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515010109, and in part by the Internal Project Fund from Shenzhen Research Institute of Big Data under Grant J00120230001.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Dongzhu
• Authors: Xing, H., Zhu, G., Liu, D., Wen, H., Huang, K., and Wu, K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Network
• Publisher: IEEE
• ISSN: 0890-8044
• ISSN (Online): 1558-156X
• Published Online: 24 October 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Network 37(4):135-144
• Publisher Policy: Reproduced in accordance with the publisher copyright policy