Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge. In this system, multiple ISAC devices perform radar sensing to obtain multi-view data, and then offload the quantized version of extracted features to a centralized edge server, which conducts model inference based on the cascaded feature vectors. Under this setup and by considering classification tasks, we measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain, which is defined as the distance of two classes in the…

• NS
  Natural Science Foundation of Shanghai

  Award: 21ZR1442700
• NN
  National Natural Science Foundation of China

  Awards: 92267202, U2001208, 62001310, 62271318, 62293482
• IS
  Israel Science Foundation

  Award: 536/22
• SF
  Shenzhen Fundamental Research Program

  Award: JCYJ20210324133405015
• SR
  Shenzhen Research Institute of Big Data

  Award: J00120230001
• ER
  European Research Council

  Award: 101000967
• SP
  Special Project for Research and Development in Key areas of Guangdong Province

  Award: 2018B030338001
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2022A1515010109