Accelerating Edge Intelligence via Integrated Sensing and Communication

Tong Zhang1; Shuai Wang2; Guoliang Li1; Fan Liu1; Guangxu Zhu3; Rui Wang1

10.1109/ICC45855.2022.9839016

2022

IEEE International Conference on Communications (ICC)

1550-3607

978-1-5386-8348-4

ICC 2022 - IEEE International Conference on Communications

2022-May

1586-1592

16-20 May 2022

Seoul, Korea, Republic of

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and up-loading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.

Beamforming edge intelligence integrated sensing and communication (ISAC)

第一

英语

EI ; CPCI-S

Guangdong Youth Innovative Talent Project[2020KQNCX063]

Telecommunications

Telecommunications

WOS:000864709901151

20223712710875

Electromagnetic Waves in Relation to Various Structures:711.2

IEEE

1.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
2.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3.Shenzhen Research Institute of Big Data, Shenzhen, China

Tong Zhang,Shuai Wang,Guoliang Li,et al. Accelerating Edge Intelligence via Integrated Sensing and Communication[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1586-1592.