Unlicensed assisted transmission in vehicular edge computing networks

EURASIP Journal on Advances in Signal Processing

Celimuge Wu¹  Rui Yin²  Xiao Lu³  Zhongbao Ji⁴ 

[1] Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan;School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China;School of Information and Electronic Engineering, Zhejiang University, Hangzhou, China;School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, China;

关键词: Mobile edge computing;    Vehicular edge computing;    Task offloading;    Resource allocation;    Unlicensed channels;   

DOI  :  10.1186/s13634-022-00840-z

来源: Springer

【 摘 要 】

Recently, the emerging computation-intensive in-vehicle applications and the exponentially growing data have posed a serious challenge to resource-limited in-vehicle devices. By integrating the mobile edge computing (MEC) into the vehicle networks, vehicular edge computing (VEC) is envisioned as a promising solution. In this paper, we divide the VEC system into small time slots, which effectively simulates dense small-scale computing scenarios in the rapidly varying channel environment over time. Furthermore, to alleviate the shortage of spectrum resources, vehicles can offload part of computing tasks to the edge server not only through licensed channels but also through unlicensed channels. However, dynamic topology changes and complex communication characteristics of the vehicle networks lead to the strategies of task offloading and resource allocation being difficult to obtain. By decoupling the non-convex optimizing problem and splitting it into multiple sub-problems, we design a joint power, spectrum and computing resource allocation strategy with low complexity, in order to minimize the total energy consumption of all vehicles for processing tasks. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme that employing licensed and unlicensed access in VEC networks can reduce the energy consumption of vehicles for processing tasks in comparison with baseline schemes.

【 授权许可】

CC BY