Intelligent Task Offloading for Heterogeneous V2X Communications

TL;DR

This paper presents an intelligent task offloading framework using federated Q-learning in heterogeneous V2X networks, improving delay, resource efficiency, and reducing failure probabilities for autonomous driving tasks.

Contribution

It introduces a novel offloading framework with stochastic delay bounds and a federated Q-learning approach for resource optimization in heterogeneous vehicular networks.

Findings

Significant reduction in offloading failure probability.

Improved resource utilization efficiency.

Enhanced delay performance compared to existing methods.

Abstract

With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this paper, we propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this paper firstly derives the delay upper bound of different offloading technologies with a certain failure probability. Moreover, we propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that our proposed algorithm can significantly outperform the existing algorithms in terms of offloading failure probability and resource cost.