Deep reinforcement learning-based URLLC-aware task offloading in collaborative vehicular networks

Abstract

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and low-latency communications (URLLC) requirements. A user vehicle (UV) dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing, incomplete information, and dimensionality curse. In this paper, we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs. Then, a Deep Reinforcement lEarning-based URLLC-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, queuing delay, and URLLC.