URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing

TL;DR

This paper proposes a novel resource allocation policy for heterogeneous vehicular edge computing that integrates multiple communication technologies to meet URLLC requirements, using a Lyapunov-guided deep reinforcement learning approach.

Contribution

It introduces a new optimization framework combining stochastic network calculus and deep reinforcement learning for URLLC-aware resource allocation in VEC.

Findings

The proposed approach effectively minimizes system utility while satisfying URLLC constraints.

Simulation results demonstrate the method's superior performance over traditional strategies.

The framework adapts to multiple communication technologies and task types in VEC.

Abstract

Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing. However, the traditional VEC that relies on single communication technology cannot well meet the communication requirement for task offloading, thus the heterogeneous VEC integrating the advantages of dedicated short-range communications (DSRC), millimeter-wave (mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced to enhance the communication capacity. The communication resource allocation and computation resource allocation may significantly impact on the ultra-reliable low-latency communication (URLLC) performance and the VEC system utility, in this case, how to do the resource allocations is becoming necessary. In this paper, we consider a heterogeneous VEC with multiple communication technologies and various types of tasks, and propose an effective resource allocation policy to minimize the system utility while satisfying the URLLC requirement. We first formulate an optimization problem to minimize the system utility under the URLLC constraint which modeled by the moment generating function (MGF)-based stochastic network calculus (SNC), then we present a Lyapunov-guided deep reinforcement learning (DRL) method to convert and solve the optimization problem. Extensive simulation experiments illustrate that the proposed resource allocation approach is effective.