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Vehicular edge computing (VEC) is a promising technology to support real-timevehicular applications, where vehicles offload intensive computation tasks tothe nearby VEC server for processing. However, the traditional VEC that relieson single communication technology cannot well meet the communicationrequirement for task offloading, thus the heterogeneous VEC integrating theadvantages of dedicated short-range communications (DSRC), millimeter-wave(mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced toenhance the communication capacity. The communication resource allocation andcomputation resource allocation may significantly impact on the ultra-reliablelow-latency communication (URLLC) performance and the VEC system utility, inthis case, how to do the resource allocations is becoming necessary. In thispaper, we consider a heterogeneous VEC with multiple communication technologiesand various types of tasks, and propose an effective resource allocation policyto minimize the system utility while satisfying the URLLC requirement. We firstformulate an optimization problem to minimize the system utility under theURLLC constraint which modeled by the moment generating function (MGF)-basedstochastic network calculus (SNC), then we present a Lyapunov-guided deepreinforcement learning (DRL) method to convert and solve the optimizationproblem. Extensive simulation experiments illustrate that the proposed resourceallocation approach is effective.

URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing