Latency Minimization for MEC-V2X Assisted Autonomous Vehicles Task Offloading

Abstract

Delay-sensitive applications for autonomous vehicles (AVs) require a substantial amount of computational resources. However, the onboard computation resources may be insufficient, resulting in long processing latencies. To deal with this critical issue, we jointly consider roadside unit (RSU) and assistant vehicle offloading, along with resource allocation, to minimize latency for vehicular tasks. This approach also takes into account frequency reuse among sub-areas for assistant vehicle offloading. The latency minimization problem can be formulated as a mixed-integer non-linear programming (MINLP) problem. Given the inherent complexity of the MINLP problem, we propose a two-step solution. The first step focuses on the combined decision of assistant vehicle offloading and transmit power allocation. To solve this problem, we propose a particle swarm optimization (PSO) algorithm with low complexity and low average transmit power. The second step deals with RSU offloading/local computation decision, bandwidth allocation, and computation resource allocation. An iterative algorithm is proposed to achieve the optimal solution. Without adding additional computation resources, simulation results demonstrate that the proposed vehicular task offloading approach improves overall delay performance than the adaptive MEC offloading scheme and the pure MEC computing scheme.