An MEC-Enabled Framework for Task Offloading and Power Allocation in NOMA Enhanced ABS-Assisted VANETs

Abstract

This letter investigates the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) in air base station (ABS)-assisted vehicular ad hoc networks (VANETs). A tethered ABS acts as the server to provide MEC services for vehicles. Vehicles adopt NOMA to offload their tasks to the ABS. For the considered network, we formulate an average task processing ratio maximization problem by jointly optimizing the offloading decision, computation resource allocation, and power allocation. For tackling this non-convex problem, we first obtain the offloading decision and the computation resource allocation with the aid of the relax-and-round method and the convex problem solver. Then, the optimal transmission power allocation of vehicles is derived by considering the successive interference cancellation decoding threshold. Finally, the simulation results show that the proposed algorithm has a significant performance improvement in the average task processing ratio compared with the current works in different urban scenarios.