Task Scheduling and Power Allocation in Multiuser Multiserver Vehicular Networks by NOMA and Deep Reinforcement Learning

In the pursuit of achieving optimal functionality for internet of vehicles (IoV), the integration of multi-access edge computing (MEC) emerges as a solution, offering high bandwidth, low latency, robust security, and reliability services. In this article, we consider a multi-user multi-server vehicular network scenario, where the non-orthogonal multiple access (NOMA) technology in 5G is used to optimize spectrum resource utilization. We firstly formulate the problem using mixed integer non-linear programming (MINLP) and propose a task scheduling scheme based on deep reinforcement learning (DRL) to handle high-dimensional state and action spaces and to approximate the optimal solution. We then proposed solutions to the NOMA clustering and power allocation problems in order to further reducing system latency in the uplink transmission stage. Simulation results underscore the efficacy of our proposed algorithm in systems with unevenly distributed computing resources, showcasing superior performance compared to alternative algorithms.