NOMA-based energy-efficient task scheduling in vehicular edge computing networks: A self-imitation learning-based approach

Mobile Edge Computing (MEC) is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access (OMA) technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access (NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning (SIL)-based Deep Reinforcement Learning (DRL) has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.