Deep Reinforcement Learning Enabled Energy-Efficient Resource Allocation in Energy Harvesting Aided V2X Communication

Abstract

With the commercialization of the 5th generation mobile networks, vehicle-to-everything (V2X) communication has gained tremendous attention over the last decade. However, prevailing research has not sufficiently deliberated on the energy efficiency (EE) optimization issue. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL) based resource allocation algorithm. Moreover, we leverage energy harvesting (EH) to achieve long-term EE maximization. Based on the proximal policy optimization (PPO) framework, we invoke power splitting (PS) to divide the harvested energy delicately. Numerical results demonstrate that our proposed algorithm outperforms traditional and straightforward DRL-based resource allocation approaches in effectiveness and robustness.