NOMA Green Communication for Electric Vehicles on Electrified Roads: A Hybrid DRL Approach

This paper presents an optimization framework aimed at enhancing communication throughput for electric vehicles (EVs) operating on electrified roads (eROADs) while simultaneously ensuring quality of service (QoS). By integrating inductive coil-based wireless power transfer (WPT) with multiuser vehicular networks, our framework facilitates continuous inmotion charging alongside efficient communication management. To address transmission power and data rate constraints in a non-orthogonal multiple access (NOMA) uplink scenario, we tackle challenges posed by realistic mobility models, Doppler effects, and variability in network coverage. We develop a hybrid deep reinforcement learning (DRL) algorithm that integrates deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) techniques to maximize throughput. Simulation results indicate that our hybrid DRL algorithm surpasses traditional DDPG and PPO methods, achieving up to 13.74% higher throughput under low transmission power limits. Our approach manages WPT and communications efficiently, promoting EV operations in intelligent transport systems and aligning with green communication goals.