Multi-Agent DRL for Resource Allocation in AoI-Aware Energy-Efficient C-V2X Networks

This paper aims to tackle the complex problem of channel assignment and joint power-energy al-location within a cellular-vehicle-to-everything (C-V2X) network in an urban traffic intersection. Here, the C-V2X network is strategically deployed to facilitate the coordination of multiple vehicle platoons. This includes updating platoon states to the roadside unit (RSU) and managing the exchange of cooperative awareness messages (CAMs) among vehicles. Our objective is to minimise the average age of information (AoI), maximise the CAM delivery probability, and promote sustainable, green communication practices through optimal power-energy management. Given the intricate nature of this challenge, we adopt a multi-agent deep reinforcement learning (MADRL) approach based on Markov decision process (MDP). Next, we introduce two innovative algorithms based on multi-agent deep deterministic policy gradient (MADDPG) and twin delayed deep deterministic policy gradient (TD3) to effectively address the optimisation problem. Finally, the simulation results demonstrate remarkable performance in terms of energy efficiency, while maintaining algorithm convergence speed and AoI level.