The rapid increase in interest for Vehicle-to-Everything (V2X) networks has created significant challenges in efficient radio resource management. This paper addresses the problem of joint subcarrier assignment and power allocation to maximize the spectral efficiency of the system. First, this paper mathematically formulates resource allocation and power allocation as an optimization problem, which is solved using conventional optimization methodologies to establish a baseline for performance benchmarking. To overcome the high computational complexity associated with traditional optimization, we subsequently propose a Multi-Agent Deep Q-Network (Multi-DQN) agent framework based on deep reinforcement learning (DRL). The proposed agent learns optimal allocation strategies through interaction with the environment, enabling adaptive and real-time decision-making. The system performance is investigated in different environments under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, addressing a gap in prior approaches. Simulation results demonstrate that the proposed Multi-DQN agent approach significantly outperforms the enhanced conventional benchmark, achieving higher spectral efficiency (SE) while substantially reducing the computational complexity.
Al-Masry et al. (Tue,) studied this question.