Maximizing Group-Based Vehicle Communications and Fairness: A Reinforcement Learning Approach

Vehicle-to-vehicle (V2V) communications retain immense potential in elevating network throughput for next-generation vehicular applications. This study investigates the problem of maximizing the total number of communications while ensuring fairness among V2V communication pairs (MVGCF). In the experiments conducted, each vehicle has a dedicated data stream to be shared among others in the same group. However, not all pairs can directly communicate due to communication range limitations. Hence, the current study focuses on relaying data packets in networks through multi-hop vehicles sharing resource blocks within a time frame while adhering to signal-to-interference-plus-noise ratio (SINR) and half-duplex constraints. To accomplish the research objectives mentioned above, two reinforcement learning (RL) algorithms, namely Q-Learning and Double Deep Q-Networks (DDQN), are proposed. However, to overcome the scalability and computational limitations of RL methods, we devise hybrid heuristic-based reinforcement learning methods, MVGCF_QLearning and MVGCF_DDQN. The numerical results demonstrate the hybrid approaches' effectiveness in terms of the number of successful communications and max-min fairness when compared to a random_agent, and the conventional RL methods for small and large networks.