Double Deep Reinforcement Learning for Fairness-Aware Sum-Rate Optimization in UxV-Enabled Multi-User Communication Systems

This study proposes a double deep reinforcement learning (D-DRL) to improve an index of rate fairness and sum-rate in UxV-enabled multi-user communication systems. In this study, a UxV-assisted multi-user communications scenario is considered. By taking into account the tradeoff between the two objectives and the time-sequential movement of the UxV, two DRL-based actor-critic networks are integrated to solve the designated problem. In the first actor-critic network, the rate fairness is maximized by jointly optimizing a hybrid precoder with a UxV trajectory. Subsequently, considering the rate fairness as a learning reward of the first network, sum-rate is maximized in the second network under the consideration of transmission power budgets, limited UxV battery capacity, and quality of service (QoS) constraints. The results show that the D-DRL which considered rate fairness outperformed DRL which did not by achieving maximum rate fairness and a higher sum-rate.