In this paper, we propose a novel joint deep reinforcement learning (DRL)-based solution to optimize the utility of an uncrewed aerial vehicle (UAV)-assisted communication network. To maximize the number of users served within the constraints of the UAV's limited bandwidth and power resources, we employ deep Q-Networks (DQN) and deep deterministic policy gradient (DDPG) algorithms for optimal resource allocation to ground users with heterogeneous data rate demands. The DQN algorithm dynamically allocates multiple bandwidth resource blocks to different users based on current demand and available resource states. Simultaneously, the DDPG algorithm manages power allocation, continuously adjusting power levels to adapt to varying distances and fading conditions, including Rayleigh fading for non-line-of-sight (NLoS) links and Rician fading for line-of-sight (LoS) links. Our joint DRL-based solution demonstrates an increase of up to 41% in the number of users served compared to scenarios with equal bandwidth and power allocation.

翻译：本文提出了一种基于联合深度强化学习（DRL）的新颖解决方案，用于优化无人机（UAV）辅助通信网络的效用。为了在无人机有限的带宽和功率资源约束下最大化所服务的用户数量，我们采用深度Q网络（DQN）和深度确定性策略梯度（DDPG）算法，为具有异构数据速率需求的地面用户进行最优资源分配。DQN算法根据当前需求和可用资源状态，动态地将多个带宽资源块分配给不同用户。同时，DDPG算法管理功率分配，持续调整功率水平以适应变化的距离和衰落条件，包括非视距（NLoS）链路的瑞利衰落和视距（LoS）链路的莱斯衰落。与采用均等带宽和功率分配的场景相比，我们基于联合DRL的解决方案所服务的用户数量最多可增加41%。