Intelligent UAV Positioning and Fair Power Allocation via DRL in Cloud-Impaired HAP-to-UAV FSO/RF Systems

This paper proposes an adaptive resource allocation strategy for a non-terrestrial network (NTN) architecture, where a high-altitude platform (HAP) provides free-space optical (FSO) backhaul links, and unmanned aerial vehicles (UAVs) serve as radio-frequency (RF) access nodes for end users. The goal is to ensure a fair and efficient allocation of transmission capacity among users. However, atmospheric turbulence and cloud dynamics in FSO links can severely degrade system performance. To address this issue, this work integrates deep reinforcement learning (DRL) to optimize UAV positioning in real time, enabling UAVs to avoid cloud-blocked regions and maintain stable connectivity for users. Based on channel states and user rate requirements, the proposed method decouples subchannel assignment from power allocation and incorporates DRL-based UAV trajectory control, forming a comprehensive optimization framework. The numerical results demonstrate that the proposed approach significantly improves system throughput, maintains user fairness, and reduces transmit power requirements compared to conventional methods.