Energy Efficient Trajectory Optimization and Resource Allocation for HAP-Assisted UAV Wireless Networks

Abstract

In this paper, we propose an energy efficiency (EE) optimization scheme for data collection in high altitude platform (HAP)-assisted unmanned aerial vehicle (UAV) network. The HAP acts as the aerial base station (ABS) to assist UAV and sensing devices (SDs) establishing direct uplink data transmission, i.e., device-to-UAV (D2U) communication. Our goal is to operate the UAV in an energy-efficient manner for cyclic data collection by dynamically clustering the target area and optimizing sink SD selection and transmit power in D2U communications. An optimization problem is formulated that maximizes the throughput-energy utility. Aiming at addressing the formulated problem, we propose a dynamic clustering algorithm based on Affinity Propagation (AP) to determine sink SDs for D2U communications, and a centralized reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to obtain the optimal UAV trajectory and sink SDs' transmit power. Simulation results demonstrate that the proposed scheme has advantages in EE compared with other schemes.