RL-based Joint Coverage and Beam Optimization of High Altitude Platform Systems

Abstract

High Altitude Platform Systems (HAPS) are a promising component of 6G network architectures, offering a unique "freedom of movement" that distinguishes them from static terrestrial networks (TN) and orbit-constrained satellite communications. This inherent mobility for HAPS provides a powerful mechanism to address non-stationarity, spatio-temporal user distributions, and traffic dynamics, such as periodic population migrations. This work addresses three key optimization problems in HAPS networks: (a) HAPS positioning for optimal coverage, (b) beam allocation, and (c) joint optimization of coverage and beam allocation. To tackle these complex challenges, a Reinforcement Learning (RL) framework is proposed, capable of operating in scenarios with multiple HAPS. The results demonstrate that the RL-based approach effectively learns to control HAPS positioning and resource allocation, dynamically adapting to variations in user distributions and traffic patterns. In particular, by employing a multi-policy Proximal Policy Optimization (PPO) approach, the proposed framework jointly learns HAPS positioning and allocating beams under spatio-temporal traffic demand variations and outperforms heuristic baselines. Simulation results demonstrate that our joint optimization approach significantly improves sum-rate and user satisfaction, showing that the dynamic mobility of HAPS can be successfully exploited to create highly responsive and efficient next-generation networks.