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

arXiv: https://arxiv.org/abs/2606.20578 · HTML: https://mlsanity.com/paper/2606.20578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2026.

## Built on by (most cited first, 1 of 1)

- [Single or Multiple Policies for Phase-Structured Reinforcement Learning?](https://mlsanity.com/paper/2610.03475/) (2026)

## Abstract (arXiv)

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.