Reinforcement Learning With Clustering Optimization for Antenna Parameter Adjustment in HAPS Networks

High-Altitude Platform Stations (HAPS) promise to extend broadband coverage over wide areas by acting as quasi‑stationary aerial base stations. However, wind‑induced movements cause antenna misalignment and unbalanced user distributions, leading to throughput degradation. This work presents a low‑complexity, equal clustering (EC) ‑aided deep Q‑network (DQN) algorithm for dynamic antenna control in HAPS networks. The proposed EC algorithm partitions users into balanced clusters by identifying low‑density sectors in a single pass, avoiding the iterative refinements of other clustering algorithms such as K‑Means. A DQN then fine-tunes the antenna tilt angles and beam widths based on the EC results to maximize the throughput of low‑throughput users. The proposed method’s performance is evaluated through simulations under rotation and shift scenarios and benchmarked against three Reinforcement Learning (RL)-based approaches and a K-Means clustering baseline. Compared with these methods, the EC-aided DQN delivers higher throughput, especially for users in the low-throughput tail, while reducing computational complexity. For instance, the percentage of low-throughput users was reduced by over 10% in the case of 5 km shift in the regions of Osaka and Sendai, compared to K-means. Similarly, in the case of 30° rotation, the percentage is reduced by 4.1% and 6.5% for the same regions. Statistical significance tests confirm that the improvements are not due to random simulation noise.