FAIR-DQL: Fairness-Aware Deep Q-Learning for Enhanced Resource Allocation and RIS Optimization in High-Altitude Platform Networks

The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments. Key findings demonstrate substantial improvements: 9.15 dB SINR gain, 12.5 bps/Hz capacity, 78% power efficiency, and 0.82 fairness index. The framework achieves rapid 40-episode convergence with consistent delay performance. These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications, enabling practical HAPS-RIS deployments in rural connectivity, emergency communications, and urban networks.