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Open Access

ARTICLE

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

Muhammad Ejaz1, Muhammad Asim2,*, Mudasir Ahmad Wani2,3, Kashish Ara Shakil4,*
1 School of Computer Science and Engineering, Central South University, Changsha, 410083, China
2 EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
* Corresponding Author: Muhammad Asim. Email: email; Kashish Ara Shakil. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072464

Received 27 August 2025; Accepted 10 October 2025; Published online 06 November 2025

Abstract

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.

Keywords

Wireless communication; high-altitude platform station; reconfigurable intelligent surfaces; deep Q-learning

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