Particle Swarm Optimization Based Resource Allocation for High Altitude Platform Networks

Abstract

High Altitude Platforms (HAPs) are emerging as cost-effective aerial communication nodes capable of providing broadband wireless coverage over large geographical areas. However, efficient resource allocation remains a key challenge due to channel variability, power constraints, and user heterogeneity. This paper proposes a Particle Swarm Optimization (PSO)–based framework for joint subcarrier and power allocation in an OFDMA-based HAP communication system. The proposed approach employs PSO for adaptive subcarrier assignment and integrates the water-filling algorithm for power allocation. Performance is evaluated against conventional resource allocation schemes, including Max-SNR, Round Robin, and Proportional Fair. Simulation results demonstrate that the proposed PSO-based framework achieves competitive spectral and power efficiency, a more balanced distribution of user capacity, and a significant reduction in computational complexity, with an average execution-time reduction of approximately 97% compared to conventional methods. These results highlight the practicality of the proposed approach for real-time and adaptive resource management in next generation HAP-based aerial communication networks.