Abstract This paper investigates an intelligent reflecting surface (IRS)-assisted hybrid high-altitude platform (HAP) and unmanned aerial vehicle (UAV) uplink communication network employing non-orthogonal multiple access (NOMA). A unified uplink system model is developed that jointly captures UAV three-dimensional deployment, IRS-assisted composite channels, SIC-based uplink NOMA reception, and shared-spectrum cross-tier interference between HAP and UAV layers. To efficiently support dense uplink connectivity, a joint uplink sum-rate maximization problem is formulated under practical mobility, power, quality-of-service, and interference constraints, resulting in a highly non-convex mixed-integer optimization problem. A low-complexity block coordinate descent (BCD)-based framework is proposed to iteratively optimize UAV deployment, user association, uplink power allocation, and IRS phase shifts. The proposed framework is particularly well-suited for high-capacity data offloading and IoT-based safety monitoring in remote mining environments, supporting the digital transformation of the mining sector in alignment with Saudi Vision 2030. Extensive simulations demonstrate that the proposed framework significantly outperforms conventional aerial and terrestrial benchmarks. Specifically, compared to hybrid HAP–UAV uplink NOMA without IRS, the proposed design improves the uplink sum rate by up to 23.03% and by an average of 15.39% over K=10–100 simultaneously scheduled users. Relative to UAV-only and HAP-only IRS-assisted uplink schemes, gains of up to 43.80% and 59.01% are achieved, respectively, while effective improvement is observed over terrestrial uplink baselines under dense user loading. Moreover, IRS-assisted user association yields additional gains of up to 21.45% in sum rate, while outage probability is substantially reduced across the entire SINR range. These results confirm that IRS-assisted hybrid HAP–UAV uplink NOMA provides a scalable and reliable solution for future 6G-oriented dense uplink communication networks.
Alhashmi et al. (Sat,) studied this question.