UAV-Based Opportunistic Non-Orthogonal Multiple Access Network

Unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) is a promising approach for improving spectral efficiency (SE), but conventional UAV-NOMA systems still suffer from high computational complexity and the difficulty of acquiring perfect channel state information (CSI) in practical air-to-ground (A2G) channels. To address these issues, this paper introduces opportunistic beamforming (OBF) into UAV-NOMA and develops a UAV-based opportunistic non-orthogonal multiple access (ONOMA) framework. The UAV deployment is modeled as a three-dimensional (3D) optimization problem, where the UAV altitude is jointly optimized with the horizontal position. Based on this model, two transmission schemes are proposed: spectral-efficiency-priority ONOMA (SP-ONOMA), which performs continuous 3D joint optimization, and complexity-priority ONOMA (CP-ONOMA), which performs reduced-complexity quasi-3D deployment through a finite candidate-altitude search. Closed-form power-allocation solutions are derived, and the lower bound, convergence behavior, and computational complexity of the proposed schemes are analyzed. In addition, a recursive binary pairing extension is developed to embed the original pairwise ONOMA optimizer into larger user sets through virtual-user aggregation, together with the corresponding raw and overhead-aware effective hierarchical utility metrics. Numerical results show that the proposed schemes outperform conventional OBF-OMA and several benchmark schemes, while achieving a favorable trade-off among SE, channel-estimation latency, and computational complexity. The results also show that the UAV altitude affects not only the propagation distance, but also the joint service feasibility through the minimum elevation-angle induced service region, and that the proposed multi-user extension reveals a clear trade-off between raw and overhead-aware effective utility in larger user sets.