Aerial RIS-Aided Uplink NOMA: A DRL-Aided Resource Allocation Framework

Abstract

Integrating uncrewed aerial vehicle (UAV)-mounted aerial reconfigurable intelligent surfaces (RISs) holds significant promise for enhancing the performance of ground-based networks. This paper proposes a novel three-dimensional (3D) deployment and partitioning framework for aerial RIS-assisted uplink grant-free non-orthogonal multiple access (GF-NOMA). GF-NOMA allows users to access the resource block immediately without scheduling overhead, but requires sufficient received power disparity for reliable successive interference cancellation (SIC). Specifically, we consider three design objectives, namely max-sum throughput (MST), max-min fairness (MMF), and proportional-fairness rate (PFR), and jointly optimize aerial RIS partitioning and deployment. A closed-form analytical solution is derived for MST and MMF regimes, while an unsupervised learning (USL) framework is developed for partitioning, and a deep reinforcement learning (DRL)-based policy is designed for adaptive UAV deployment under imperfect channel estimates and residual SIC conditions. Extensive numerical results show that the learned schemes for MST and MMF closely track their respective theoretical benchmarks, within 1% for MST and $2-10\%$ for MMF, under the same non-ideal channel knowledge model. All three proposed USL-DRL schemes significantly outperform fixed-deployment baselines: the proposed MST-USL-DRL achieves approximately 32–34% sum-rate gain, the proposed MMF-USL-DRL improves the minimum user rate by about 47–74% depending on the evaluation regime, and the proposed PFR-USL-DRL delivers roughly 57% gain over fixed deployment.