Reinforcement Learning for Uplink Access Optimization in UAV-Assisted 5G Networks Under Emergency Response

We study Unmanned Aerial Vehicle (UAV) assisted 5G uplink connectivity for disaster response, where a UAV acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink Non-Orthogonal Multiple Access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic Line of Sight/Non Line of Sight (LoS/NLoS), realistic receiver noise and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2−10.1 percentage points over strong baselines (OFDMA with heuristic placement, PSO-based placement/power, PPO without NOMA) while reducing median UE transmit power by 64.6%. Results are averaged over ≥5 random seeds with 95% confidence intervals; ablations isolate the gains from NOMA, adaptive SIC order, and bounded action parameterization. We discuss robustness to imperfect SIC and CSI errors, and release code/configurations to support reproducibility.