Measurement-in-the-Loop Reinforcement Learning for Regional UAV Communication Enhancement

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This paper investigates a measurement-driven rotary-wing unmanned aerial vehicle (UAV)-enabled wireless communication system serving multiple ground users. Unlike conventional studies relying on simplified theoretical channel models, we derive the air-to-ground (A2G) path loss and shadow-fading models from software-defined radio (SDR) channel measurements at 3.3 GHz. To maximize the minimum average user rate, we formulate a joint optimization of three-dimensional (3D) trajectory, velocity, transmit power, and bandwidth, subject to quality-of-service (QoS) and UAV kinematic constraints. Owing to the strong coupling and non-convexity, we reformulate it as a constrained Markov decision process (CMDP) with a continuous-action space and solve it via proximal policy optimization (PPO), where a constraint-preserving action parameterization enforces power, bandwidth, and motion constraints by construction. Simulations over real digital-elevation-model terrain show that the proposed approach attains 95.7% of a search-based rate reference at K=3 and surpasses an offline AO/SCA benchmark in max–min rate at all user densities, with 100% QoS satisfaction at K=3, near-zero constraint violations, and 0.142–0.181 ms online inference per decision.