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Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargodelivery. However, the limited on-board battery capacity, cellularunreliability, and frequent handoffs in the airspace are the main obstacles tounleash its full potential. Given that existing cellular networks wereprimarily designed to service ground users, re-utilizing the same architecturefor highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging.Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilizethe available energy efficiently, while guaranteeing reliable connectivity forcommand-and-control and avoiding frequent handoff. To achieve this goal, wepropose a novel approach for joint cargo-UAV trajectory planning and cellassociation. Specifically, we formulate the cargo-UAV mission as amulti-objective problem aiming to 1) minimize energy consumption, 2) reducehandoff events, and 3) guarantee cellular reliability along the trajectory. Weleverage reinforcement learning (RL) to jointly optimize the cargo-UAV'strajectory and cell association. Simulation results demonstrate a performanceimprovement of our proposed method, in terms of handoffs, disconnectivity, andenergy consumption, compared to benchmarks.

RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption