RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum
  Handoffs, Disconnectivity, and Energy Consumption
Nesrine Cherif Wael Jaafar Halim Yanikomeroglu Abbas Yongacoglu
Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo
delivery. However, the limited on-board battery capacity, cellular
unreliability, and frequent handoffs in the airspace are the main obstacles to
unleash its full potential. Given that existing cellular networks were
primarily designed to service ground users, re-utilizing the same architecture
for highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging.
Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilize
the available energy efficiently, while guaranteeing reliable connectivity for
command-and-control and avoiding frequent handoff. To achieve this goal, we
propose a novel approach for joint cargo-UAV trajectory planning and cell
association. Specifically, we formulate the cargo-UAV mission as a
multi-objective problem aiming to 1) minimize energy consumption, 2) reduce
handoff events, and 3) guarantee cellular reliability along the trajectory. We
leverage reinforcement learning (RL) to jointly optimize the cargo-UAV's
trajectory and cell association. Simulation results demonstrate a performance
improvement of our proposed method, in terms of handoffs, disconnectivity, and
energy consumption, compared to benchmarks.