Joint Trajectory and Resource Optimization for Delay Minimization of UAV-Enabled NOMA-MEC System With LWPT

Unmanned aerial vehicles (UAVs) enhance mobile edge computing (MEC) coverage, but in remote emergency scenarios limited battery life and scarce spectrum exacerbate interference, link instability, and end-to-end delay. To address these issues, we propose a joint trajectory and delay-minimization framework that integrates laser-beamed wireless power transfer (LWPT) with UAV-enabled non-orthogonal multiple access (NOMA) MEC. First, we present a practical system architecture where a ground laser-powered beacon (PB) continuously recharges the UAV during flight, enabling persistent aerial patrols that concurrently offer wireless charging and computation services to ground users. Second, we formulate a unified mixed-integer nonconvex optimization problem that jointly optimizes the UAV trajectory, task offloading ratios, PB power distribution, and user-scheduling policy under energy-causality, NOMA interference, and flight-dynamics constraints. Third, to address the resulting non-convexity, we develop a hierarchical decomposition and alternating-optimization method: the original problem is decomposed into trajectory and resource-allocation subproblems and solved using convex approximations and efficient scheduling algorithms to obtain practical solutions. Fourth, extensive simulations demonstrate that the proposed LWPT-assisted NOMA UAV-MEC scheme substantially reduces total system delay while improving energy efficiency and throughput compared with conventional OMA-MEC baselines and five recent heuristic algorithms.