Energy Tradeoff-Aware Offloading and Resource Allocation for 3D UAV-MEC Networks

Unmanned aerial vehicles (UAVs) have become key enablers for expanding mobile edge computing (MEC) coverage, offering enhanced computational efficiency and responsiveness. This paper investigates a fundamental tradeoff in UAV-assisted three-dimensional (3D) MEC networks: while lowering the energy consumption of ground devices can improve system efficiency, it often increases UAV propulsion energy and communication distance, especially under high-altitude line-of-sight (LoS) conditions. To address this issue, we design an optimization framework that minimizes a weighted sum of UAV and ground device energy consumption by jointly optimizing the UAV's 3D trajectory, binary offloading decisions, and computation resource allocation under delay constraints. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved through a block coordinate descent (BCD) framework, leveraging a double-loop penalty successive convex approximation (P-SCA) method to handle non-convexities and binary variables. Extensive simulations demonstrate the proposed scheme's superior performance compared to benchmarks. The findings also reveal key insights into the tradeoffs between aerial propulsion demands and ground communication efficiency, as well as the impact of altitude variations on system behavior in 3D UAV-MEC environments.