DRO-Based Computation Offloading and Trajectory Design for Low-Altitude Networks

TL;DR

This work tackles uncertain task sizes in a LAN where UAVs collaborate with a HAP to provide edge computing for ground users. It introduces a distributionally robust optimization using $L_1$-norm uncertainty sets to minimize the worst-case delay and develops the DRCOTO algorithm, which combines Benders decomposition with successive convex approximation to handle the mixed-integer min-max problem. Through simulations, DRCOTO demonstrates superior robustness and lower worst-case delays compared to deterministic, stochastic, and robust benchmarks, and reveals UAV trajectories that concentrate toward denser user regions. The approach offers a practical framework for robust offloading and trajectory design in UAV-HAP assisted LANs with uncertain workloads.

Abstract

The low-altitude networks (LANs) integrating unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) have become a promising solution for the rising computation demands. However, the uncertain task sizes and high mobility of UAVs pose great challenges to guarantee the quality of service. To address these issues, we propose an LAN architecture where UAVs and HAPs collaboratively provide computation offloading for ground users. Moreover, the uncertainty sets are constructed to characterize the uncertain task size, and a distributionally robust optimization problem is formulated to minimize the worst-case delay by jointly optimizing the offloading decisions and UAV trajectories. To solve the mixed-integer min-max optimization problem, we design the distributionally robust computation offloading and trajectories optimization algorithm. Specifically, the original problem is figured out by iteratively solving the outerlayer and inner-layer problems. The convex outer-layer problem with probability distributions is solved by the optimization toolkit. As for the inner-layer mixed-integer problem, we employ the Benders decomposition. The decoupled master problem concerning the binary offloading decisions is solved by the integer solver, and UAV trajectories in the sub-problem are optimized via the successive convex approximation. Simulation results show the proposed algorithm outperforms traditional optimization methods in balancing the worst-case delay and robustness.

Figures (3)

• Figure 1: Scenario of LAN.
• Figure 2: The UAV trajectories and locations of GUs and HAP.
• Figure 3: Comparison of multi-dimensional performance of various optimization methods.