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

TL;DR

This paper presents a robust optimization framework for UAV and HAP-assisted low-altitude networks to minimize worst-case delay amid uncertain task sizes, using a novel distributionally robust approach.

Contribution

It introduces a distributionally robust optimization model for joint offloading and trajectory design in LANs, addressing uncertainty and mobility challenges.

Findings

Proposed algorithm outperforms traditional methods in reducing worst-case delay.

Effectively balances robustness and delay in UAV-HAP networks.

Demonstrates improved performance through simulation results.

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.