UAV-Assisted Over-the-Air Computation

TL;DR

This paper introduces a UAV-assisted over-the-air computation system that optimizes UAV trajectory, power, and scaling to improve data aggregation accuracy over long distances, addressing channel fading issues.

Contribution

It proposes a novel joint optimization framework for UAV trajectory, power, and scaling factors to enhance AirComp performance, which was not addressed in prior work.

Findings

The proposed algorithm converges reliably.

Significant performance improvements over benchmarks.

Enhanced robustness in long-distance transmissions.

Abstract

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV's predetermined locations and flying speed, sensors' average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.