UAV-Assisted Integrated Communication and Over-the-Air Computation with Interference Awareness

TL;DR

This paper introduces a UAV-assisted integrated communication and over-the-air computation framework that leverages UAV mobility and deep reinforcement learning to mitigate interference and optimize data aggregation performance.

Contribution

It proposes a novel joint optimization approach for UAV trajectory, scheduling, and transmission strategies to enhance AirComp accuracy and data rates.

Findings

The proposed method converges reliably in simulations.

It outperforms baseline methods in various scenarios.

UAV mobility effectively reduces interference and improves performance.

Abstract

Over the air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor critic based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.