IRS-assisted UAV wireless powered communication network for sustainable federated learning

Abstract

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are indispensable potential technologies in the future sixth-generation mobile communication technology (6 G). Utilizing the high beamforming gain of IRS and the high mobility of UAV can achieve ubiquitous network coverage and ensure a high-quality communication environment. Wireless Powered Communication Network (WPCN) is an emerging green communication technology network that converts received radio frequency (RF) signals into electrical energy to power Federated Learning (FL) users. FL users perform local computing and model transmission through the collected energy, ensuring the sustainability of FL. In order to solve the problems of complex communication environment, privacy protection, and energy constraints on terminal devices, we design an FL system based on an IRS-assisted UAV wireless power communication network, which minimizes the UAV transmission energy by jointly optimizing UAV location, IRS phase shift, and resource allocation strategies. We use a low-complexity iterative algorithm to solve this complex non-convex problem. The simulation results show that the performance of the proposed algorithm is obviously better than that of other benchmark schemes, indicating that joint optimization plays an essential role in improving the performance of the system.