Work

Robust UAV-Oriented Wireless Communications via Multi-Agent Deep Reinforcement Learning to Optimize User Coverage

Year: 2025

Type: article

Abstract: In this study, we deploy drones as dynamic base stations to address the issue of optimizing user coverage in areas without fixed base station infrastructure. To optimize drone placement, we employ Deep Q-Learning, beginning with a centralized approach due to its simplicity and ease of training. In this centralized approach, all drones are trained simultaneously. We also employ a decentralized technique in which each drone acts autonomously while sharing a common neural network, allowing for individualized learning. In addition, we explore the impacts of jamming on UAVs and provide a reliable approach for mitigating this interference. To boost robustness, we employ stochastic user distributions, which train our policy to successfully respond to a wide range of user situations.

Source: Drones

DOI: 10.3390/drones9050321

Authors: Mahfizur Rahman Khan, Gowtham Raj Veeraswamy Premkumar, Bryan Van Scoy

Institution: Miami University

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137