Machine Learning for Predictive On-Demand Deployment of UAVs for Wireless Communications

TL;DR

This paper introduces a machine learning framework using Gaussian mixture models to predict network congestion and optimize UAV deployment for wireless coverage, significantly reducing power consumption.

Contribution

The paper presents a novel ML-based approach for predictive UAV deployment that minimizes power use and improves efficiency in wireless networks.

Findings

Reduces downlink transmit power by over 20%.

Improves power efficiency compared to non-predictive deployment.

Provides a method for optimal UAV placement based on traffic prediction.

Abstract

In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular users. In order to have a comprehensive analysis of cellular traffic, an ML framework based on a Gaussian mixture model (GMM) and a weighted expectation maximization (WEM) algorithm is introduced to predict the potential network congestion. Then, the optimal deployment of UAVs is studied to minimize the transmit power needed to satisfy the communication demand of users in the downlink, while also minimizing the power needed for UAV mobility, based on the predicted cellular traffic. To this end, first, the optimal partition of service areas of each UAV is derived, based on a fairness principle. Next, the optimal location of each UAV that minimizes the total power consumption is derived. Simulation results show that the proposed ML approach can reduce the required downlink transmit power and improve the power efficiency by over 20%, compared with an optimal deployment of UAVs with no ML prediction.