A Machine Learning Based Quality of Service Estimator for Aerial Wireless Networks

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Eduardo Nuno Almeida (Author) Other; Kelwin Fernandes (Author) Other; Francisco Andrade (Author) Other; Pedro Silva (Author) Other; Rui Campos (Author) FEUP; Manuel Ricardo (Author) FEUP

Conference proceedings International

Title: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019, Barcelona, Spain, October 21-23, 2019

Pages: 1-6

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-JCM

DOI: 10.1109/wimob.2019.8923217

Abstract (EN): Unmanned Aerial Vehicles (UAVs) acting as aerial Wi-Fi Access Points or cellular Base Stations are being considered to deploy on-demand network capacity in order to serve traffic demand surges or replace Base Stations. The ability to estimate the Quality of Service (QoS) for a given network setup may help in solving UAV placement problems. This paper proposes a Machine Learning (ML) based QoS estimator, based on convolutional neural networks, which estimates the QoS for a given network by considering the UAV positions, the user positions and their offered traffic. The ML-based QoS estimator represents a novel paradigm for estimating the QoS in aerial wireless networks. It provides fast and accurate estimations with reduced computational complexity. We demonstrate the usefulness and applicability of the proposed QoS estimator using the ideal UAV placement algorithm. Simulation results show the QoS estimator has an average prediction error lower than 5%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6