A Machine Learning Approach to Predicting Coverage in Random Wireless Networks

El Hammouti, H, Ghogho, M and Zaidi, SAR (2019) A Machine Learning Approach to Predicting Coverage in Random Wireless Networks. In: Proceedings of 2018 IEEE Globecom Workshops (GC Wkshps). 2018 IEEE Globecom Workshops (GC Wkshps), 09-13 Dec 2018, Abu Dhabi, United Arab Emirates. IEEE . ISBN 978-1-5386-4920-6

Abstract

Metadata

Authors/Creators:
  • El Hammouti, H
  • Ghogho, M
  • Zaidi, SAR
Copyright, Publisher and Additional Information: © 2018 IEEE. This is an author produced version of a paper published in Proceedings of 2018 IEEE Globecom Workshops (GC Wkshps). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Coverage probability; sigmoid function; neural networks; machine learning; stochastic geometry
Dates:
  • Published: 21 February 2019
  • Accepted: 30 August 2018
  • Published (online): 21 February 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 09 Nov 2018 10:27
Last Modified: 07 May 2019 15:21
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/GLOCOMW.2018.8644199

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