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
There is a rich literature on the prediction of coverage in random wireless networks using stochastic geometry. Though valuable, the existing stochastic geometry-based analytical expressions for coverage are only valid for a restricted set of oversimplified network scenarios. Deriving such expressions for more general and more realistic network scenarios has so far been proven intractable. In this work, we adopt a data-driven approach to derive a model that can predict the coverage probability in any random wireless network. We first show that the coverage probability can be accurately approximated by a parametrized sigmoid-like function. Then, by building large simulation-based datasets, the relationship between the wireless network parameters and the parameters of the sigmoid-like function is modeled using a neural network.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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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: | 10.1109/GLOCOMW.2018.8644199 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136499 |