Aldalahmeh, S, Al-Jazzar, SO, McLernon, D orcid.org/0000-0002-5163-1975 et al. (2 more authors) (2020) Distributed Combining Techniques for Distributed Detection in Fading Wireless Sensor Networks. In: Proceedings of 2nd IEEE Middle East and North Africa Communications Conference (MENACOMM). 2019 2nd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), 19-21 Nov 2019, Manama, Bahrain. IEEE ISBN 978-1-7281-3687-5
Abstract
We investigate distributed combining techniques for distributed detection in wireless sensor networks (WSNs) over Rayleigh fading multiple access channel (MAC). The MAC also suffers from with path loss and additive noise. The WSN is modelled as a Poisson point process (PPP). Two distributed transmit combining techniques are proposed to mitigate fading; distributed equal gain transmit combining (ddEGTC) and distributed maximum ratio transmit combining (dMRTC). The performance of the previous methods is analysed using stochastic geometry tools, where the mean and variance of the detector’s test statistic are found thus enabling the fitting of the received signal distribution by a log-normal distribution. Surprisingly, simulation results show a that ddEGTC outperforms dMRTC.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. This is an author produced version of a paper published in Proceedings of 2nd IEEE Middle East and North Africa Communications Conference (MENACOMM). 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: | Wireless sensor networks; distributed detection; multiple access channel; distributed transmit combining; 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: | 18 Sep 2019 12:49 |
Last Modified: | 28 Feb 2020 12:28 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MENACOMM46666.2019.8988544 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150995 |