Wet-dry classification using LSTM and commercial microwave links

Hai Victor Habi, Hagit Messer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The task of rain detection, or wet-dry classification using measurements from commercial microwave links (CMLs) is a subject that been studied in depth. However, these studies are based on direct measurement of the signal level, which is known to be attenuated by rain. In this paper we present, for the first time an empirical study on rain classification using records of transmissions errors in the CMLs. Based on a dataset of measurements taken from operational cellular backhaul networks and meteorological measurements, and using long short-term memory (LSTM) units with a multi-variable time series, we demonstrate that measurements of microwave link error are related to rain and can even be used for rain detection (wet-dry classification). We evaluate the performance of LSTM on CMLs empirically, and analyze the results by comparison with rain detection based on attenuation measurements in the same links.

Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PublisherIEEE Computer Society
Pages149-153
Number of pages5
ISBN (Print)9781538647523
DOIs
StatePublished - 27 Aug 2018
Event10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2018-July
ISSN (Electronic)2151-870X

Conference

Conference10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Country/TerritoryUnited Kingdom
CitySheffield
Period8/07/1811/07/18

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