Parameter Estimation for Stochastic Channel Models using Temporal Moments

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Abstract

This paper proposes a method to infer on the parameters of a stochastic channel model from observations of temporal moments without multipath extraction. The distribution of the temporal moments is approximated to be Gaussian, and sampling is carried out from the approximate posterior.
The temporal moments are found to be informative about the model parameters, as the parameters can be recovered from the samples.
Original languageEnglish
Title of host publication2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019
Number of pages2
PublisherIEEE
Publication date31 Oct 2019
Pages1267-1268
Article number8888862
ISBN (Print) 978-1-7281-0692-2, 978-1-7281-0692-2, 978-1-7281-0693-9
ISBN (Electronic) 978-1-7281-0692-2
DOIs
Publication statusPublished - 31 Oct 2019
Event2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Atlanta, USA, Atlanta, United States
Duration: 7 Jul 201912 Jul 2019
http://www.2019apsursi.org

Conference

Conference2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
LocationAtlanta, USA
Country/TerritoryUnited States
CityAtlanta
Period07/07/201912/07/2019
Internet address
SeriesI E E E Antennas and Propagation Society. International Symposium
ISSN1522-3965

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