Parameter Estimation for Stochastic Channel Models using Temporal Moments

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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.
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Details

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 publicationProceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
Publication date2019
Publication statusAccepted/In press - 2019
Publication categoryResearch
Peer-reviewedYes
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
LandUnited States
ByAtlanta
Periode07/07/201912/07/2019
Internetadresse
ID: 293818193