Parameter Estimation for Stochastic Channel Models using Temporal Moments

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

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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 publicationProceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
Publication statusAccepted/In press - 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
CountryUnited States
CityAtlanta
Period07/07/201912/07/2019
Internet address

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Parameter estimation
Sampling

Cite this

Bharti, A., Adeogun, R., & Pedersen, T. (Accepted/In press). Parameter Estimation for Stochastic Channel Models using Temporal Moments. In Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
Bharti, Ayush ; Adeogun, Ramoni ; Pedersen, Troels. / Parameter Estimation for Stochastic Channel Models using Temporal Moments. Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. 2019.
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title = "Parameter Estimation for Stochastic Channel Models using Temporal Moments",
abstract = "This paper proposes a method to infer on theparameters of a stochastic channel model from observations oftemporal moments without multipath extraction. The distributionof 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 themodel parameters, as the parameters can be recovered from thesamples.",
author = "Ayush Bharti and Ramoni Adeogun and Troels Pedersen",
year = "2019",
language = "English",
booktitle = "Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting",

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Bharti, A, Adeogun, R & Pedersen, T 2019, Parameter Estimation for Stochastic Channel Models using Temporal Moments. in Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Atlanta, United States, 07/07/2019.

Parameter Estimation for Stochastic Channel Models using Temporal Moments. / Bharti, Ayush; Adeogun, Ramoni; Pedersen, Troels.

Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. 2019.

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

TY - GEN

T1 - Parameter Estimation for Stochastic Channel Models using Temporal Moments

AU - Bharti, Ayush

AU - Adeogun, Ramoni

AU - Pedersen, Troels

PY - 2019

Y1 - 2019

N2 - This paper proposes a method to infer on theparameters of a stochastic channel model from observations oftemporal moments without multipath extraction. The distributionof 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 themodel parameters, as the parameters can be recovered from thesamples.

AB - This paper proposes a method to infer on theparameters of a stochastic channel model from observations oftemporal moments without multipath extraction. The distributionof 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 themodel parameters, as the parameters can be recovered from thesamples.

M3 - Article in proceeding

BT - Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

ER -

Bharti A, Adeogun R, Pedersen T. Parameter Estimation for Stochastic Channel Models using Temporal Moments. In Proceedings 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. 2019