Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

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

Abstract

Stochastic channel models are usually calibrated
after extracting the parameters of the multipath components from measurements. This paper proposes a method to infer on the underlying parameters of a stochastic channel model, in particular the Turin model, without resolving the multipath components. Channel measurements are summarised into temporal moments instead of the multipath parameters. The parameters of the stochastic model are then estimated from the observations of temporal moments using a method of moments approach. The estimator is tested on real data obtained from in-room channel measurements. It is concluded that calibration of stochastic models can be done without multipath extraction, and that temporal moments are informative summary statistics about the model parameters.
Original languageEnglish
Title of host publication20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Number of pages5
Publication date2019
Publication statusSubmitted - 2019
Event20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 - Cannes, France
Duration: 2 Jul 20195 Jul 2019

Conference

Conference20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
CountryFrance
CityCannes
Period02/07/201905/07/2019

Fingerprint

parameter
calibration
method
statistics

Keywords

  • stochastic channel model
  • multipath
  • summary statistics
  • Parameter estimation
  • method of moments

Cite this

Bharti, A., Adeogun, R., & Pedersen, T. (2019). Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. Manuscript submitted for publication. In 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Bharti, Ayush ; Adeogun, Ramoni ; Pedersen, Troels. / Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019.
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abstract = "Stochastic channel models are usually calibratedafter extracting the parameters of the multipath components from measurements. This paper proposes a method to infer on the underlying parameters of a stochastic channel model, in particular the Turin model, without resolving the multipath components. Channel measurements are summarised into temporal moments instead of the multipath parameters. The parameters of the stochastic model are then estimated from the observations of temporal moments using a method of moments approach. The estimator is tested on real data obtained from in-room channel measurements. It is concluded that calibration of stochastic models can be done without multipath extraction, and that temporal moments are informative summary statistics about the model parameters.",
keywords = "stochastic channel model, multipath, summary statistics, Parameter estimation, method of moments",
author = "Ayush Bharti and Ramoni Adeogun and Troels Pedersen",
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Bharti, A, Adeogun, R & Pedersen, T 2019, Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. in 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019, Cannes, France, 02/07/2019.

Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. / Bharti, Ayush; Adeogun, Ramoni; Pedersen, Troels.

20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019.

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

TY - GEN

T1 - Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

AU - Bharti, Ayush

AU - Adeogun, Ramoni

AU - Pedersen, Troels

PY - 2019

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N2 - Stochastic channel models are usually calibratedafter extracting the parameters of the multipath components from measurements. This paper proposes a method to infer on the underlying parameters of a stochastic channel model, in particular the Turin model, without resolving the multipath components. Channel measurements are summarised into temporal moments instead of the multipath parameters. The parameters of the stochastic model are then estimated from the observations of temporal moments using a method of moments approach. The estimator is tested on real data obtained from in-room channel measurements. It is concluded that calibration of stochastic models can be done without multipath extraction, and that temporal moments are informative summary statistics about the model parameters.

AB - Stochastic channel models are usually calibratedafter extracting the parameters of the multipath components from measurements. This paper proposes a method to infer on the underlying parameters of a stochastic channel model, in particular the Turin model, without resolving the multipath components. Channel measurements are summarised into temporal moments instead of the multipath parameters. The parameters of the stochastic model are then estimated from the observations of temporal moments using a method of moments approach. The estimator is tested on real data obtained from in-room channel measurements. It is concluded that calibration of stochastic models can be done without multipath extraction, and that temporal moments are informative summary statistics about the model parameters.

KW - stochastic channel model

KW - multipath

KW - summary statistics

KW - Parameter estimation

KW - method of moments

M3 - Article in proceeding

BT - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019

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Bharti A, Adeogun R, Pedersen T. Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. In 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019