Calibration of Stochastic Channel Models using Approximate Bayesian Computation

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Abstract

Calibration of stochastic radio channel models is the process of fitting the parameters of a model such that it generates synthetic data similar to the measurements. The traditional calibration approach involves, first, extracting the multipath components, then, grouping them into clusters, and finally, estimating the model parameters. In this paper, we propose to use approximate Bayesian computation (ABC) to calibrate stochastic channel models so as to bypass the need for multipath extraction and clustering. We apply the ABC method to calibrate the well-known Saleh-Valenzuela model and show its performance in simulations and using measured data. We find that the Saleh-Valenzuela model can be calibrated directly without the need for multipath extraction or clustering.
Original languageEnglish
Title of host publication2019 IEEE GLOBECOM Workshops
Number of pages6
Publication date2019
Publication statusPublished - 2019
EventGLOBECOM 2019 - Hawaii
Duration: 9 Dec 201913 Dec 2019

Conference

ConferenceGLOBECOM 2019
LocationHawaii
Period09/12/201913/12/2019

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Calibration

Keywords

  • stochastic channel model
  • Bayesian
  • Parameter Estimation
  • multipath

Cite this

@inproceedings{1a53cf5d7feb4bf7b79ffd25b7d1e501,
title = "Calibration of Stochastic Channel Models using Approximate Bayesian Computation",
abstract = "Calibration of stochastic radio channel models is the process of fitting the parameters of a model such that it generates synthetic data similar to the measurements. The traditional calibration approach involves, first, extracting the multipath components, then, grouping them into clusters, and finally, estimating the model parameters. In this paper, we propose to use approximate Bayesian computation (ABC) to calibrate stochastic channel models so as to bypass the need for multipath extraction and clustering. We apply the ABC method to calibrate the well-known Saleh-Valenzuela model and show its performance in simulations and using measured data. We find that the Saleh-Valenzuela model can be calibrated directly without the need for multipath extraction or clustering.",
keywords = "stochastic channel model, Bayesian, Parameter Estimation, multipath",
author = "Ayush Bharti and Troels Pedersen",
year = "2019",
language = "English",
booktitle = "2019 IEEE GLOBECOM Workshops",

}

Bharti, A & Pedersen, T 2019, Calibration of Stochastic Channel Models using Approximate Bayesian Computation. in 2019 IEEE GLOBECOM Workshops. GLOBECOM 2019, 09/12/2019.

Calibration of Stochastic Channel Models using Approximate Bayesian Computation. / Bharti, Ayush; Pedersen, Troels.

2019 IEEE GLOBECOM Workshops. 2019.

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

TY - GEN

T1 - Calibration of Stochastic Channel Models using Approximate Bayesian Computation

AU - Bharti, Ayush

AU - Pedersen, Troels

PY - 2019

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N2 - Calibration of stochastic radio channel models is the process of fitting the parameters of a model such that it generates synthetic data similar to the measurements. The traditional calibration approach involves, first, extracting the multipath components, then, grouping them into clusters, and finally, estimating the model parameters. In this paper, we propose to use approximate Bayesian computation (ABC) to calibrate stochastic channel models so as to bypass the need for multipath extraction and clustering. We apply the ABC method to calibrate the well-known Saleh-Valenzuela model and show its performance in simulations and using measured data. We find that the Saleh-Valenzuela model can be calibrated directly without the need for multipath extraction or clustering.

AB - Calibration of stochastic radio channel models is the process of fitting the parameters of a model such that it generates synthetic data similar to the measurements. The traditional calibration approach involves, first, extracting the multipath components, then, grouping them into clusters, and finally, estimating the model parameters. In this paper, we propose to use approximate Bayesian computation (ABC) to calibrate stochastic channel models so as to bypass the need for multipath extraction and clustering. We apply the ABC method to calibrate the well-known Saleh-Valenzuela model and show its performance in simulations and using measured data. We find that the Saleh-Valenzuela model can be calibrated directly without the need for multipath extraction or clustering.

KW - stochastic channel model

KW - Bayesian

KW - Parameter Estimation

KW - multipath

M3 - Article in proceeding

BT - 2019 IEEE GLOBECOM Workshops

ER -