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
PublisherIEEE
Publication date5 Mar 2020
Article number9024563
ISBN (Print)978-1-7281-0961-9
ISBN (Electronic)978-1-7281-0960-2
DOIs
Publication statusPublished - 5 Mar 2020
Event2019 IEEE Globecom Workshops (GC Wkshps) - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019

Conference

Conference2019 IEEE Globecom Workshops (GC Wkshps)
CountryUnited States
CityWaikoloa
Period09/12/201913/12/2019

Keywords

  • stochastic channel model
  • Bayesian
  • Parameter Estimation
  • multipath

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