A General Method for Calibrating Stochastic Radio Channel Models with Kernels

Ayush Bharti, Francois Xavier Briol, Troels Pedersen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.

Original languageEnglish
JournalIEEE Transactions on Antennas and Propagation
DOIs
Publication statusE-pub ahead of print - 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • approximate Bayesian computation
  • Calibration
  • calibration
  • Channel models
  • Computational modeling
  • Data models
  • Frequency measurement
  • Kernel
  • kernel methods
  • likelihood-free inference
  • machine learning
  • maximum mean discrepancy
  • radio channel modeling
  • Stochastic processes

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