Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

Christian Hirsch, Ayush Bharti*, Troels Pedersen, Rasmus Waagepetersen

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

We propose Monte Carlo maximum likelihood estimation as a novel approach in the context of calibration and selection of stochastic channel models. First, considering a Turin channel model with inhomogeneous arrival rate as a prototypical example, we explain how the general statistical methodology is adapted and refined for the specific requirements and challenges of stochastic multipath channel models. Then, we illustrate the advantages and pitfalls of the method based on simulated data. Finally, we apply our calibration method to wideband signal data from indoor channels.

Original languageEnglish
Article number9298915
JournalIEEE Transactions on Antennas and Propagation
Volume69
Issue7
Pages (from-to)4058-4069
Number of pages12
ISSN0018-926X
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

Keywords

  • Delays
  • Stochastic processes
  • Calibration
  • Maximum likelihood estimation
  • Data models
  • Adaptation models
  • Power measurement
  • Maximum likelihood estimation (MLE)
  • Monte Carlo methods
  • multipath channels
  • point processes
  • radio propagation
  • shot noise
  • ENVIRONMENTS

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