Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

Christian Hirsch; Ayush Bharti; Troels Pedersen; Rasmus Waagepetersen

doi:10.1109/TAP.2020.3044379

Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

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

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

2 Citationer (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.

Originalsprog	Engelsk
Artikelnummer	9298915
Tidsskrift	IEEE Transactions on Antennas and Propagation
Vol/bind	69
Nummer	7
Sider (fra-til)	4058-4069
Antal sider	12
ISSN	0018-926X
DOI	https://doi.org/10.1109/TAP.2020.3044379
Status	Udgivet - jul. 2021
Udgivet eksternt	Ja

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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.",

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",

author = "Christian Hirsch and Ayush Bharti and Troels Pedersen and Rasmus Waagepetersen",

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AU - Bharti, Ayush

AU - Pedersen, Troels

AU - Waagepetersen, Rasmus

PY - 2021/7

Y1 - 2021/7

N2 - 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.

AB - 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.

KW - Delays

KW - Stochastic processes

KW - Calibration

KW - Maximum likelihood estimation

KW - Data models

KW - Adaptation models

KW - Power measurement

KW - Maximum likelihood estimation (MLE)

KW - Monte Carlo methods

KW - multipath channels

KW - point processes

KW - radio propagation

KW - shot noise

KW - ENVIRONMENTS

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DO - 10.1109/TAP.2020.3044379

M3 - Journal article

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EP - 4069

JO - IEEE Transactions on Antennas and Propagation

JF - IEEE Transactions on Antennas and Propagation

IS - 7

M1 - 9298915

ER -

Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

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