Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

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

doi:10.1109/tap.2020.3044379

“…In this paper, we propose to carry out approximate likelihood-based inference for the model parameters. As in [23], we apply Markov chain Monte Carlo (MCMC) techniques to handle the computational problems arising from the analytically intractable likelihood function. In the context of the Turin model [1], [23] suggested to obtain estimates by maximizing a Monte Carlo estimate of the likelihood function, integrating out the unobserved multipath components.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Stochastic Multipath Radio Channel Models

Hirsch

¹

,

Bharti

²

,

Pedersen

³

et al. 2023

IEEE Trans. Antennas Propagat.

1

0

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Stochastic radio channel models based on underlying point processes of multipath components have been studied intensively since the seminal papers of Turin and Saleh-Valenzuela. Despite of this, inference regarding parameters of these models has remained a major challenge. Current methods typically have a somewhat ad hoc flavor involving a multitude of steps requiring user specification of tuning parameters. In this paper, we propose to instead adopt the principled framework of Bayesian inference to conduct inference for the Saleh-Valenzuela model. The posterior distribution is not analytically tractable and we therefore compute approximations of the posterior using Markov chain Monte Carlo (MCMC) methods specific to point processes. To demonstrate the flexibility of our approach, we additionally propose a new multipath model and apply our inference method to it. The resulting inference methodology is computationally demanding and our successful implementation relies critically on our novel multipath component updates within the MCMC sampler. We demonstrate the usefulness of our approach on simulated and real radio channel data.

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“…They have been used to calibrate the Turin model [1], the Saleh-Valenzuela (S-V) model [2] and the polarized propagation graph (PG) model [15]. These calibration methods rely either on a Monte Carlo approximation of the likelihood [16], [17], the method of moments [18], [19], or a summarybased likelihood-free inference framework [20]- [23] such as approximate Bayesian computation (ABC). First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [24] for an overview.…”

Section: Introductionmentioning

confidence: 99%

“…First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [24] for an overview. The main drawback of the calibration methods [17]- [19] is their reliance on equations that explicitly link the moments of the summaries with the model parameters, or in case of [16], on the model-specific point process. These methods should therefore be re-derived for each new model.…”

Section: Introductionmentioning

confidence: 99%

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

Bharti¹,

Briol

²

,

Pedersen

³

2022

IEEE Trans. Antennas Propagat.

Self Cite

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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.

show abstract

“…They have been used to calibrate the Turin model [1], the Saleh-Valenzuela (S-V) model [2] and the polarized propagation graph (PG) model [15]. These calibration methods rely either on a Monte Carlo approximation of the likelihood [16], [17], the method of moments [18], [19], or a summarybased likelihood-free inference framework [20]- [22] such as approximate Bayesian computation (ABC). First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [23] for an overview.…”

Section: Introductionmentioning

confidence: 99%

“…First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [23] for an overview. The main drawback of the calibration methods [17]- [19] is their reliance on equations that explicitly link the moments of the summaries with the model parameters, or in case of [16], on the model-specific point process. These methods should therefore be re-derived for each new model.…”

Section: Introductionmentioning

confidence: 99%

A General Method for Calibrating Stochastic Radio Channel Models with Kernels

Bharti¹,

Briol²,

Pedersen³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

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Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models

Cited by 3 publications

References 33 publications

Bayesian Inference for Stochastic Multipath Radio Channel Models

Bayesian Inference for Stochastic Multipath Radio Channel Models

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

A General Method for Calibrating Stochastic Radio Channel Models with Kernels

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