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A General Method for Calibrating Stochastic Radio Channel Models with Kernels

Bharti, A; Briol, FX; Pedersen, T; (2021) A General Method for Calibrating Stochastic Radio Channel Models with Kernels. IEEE Transactions on Antennas and Propagation 10.1109/TAP.2021.3083761. (In press). Green open access

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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.

Type: Article
Title: A General Method for Calibrating Stochastic Radio Channel Models with Kernels
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TAP.2021.3083761
Publisher version: https://doi.org/10.1109/TAP.2021.3083761
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Keywords: Radio channel modeling, machine learning, approximate Bayesian computation, kernel methods, maximum mean discrepancy, likelihood-free inference, calibration
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10130101
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