A General Method for Calibrating Stochastic Radio Channel Models with Kernels

TL;DR

This paper introduces a likelihood-free calibration method for stochastic radio channel models using approximate Bayesian computation and maximum mean discrepancy, enabling automatic, distribution-based parameter estimation without complex clustering.

Contribution

It presents a novel, automatic calibration approach that bypasses clustering, providing full posterior distributions for model parameters in radio channel modeling.

Findings

Accurately estimates parameters of Saleh-Valenzuela and propagation graph models.

Effective on both simulated and real 60 GHz indoor measurement data.

Avoids high-resolution clustering algorithms.

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.