A General Method for Calibrating Stochastic Radio Channel Models with Kernels

Characterization of the environment in which communication is taking place, termed the \emph{radio channel}, is imperative for the design and analysis of communication systems. Stochastic models of the radio channel are widely used simulation tools that construct a probabilistic model of the radio channel. Calibrating these 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 instead an approximate Bayesian computation algorithm based on the maximum mean discrepancy with a kernel careful crafted for this task. The proposed method is able to estimate the parameters of the model accurately in simulations, and has the advantage that it can be used on a wide range of models.