  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.
