A General Method for Calibrating Stochastic Radio Channel Models with Kernels

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.

Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo

“…This was studied by [15,20,21,3] in the context of MDE, and by [47,28,45,73,13] for the case where G θ is a neural network in particular. It was also used by [55,64,49,42,12] in the context of ABC. The two other discrepancies we will consider are the Wasserstein distance, as well as its relaxation called the Sinkhorn divergence.…”