Calibration of Stochastic Channel Models Using Approximate Bayesian Computation

Abstract

Calibration of stochastic radio channel models is the process of fitting the parameters of a model such that it generates synthetic data similar to the measurements. The traditional calibration approach involves, first, extracting the multipath components, then, grouping them into clusters, and finally, estimating the model parameters. In this paper, we propose to use approximate Bayesian computation (ABC) to calibrate stochastic channel models so as to bypass the need for multipath extraction and clustering. We apply the ABC method to calibrate the well-known Saleh-Valenzuela model and show its performance in simulations and using measured data. We find that the Saleh-Valenzuela model can be calibrated directly without the need for multipath extraction or clustering.