Auto-Generated Summaries for Stochastic Radio Channel Models

Recently, a calibration method has been proposed for estimating the parameters of stochastic radio channel models using summaries of channel impulse response measurements without multipath extraction. In this paper, we attempt to automatically generate summaries using an autoencoder for calibration of channel models. This approach avoids the need for explicitly designing informative summaries about the model parameters, which can be tedious. We test the method by calibrating the stochastic polarized propagation graph model on simulated as well as measured data. The autoencoder is found to generate summaries that give reasonably accurate results while calibrating the considered model.