Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

Ayush Bharti, Ramoni Adeogun, Troels Pedersen

Abstract: Stochastic channel models are usually calibrated after extracting the parameters of the multipath components from measurements. This paper proposes a method to infer on the underlying parameters of a stochastic multipath model, in particular the Turin model, without resolving the multipath components. Channel measurements are summarised into temporal moments instead of the multipath parameters. The parameters of the stochastic model are then estimated from the observations of temporal moments using a method of moments approach. The estimator is tested on real data obtained from in-room channel measurements. It is concluded that calibration of stochastic models can be done without multipath extraction, and that temporal moments are informative summary statistics about the model parameters.