Abstract
We propose Monte Carlo maximum likelihood estimation as a novel approach in the context of calibration and selection of stochastic channel models. First, considering a Turin channel model with inhomogeneous arrival rate as a prototypical example, we explain how the general statistical methodology is adapted and refined for the specific requirements and challenges of stochastic multipath channel models. Then, we illustrate the advantages and pitfalls of the method based on simulated data. Finally, we apply our calibration method to wideband signal data from indoor channels.
Highlights
S TOCHASTIC multipath models are indispensable for simulating and analyzing radio systems for communication and localization
Considering a parametric Turin model for simulated and real data, we demonstrate the feasibility of Markov chain Monte Carlo Maximum likelihood estimation (MLE) (MCMC MLE) for model calibration
The developed calibration method for stochastic multipath radio channel models is based on the well-established method of MLE
Summary
S TOCHASTIC multipath models are indispensable for simulating and analyzing radio systems for communication and localization. The parameters of the underlying point process are estimated from the obtained multipath components This data reduction step was employed due to technical limitations of the measurement equipment and data processing used by Turin at that time, many works have since adopted and expanded upon this calibration method [4]–[10]. Calibration techniques that do not require multipath extraction but rely on summarizing the data into a set of statistics have been introduced recently in the literature [19]–[23] These methods call for definition of appropriate summary statistics that are informative regarding the model parameters. We propose to use the principled and recognized statistical methodology of maximum likelihood estimation (MLE) to calibrate stochastic channel models with inhomogeneous intensity function.
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