Bayesian Inference for Stochastic Multipath Radio Channel Models

Abstract

Stochastic radio channel models based on underlying point processes of multipath components have been studied intensively since the seminal papers of Turin and Saleh-Valenzuela. Despite of this, inference regarding parameters of these models has remained a major challenge. Current methods typically have a somewhat ad hoc flavor involving a multitude of steps requiring user specification of tuning parameters. In this paper, we propose to instead adopt the principled framework of Bayesian inference to conduct inference for the Saleh-Valenzuela model. The posterior distribution is not analytically tractable and we therefore compute approximations of the posterior using Markov chain Monte Carlo (MCMC) methods specific to point processes. To demonstrate the flexibility of our approach, we additionally propose a new multipath model and apply our inference method to it. The resulting inference methodology is computationally demanding and our successful implementation relies critically on our novel multipath component updates within the MCMC sampler. We demonstrate the usefulness of our approach on simulated and real radio channel data.

Introductory Chapter: A Strong Come-Back of Bayesian Inference

“…The first of these studies was chosen from communication theory. Bayesian inference has been conducted for simulating radio channels on a newly proposed stochastic multipath model to approximate the analytically intractable posterior distribution as likelihood function by introducing a novel Markov Chain Monte Carlo technique to improve the efficiency of Monte Carlo computations [5].…”