TY - JOUR
T1 - Bayesian Inference for Stochastic Multipath Radio Channel Models
AU - Hirsch, Christian
AU - Bharti, Ayush
AU - Pedersen, Troels
AU - Waagepetersen, Rasmus
N1 - Publisher Copyright:
Author
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Stochastic radio channel models based on underlying point processes of multipath components (MPCs) have been studied intensively since the seminal papers of Turin and Saleh-Valenzuela (SV). Despite 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 article, we propose to instead adopt the principled framework of Bayesian inference to conduct inference for the SV 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 MPC updates within the MCMC sampler. We demonstrate the usefulness of our approach on simulated and real radio channel data.
AB - Stochastic radio channel models based on underlying point processes of multipath components (MPCs) have been studied intensively since the seminal papers of Turin and Saleh-Valenzuela (SV). Despite 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 article, we propose to instead adopt the principled framework of Bayesian inference to conduct inference for the SV 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 MPC updates within the MCMC sampler. We demonstrate the usefulness of our approach on simulated and real radio channel data.
KW - Bayesian inference
KW - Markov chain Monte Carlo (MCMC) sampling
KW - multipath propagation
KW - parameter estimation
KW - radio channel modeling
UR - http://www.scopus.com/inward/record.url?scp=85149413715&partnerID=8YFLogxK
U2 - 10.1109/TAP.2022.3215820
DO - 10.1109/TAP.2022.3215820
M3 - Journal article
AN - SCOPUS:85149413715
SN - 0018-926X
VL - 71
SP - 3460
EP - 3472
JO - IEEE Transactions on Antennas and Propagation
JF - IEEE Transactions on Antennas and Propagation
IS - 4
ER -