@inproceedings{33c39426f70c4d4d92fe8f5e718a88f4,
title = "Bayesian Synthetic Likelihood for Calibration of Stochastic Radio Channel Model",
abstract = "This paper presents a novel Bayesian Synthetic Likelihood (BSL) method for calibration of stochastic radio channels without multi path parameter estimation. To calibrate a stochastic channel model, we apply a Markov Chain Monte Carlo (MCMC) algorithm with a Metropolis accept/reject criterion and synthetic likelihood obtained from data generated using the model. The proposed method is applied to calibrate the Turin model and the polarized propagation graph model. Simulation examples show that the BSL method yield similar calibration accuracy to the state-of-the-art method based on Approximate Bayesian Computation (ABC). ",
keywords = "Bayesian Synthetic Likelihood, Machine Learning, Radio propagation, model calibration",
author = "Adeogun, {Ramoni Ojekunle} and Larsen, {Claus Meyer} and Dennis Sand and Bovbjerg, {Holger Severin} and Fisker, {Peter Kj{\ae}r} and {K. Gjerde}, Tor",
year = "2021",
doi = "10.1109/VTC2021-Fall52928.2021.9625382",
language = "English",
isbn = "978-1-6654-1369-5",
series = "IEEE Vehicular Technology Conference. Proceedings",
publisher = "IEEE",
booktitle = "IEEE 94th Vehicular Technology Conference (VTC2021-Fall)",
address = "United States",
note = "2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) ; Conference date: 27-09-2021 Through 30-09-2021",
}