Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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 channel 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.
Close

Details

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 channel 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.
Original languageEnglish
Title of host publication20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Number of pages5
Publication date2019
Publication statusSubmitted - 2019
Publication categoryResearch
Peer-reviewedYes
Event20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 - Cannes, France
Duration: 2 Jul 20195 Jul 2019

Conference

Conference20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
LandFrance
ByCannes
Periode02/07/201905/07/2019

    Research areas

  • stochastic channel model, multipath, summary statistics, Parameter estimation, method of moments
ID: 295162637