Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments
- Resource Type
- Conference
- Authors
- Bharti, Ayush; Adeogun, Ramoni; Pedersen, Troels
- Source
- 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Signal Processing Advances in Wireless Communications (SPAWC), 2019 IEEE 20th International Workshop on. :1-5 Jul, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Delays
Method of moments
Frequency-domain analysis
Wireless communication
Stochastic processes
Reverberation
Mathematical model
stochastic channel model
multipath
summary statistics
parameter estimation
method of moments
- Language
- ISSN
- 1948-3252
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 multipath 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.