Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

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Resumé

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.
OriginalsprogEngelsk
Titel20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Antal sider5
Publikationsdato2019
StatusAccepteret/In press - 2019
Begivenhed20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 - Cannes, Frankrig
Varighed: 2 jul. 20195 jul. 2019

Konference

Konference20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
LandFrankrig
ByCannes
Periode02/07/201905/07/2019

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parameter
calibration
method
statistics

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    Bharti, A., Adeogun, R., & Pedersen, T. (Accepteret/In press). Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. I 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
    Bharti, Ayush ; Adeogun, Ramoni ; Pedersen, Troels. / Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019.
    @inproceedings{4e6ba3f3612547ddabe991d41379b00f,
    title = "Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments",
    abstract = "Stochastic channel models are usually calibratedafter 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.",
    keywords = "stochastic channel model, multipath, summary statistics, Parameter estimation, method of moments",
    author = "Ayush Bharti and Ramoni Adeogun and Troels Pedersen",
    year = "2019",
    language = "English",
    booktitle = "20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019",

    }

    Bharti, A, Adeogun, R & Pedersen, T 2019, Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. i 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019., Cannes, Frankrig, 02/07/2019.

    Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. / Bharti, Ayush; Adeogun, Ramoni; Pedersen, Troels.

    20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019.

    Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

    TY - GEN

    T1 - Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

    AU - Bharti, Ayush

    AU - Adeogun, Ramoni

    AU - Pedersen, Troels

    PY - 2019

    Y1 - 2019

    N2 - Stochastic channel models are usually calibratedafter 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.

    AB - Stochastic channel models are usually calibratedafter 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.

    KW - stochastic channel model

    KW - multipath

    KW - summary statistics

    KW - Parameter estimation

    KW - method of moments

    M3 - Article in proceeding

    BT - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019

    ER -

    Bharti A, Adeogun R, Pedersen T. Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments. I 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019. 2019