Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm

Ayşe Betül Büyükşar; Habib Şenol; Serhat Erküçük; Hakan Ali Çırpan

doi:10.1007/978-981-13-0408-8_25

Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm

Ayşe Betül Büyükşar^*, Habib Şenol, Serhat Erküçük, Hakan Ali Çırpan

^*Corresponding author for this work

Department of Electronics and Communication Engineering

Kadir Has University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore, in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels, orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also, by using windows, sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.

Original language	English
Title of host publication	International Telecommunications Conference - Proceedings of the ITelCon 2017
Editors	Ali Boyaci, Ali Riza Ekti, Muhammed Ali Aydin, Serhan Yarkan
Publisher	Springer Verlag
Pages	289-298
Number of pages	10
ISBN (Print)	9789811304071
DOIs	https://doi.org/10.1007/978-981-13-0408-8_25
Publication status	Published - 2019
Event	1st International Telecommunications Conference, ITelCon 2017 - İstanbul, Turkey Duration: 28 Dec 2017 → 29 Dec 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	504
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	1st International Telecommunications Conference, ITelCon 2017
Country/Territory	Turkey
City	İstanbul
Period	28/12/17 → 29/12/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.

Keywords

Autoregressive model
Fast time-varying channel
Kalman
OFDM
OMP
Sparse channel tracking

Access to Document

10.1007/978-981-13-0408-8_25

Cite this

Büyükşar, A. B., Şenol, H., Erküçük, S., & Çırpan, H. A. (2019). Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm. In A. Boyaci, A. R. Ekti, M. A. Aydin, & S. Yarkan (Eds.), International Telecommunications Conference - Proceedings of the ITelCon 2017 (pp. 289-298). (Lecture Notes in Electrical Engineering; Vol. 504). Springer Verlag. https://doi.org/10.1007/978-981-13-0408-8_25

Büyükşar, Ayşe Betül ; Şenol, Habib ; Erküçük, Serhat et al. / Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm. International Telecommunications Conference - Proceedings of the ITelCon 2017. editor / Ali Boyaci ; Ali Riza Ekti ; Muhammed Ali Aydin ; Serhan Yarkan. Springer Verlag, 2019. pp. 289-298 (Lecture Notes in Electrical Engineering).

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title = "Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm",

abstract = "It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore, in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels, orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also, by using windows, sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.",

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note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 1st International Telecommunications Conference, ITelCon 2017 ; Conference date: 28-12-2017 Through 29-12-2017",

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doi = "10.1007/978-981-13-0408-8_25",

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Büyükşar, AB, Şenol, H, Erküçük, S & Çırpan, HA 2019, Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm. in A Boyaci, AR Ekti, MA Aydin & S Yarkan (eds), International Telecommunications Conference - Proceedings of the ITelCon 2017. Lecture Notes in Electrical Engineering, vol. 504, Springer Verlag, pp. 289-298, 1st International Telecommunications Conference, ITelCon 2017, İstanbul, Turkey, 28/12/17. https://doi.org/10.1007/978-981-13-0408-8_25

Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm. / Büyükşar, Ayşe Betül; Şenol, Habib; Erküçük, Serhat et al.
International Telecommunications Conference - Proceedings of the ITelCon 2017. ed. / Ali Boyaci; Ali Riza Ekti; Muhammed Ali Aydin; Serhan Yarkan. Springer Verlag, 2019. p. 289-298 (Lecture Notes in Electrical Engineering; Vol. 504).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Büyükşar, Ayşe Betül

AU - Şenol, Habib

AU - Erküçük, Serhat

AU - Çırpan, Hakan Ali

PY - 2019

Y1 - 2019

N2 - It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore, in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels, orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also, by using windows, sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.

AB - It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore, in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels, orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also, by using windows, sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.

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BT - International Telecommunications Conference - Proceedings of the ITelCon 2017

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T2 - 1st International Telecommunications Conference, ITelCon 2017

Y2 - 28 December 2017 through 29 December 2017

ER -

Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm

Abstract

Publication series

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Keywords

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