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

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Kadir Has University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	International Telecommunications Conference - Proceedings of the ITelCon 2017
Editörler	Ali Boyaci, Ali Riza Ekti, Muhammed Ali Aydin, Serhan Yarkan
Yayınlayan	Springer Verlag
Sayfalar	289-298
Sayfa sayısı	10
ISBN (Basılı)	9789811304071
DOI'lar	https://doi.org/10.1007/978-981-13-0408-8_25
Yayın durumu	Yayınlandı - 2019
Etkinlik	1st International Telecommunications Conference, ITelCon 2017 - İstanbul, Türkiye Süre: 28 Ara 2017 → 29 Ara 2017

Yayın serisi

Adı	Lecture Notes in Electrical Engineering
Hacim	504
ISSN (Basılı)	1876-1100
ISSN (Elektronik)	1876-1119

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	1st International Telecommunications Conference, ITelCon 2017
Ülke/Bölge	Türkiye
Şehir	İstanbul
Periyot	28/12/17 → 29/12/17

Bibliyografik not

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

Finansman

Acknowledgements This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under project no. 114E298.

Finansörler	Finansör numarası
TUBITAK	114E298
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / Ali Boyaci ; Ali Riza Ekti ; Muhammed Ali Aydin ; Serhan Yarkan. Springer Verlag, 2019. pp. 289-298 (Lecture Notes in Electrical Engineering).

@inproceedings{672dadbf9d674b04846c195a5210ea35,

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.",

keywords = "Autoregressive model, Fast time-varying channel, Kalman, OFDM, OMP, Sparse channel tracking",

author = "B{\"u}y{\"u}k{\c s}ar, \{Ay{\c s}e Bet{\"u}l\} and Habib {\c S}enol and Serhat Erk{\"u}{\c c}{\"u}k and {\c C}ırpan, \{Hakan Ali\}",

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",

year = "2019",

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, hac. 504, Springer Verlag, pp. 289-298, 1st International Telecommunications Conference, ITelCon 2017, İstanbul, Türkiye, 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; Hac. 504).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm

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.

KW - Autoregressive model

KW - Fast time-varying channel

KW - Kalman

KW - OFDM

KW - OMP

KW - Sparse channel tracking

UR - https://www.scopus.com/pages/publications/85049957315

U2 - 10.1007/978-981-13-0408-8_25

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

M3 - Conference contribution

AN - SCOPUS:85049957315

SN - 9789811304071

T3 - Lecture Notes in Electrical Engineering

SP - 289

EP - 298

BT - International Telecommunications Conference - Proceedings of the ITelCon 2017

A2 - Boyaci, Ali

A2 - Ekti, Ali Riza

A2 - Aydin, Muhammed Ali

A2 - Yarkan, Serhan

PB - Springer Verlag

T2 - 1st International Telecommunications Conference, ITelCon 2017

Y2 - 28 December 2017 through 29 December 2017

ER -

Büyükşar AB, Şenol H, Erküçük S, Çırpan HA. Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm. In Boyaci A, Ekti AR, Aydin MA, Yarkan S, editörler, International Telecommunications Conference - Proceedings of the ITelCon 2017. Springer Verlag. 2019. p. 289-298. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-0408-8_25

Rapidly varying sparse channel tracking with hybrid Kalman-OMP algorithm

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap