Data-aided autoregressive sparse channel tracking for OFDM systems

Ayse Betul Buyuksar; Habib Senol; Serhat Erkucuk; Hakan Ali Cirpan

doi:10.1109/ISWCS.2016.7600941

Data-aided autoregressive sparse channel tracking for OFDM systems

Ayse Betul Buyuksar, Habib Senol, Serhat Erkucuk, Hakan Ali Cirpan

Department of Electronics and Communication Engineering

Kadir Has University

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

2 Citations (Scopus)

Abstract

In order to meet future communication system requirements, channel estimation over fast fading and frequency selective channels is crucial. In this paper, Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also, an initialization algorithm has been developed from the widely used sparse approximation algorithm Orthogonal Matching Pursuit (OMP), since the performance of SAGE algorithm strictly depends on initialization. The results show that multipath delay positions can be tracked successfully for every time instant using the proposed SAGE-MAP based approach.

Original language	English
Title of host publication	ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings
Publisher	VDE Verlag GmbH
Pages	424-428
Number of pages	5
ISBN (Electronic)	9781509020614
DOIs	https://doi.org/10.1109/ISWCS.2016.7600941
Publication status	Published - 19 Oct 2016
Event	13th International Symposium on Wireless Communication Systems, ISWCS 2016 - Poznan, Poland Duration: 20 Sept 2016 → 23 Sept 2016

Publication series

Name	Proceedings of the International Symposium on Wireless Communication Systems
Volume	2016-October
ISSN (Print)	2154-0217
ISSN (Electronic)	2154-0225

Conference

Conference	13th International Symposium on Wireless Communication Systems, ISWCS 2016
Country/Territory	Poland
City	Poznan
Period	20/09/16 → 23/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Autoregressive model
Fast Time-Varying
OFDM
OMP
SAGE-MAP
sparse channel estimation

Access to Document

10.1109/ISWCS.2016.7600941

Cite this

Buyuksar, A. B., Senol, H., Erkucuk, S., & Cirpan, H. A. (2016). Data-aided autoregressive sparse channel tracking for OFDM systems. In ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings (pp. 424-428). Article 7600941 (Proceedings of the International Symposium on Wireless Communication Systems; Vol. 2016-October). VDE Verlag GmbH. https://doi.org/10.1109/ISWCS.2016.7600941

@inproceedings{240fb3ab6bd0493c9eeef82c45365f59,

title = "Data-aided autoregressive sparse channel tracking for OFDM systems",

abstract = "In order to meet future communication system requirements, channel estimation over fast fading and frequency selective channels is crucial. In this paper, Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also, an initialization algorithm has been developed from the widely used sparse approximation algorithm Orthogonal Matching Pursuit (OMP), since the performance of SAGE algorithm strictly depends on initialization. The results show that multipath delay positions can be tracked successfully for every time instant using the proposed SAGE-MAP based approach.",

keywords = "Autoregressive model, Fast Time-Varying, OFDM, OMP, SAGE-MAP, sparse channel estimation",

author = "Buyuksar, {Ayse Betul} and Habib Senol and Serhat Erkucuk and Cirpan, {Hakan Ali}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th International Symposium on Wireless Communication Systems, ISWCS 2016 ; Conference date: 20-09-2016 Through 23-09-2016",

year = "2016",

month = oct,

day = "19",

doi = "10.1109/ISWCS.2016.7600941",

language = "English",

series = "Proceedings of the International Symposium on Wireless Communication Systems",

publisher = "VDE Verlag GmbH",

pages = "424--428",

booktitle = "ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings",

}

Buyuksar, AB, Senol, H, Erkucuk, S & Cirpan, HA 2016, Data-aided autoregressive sparse channel tracking for OFDM systems. in ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings., 7600941, Proceedings of the International Symposium on Wireless Communication Systems, vol. 2016-October, VDE Verlag GmbH, pp. 424-428, 13th International Symposium on Wireless Communication Systems, ISWCS 2016, Poznan, Poland, 20/09/16. https://doi.org/10.1109/ISWCS.2016.7600941

Data-aided autoregressive sparse channel tracking for OFDM systems. / Buyuksar, Ayse Betul; Senol, Habib; Erkucuk, Serhat et al.
ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings. VDE Verlag GmbH, 2016. p. 424-428 7600941 (Proceedings of the International Symposium on Wireless Communication Systems; Vol. 2016-October).

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

TY - GEN

T1 - Data-aided autoregressive sparse channel tracking for OFDM systems

AU - Buyuksar, Ayse Betul

AU - Senol, Habib

AU - Erkucuk, Serhat

AU - Cirpan, Hakan Ali

PY - 2016/10/19

Y1 - 2016/10/19

N2 - In order to meet future communication system requirements, channel estimation over fast fading and frequency selective channels is crucial. In this paper, Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also, an initialization algorithm has been developed from the widely used sparse approximation algorithm Orthogonal Matching Pursuit (OMP), since the performance of SAGE algorithm strictly depends on initialization. The results show that multipath delay positions can be tracked successfully for every time instant using the proposed SAGE-MAP based approach.

AB - In order to meet future communication system requirements, channel estimation over fast fading and frequency selective channels is crucial. In this paper, Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also, an initialization algorithm has been developed from the widely used sparse approximation algorithm Orthogonal Matching Pursuit (OMP), since the performance of SAGE algorithm strictly depends on initialization. The results show that multipath delay positions can be tracked successfully for every time instant using the proposed SAGE-MAP based approach.

KW - Autoregressive model

KW - Fast Time-Varying

KW - OFDM

KW - OMP

KW - SAGE-MAP

KW - sparse channel estimation

UR - http://www.scopus.com/inward/record.url?scp=84994355371&partnerID=8YFLogxK

U2 - 10.1109/ISWCS.2016.7600941

DO - 10.1109/ISWCS.2016.7600941

M3 - Conference contribution

AN - SCOPUS:84994355371

T3 - Proceedings of the International Symposium on Wireless Communication Systems

SP - 424

EP - 428

BT - ISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings

PB - VDE Verlag GmbH

T2 - 13th International Symposium on Wireless Communication Systems, ISWCS 2016

Y2 - 20 September 2016 through 23 September 2016

ER -

Data-aided autoregressive sparse channel tracking for OFDM systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this