Kalman-based time-varying sparse channel estimation

Jin Hyeok Yoo; Ali Irtaza Sayed; Jun Won Choi; Byonghyo Shim

doi:10.1109/ICCS.2016.7833599

Kalman-based time-varying sparse channel estimation

Jin Hyeok Yoo, Ali Irtaza Sayed, Jun Won Choi, Byonghyo Shim

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

1 Citation (Scopus)

Abstract

In this paper, we investigate a problem of estimating time-varying sparse channel impulse response for wireless communications. We are primarily interested in the scenario where the support of channels (i.e., the location of nonzero elements in channel impulse response) rarely changes within a local period of time. The proposed channel estimator estimates both support and amplitudes of the channel impulse response in an iterative fashion using the expectation and maximization algorithm. In order to exploit the (temporal) joint sparsity as well as temporal correlation of the channel gains, the proposed channel estimator performs two steps 1) E-step: Kalman smoothing of channel gains under the sparsity constraint and 2) M-step: semidefinite relaxation (SDR) technique for estimating the common support of channel impulse responses. Numerical evaluation shows that the proposed method performs close to the Oracle-based Kalman smoother and outperforms the existing sparse channel estimators.

Original language	English
Title of host publication	2016 IEEE International Conference on Communication Systems, ICCS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509034239
DOIs	https://doi.org/10.1109/ICCS.2016.7833599
Publication status	Published - 2017 Jan 25
Event	2016 IEEE International Conference on Communication Systems, ICCS 2016 - Shenzhen, China Duration: 2016 Dec 14 → 2016 Dec 16

Publication series

Name	2016 IEEE International Conference on Communication Systems, ICCS 2016

Other

Other	2016 IEEE International Conference on Communication Systems, ICCS 2016
Country/Territory	China
City	Shenzhen
Period	16/12/14 → 16/12/16

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (NRF-2014R1A1A2055805) and the Human Resources Program in Energy Technology Evaluation and Planning(KETEP), granted financial resource from the Ministry of Trade, Industry and Energy, Republic of Korea (No. 20154030200900)

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/ICCS.2016.7833599

Cite this

Yoo, J. H., Sayed, A. I., Choi, J. W., & Shim, B. (2017). Kalman-based time-varying sparse channel estimation. In 2016 IEEE International Conference on Communication Systems, ICCS 2016 Article 7833599 (2016 IEEE International Conference on Communication Systems, ICCS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCS.2016.7833599

Kalman-based time-varying sparse channel estimation. / Yoo, Jin Hyeok; Sayed, Ali Irtaza; Choi, Jun Won et al.
2016 IEEE International Conference on Communication Systems, ICCS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7833599 (2016 IEEE International Conference on Communication Systems, ICCS 2016).

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

Yoo, JH, Sayed, AI, Choi, JW & Shim, B 2017, Kalman-based time-varying sparse channel estimation. in 2016 IEEE International Conference on Communication Systems, ICCS 2016., 7833599, 2016 IEEE International Conference on Communication Systems, ICCS 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on Communication Systems, ICCS 2016, Shenzhen, China, 16/12/14. https://doi.org/10.1109/ICCS.2016.7833599

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abstract = "In this paper, we investigate a problem of estimating time-varying sparse channel impulse response for wireless communications. We are primarily interested in the scenario where the support of channels (i.e., the location of nonzero elements in channel impulse response) rarely changes within a local period of time. The proposed channel estimator estimates both support and amplitudes of the channel impulse response in an iterative fashion using the expectation and maximization algorithm. In order to exploit the (temporal) joint sparsity as well as temporal correlation of the channel gains, the proposed channel estimator performs two steps 1) E-step: Kalman smoothing of channel gains under the sparsity constraint and 2) M-step: semidefinite relaxation (SDR) technique for estimating the common support of channel impulse responses. Numerical evaluation shows that the proposed method performs close to the Oracle-based Kalman smoother and outperforms the existing sparse channel estimators.",

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note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (NRF-2014R1A1A2055805) and the Human Resources Program in Energy Technology Evaluation and Planning(KETEP), granted financial resource from the Ministry of Trade, Industry and Energy, Republic of Korea (No. 20154030200900) Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Communication Systems, ICCS 2016 ; Conference date: 14-12-2016 Through 16-12-2016",

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N2 - In this paper, we investigate a problem of estimating time-varying sparse channel impulse response for wireless communications. We are primarily interested in the scenario where the support of channels (i.e., the location of nonzero elements in channel impulse response) rarely changes within a local period of time. The proposed channel estimator estimates both support and amplitudes of the channel impulse response in an iterative fashion using the expectation and maximization algorithm. In order to exploit the (temporal) joint sparsity as well as temporal correlation of the channel gains, the proposed channel estimator performs two steps 1) E-step: Kalman smoothing of channel gains under the sparsity constraint and 2) M-step: semidefinite relaxation (SDR) technique for estimating the common support of channel impulse responses. Numerical evaluation shows that the proposed method performs close to the Oracle-based Kalman smoother and outperforms the existing sparse channel estimators.

AB - In this paper, we investigate a problem of estimating time-varying sparse channel impulse response for wireless communications. We are primarily interested in the scenario where the support of channels (i.e., the location of nonzero elements in channel impulse response) rarely changes within a local period of time. The proposed channel estimator estimates both support and amplitudes of the channel impulse response in an iterative fashion using the expectation and maximization algorithm. In order to exploit the (temporal) joint sparsity as well as temporal correlation of the channel gains, the proposed channel estimator performs two steps 1) E-step: Kalman smoothing of channel gains under the sparsity constraint and 2) M-step: semidefinite relaxation (SDR) technique for estimating the common support of channel impulse responses. Numerical evaluation shows that the proposed method performs close to the Oracle-based Kalman smoother and outperforms the existing sparse channel estimators.

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Kalman-based time-varying sparse channel estimation

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