TY - GEN
T1 - Kalman-based time-varying sparse channel estimation
AU - Yoo, Jin Hyeok
AU - Sayed, Ali Irtaza
AU - Choi, Jun Won
AU - Shim, Byonghyo
N1 - 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.
PY - 2017/1/25
Y1 - 2017/1/25
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.
UR - http://www.scopus.com/inward/record.url?scp=85013904934&partnerID=8YFLogxK
U2 - 10.1109/ICCS.2016.7833599
DO - 10.1109/ICCS.2016.7833599
M3 - Conference contribution
AN - SCOPUS:85013904934
T3 - 2016 IEEE International Conference on Communication Systems, ICCS 2016
BT - 2016 IEEE International Conference on Communication Systems, ICCS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Communication Systems, ICCS 2016
Y2 - 14 December 2016 through 16 December 2016
ER -