Abstract
In this paper, a time-varying channel estimation method based on compressed sensing (CS) is studied to reduce the pilot overhead for orthogonal frequency division multiplexing (OFDM) system. By taking advantage of the dynamic characteristic and temporal correlation of time-varying channel, we propose a novel channel estimation scheme based on joint sparse-autoregressive (AR) model. The proposed method performs the following two steps in a sliding window strategy. Firstly, the channel delay structure is estimated using the proposed sparsity adaptive simultaneous orthogonal matching pursuit (SASOMP) algorithm. Secondly, with the channel delay estimation, a reduced order Kalman filter (KF) is performed to obtain the amplitude of channel. Simulation results indicate that the proposed method is capable of recovering the time-varying channel with much lower pilot overhead than conventional CS-based channel estimators with a superior estimation performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hao, J., Wang, J., Wu, Y.: A new equalizer in doubly-selective channels for TDS-OFDM. IEEE Trans. Broadcast. 61(1), 91–97 (2015)
Dahlman, E., Parkvall, S., Skld, J., et al.: 4G-LTE/LTE-advanced for mobile broadband 21(3), iv (2011)
Zheng, K., Zhao, L., Mei, J., Dohler, M., Xiang, W., Peng, Y.: 10 Gb/s hetsnets with millimeter-wave communications: access and networking-challenges and protocols. IEEE Commun. Mag. 53(1), 222–231 (2015)
Lee, Y.S., Bang, Y.J., Lee, J.H., Park, Y.O.: LS channel estimation performance analysis for RoF channel environment in the OFDM system. In: Proceedings of the COIN, pp. 1–2. Jeju (2014)
Hsieh, C.Y., Lin, D.W., Ma, C.: LMMSE-based channel estimation for LTE-advanced MIMO downlink employing UE-specific reference signals. In: Proceedings of the IEEE VTC, pp. 1–5. Glasgow (2015)
Kannu, A.P., Schniter, P.: On communication over unknown sparse frequency-selective block-fading channels. IEEE Trans. Inf. Theory 57(10), 6619–6632 (2011)
Bajwa, W.U., Haupt, J., Sayeed, A.M., Nowak, R.: Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc. IEEE 98(6), 1058–1076 (2010)
Son, N.T., Guo, S., Chen, H.: Impact of channel models on compressed sensing recovery algorithms-based ultra-wideband channel estimation. IET Commun. 7(13), 1322–1330 (2013)
Berger, C.R., Wang, Z., Huang, J., Zhou, S.: Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag. 48(11), 164–174 (2010)
Feng, T., Field, T.R., Haykin, S.: Stochastic differential equation theory applied to wireless channels. IEEE Trans. Commun. 55(8), 1478–1483 (2007)
Chen, B., Cui, Q., Yang, F., Xu, J.: A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system. In: Proceedings of the IEEE WCSP, pp. 1–5. Hefei (2014)
Wang, D., Niu, K., He, Z., Tian, B.: Pilot-aided channel estimation method based on compressed sensing and Kalman filtering in OFDM systems. In: Proceedings of the IEEE ICWITS, pp. 1–4. Honolulu, HI (2010)
Gao, Z., Zhang, C., Wang, Z., Chen, S.: Priori-information aided iterative hard threshold: a low-complexity high-accuracy compressive sensing based channel estimation for TDS-OFDM. IEEE Trans. Wirel. Commun. 14(1), 242–251 (2015)
Dai, L., Wang, J., Wang, Z., Tsiaflakis, P., Moonen, M.: Spectrum- and Energy-Efficient OFDM based on simultaneous multi-channel reconstruction. IEEE Trans. Signal Process. 61(23), 6047–6059 (2013)
Tropp, J., Gilbert, A.C., Strauss, M.J.: Simulataneous sparse approximation via greedy pursuit. In: Proceedings of the ICASSP, Philadelphia (2005)
Xie, H., Andrieux, G., Wang, Y., Diouris, J.F., Feng, S.: Threshold based most significant taps detection for sparse channel estimation in OFDM system. In: Proceedings of the IEEE ICSPCC, pp. 1–5. KunMing (2013)
Zyren, J.: Overview of the 3GPP long term evolution physical layer. Freescale Semiconductor (2007)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61271181 and 61571054) and the Project Funded by Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, S., You, K., Liu, Y., Guo, W. (2019). Joint Sparse-AR Model Based OFDM Compressed Sensing Time-Varying Channel Estimation. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_90
Download citation
DOI: https://doi.org/10.1007/978-981-13-6264-4_90
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6263-7
Online ISBN: 978-981-13-6264-4
eBook Packages: EngineeringEngineering (R0)