research-article

Structured Compressive Sensing Based Block-Sparse Channel Estimation for MIMO-OFDM Systems

Authors:

Wei ChengAuthors Info & Claims

Volume 108, Issue 4

Pages 2279 - 2309

https://doi.org/10.1007/s11277-019-06522-8

Published: 01 October 2019 Publication History

Abstract

In this paper, a compressive sensing based method named Priori-Information Aided Modified-SAMP algorithm is proposed to solve the problem of channel estimation in MIMO-OFDM systems. Firstly, coarse channel state information (CSI) as a priori-information of channel is obtained by using the complete pseudo-random noise (PN) sequences. Due to noise and the interference among antennas caused by the non-orthogonality of PN sequences, then, the accuracy of channel estimation is not so high that the priori-information aided modified-SAMP algorithm based on the obtained CSI is proposed to estimate CSI more accurately in temporal domain. Though the proposed method is based on the sparsity adaptive matching pursuit (SAMP) algorithm, there are some significant differences with each other in signal structure, support set selection, and adaptive step size etc. Theoretical analysis shows that the proposed algorithm has good convergence, moderate computational complexity and less training sequence overhead. Finally, the performance of the proposed method is verified through experimental simulations which show that compared with other algorithms, especially the orthogonal matching pursuit algorithm, the proposed algorithm not only improves the estimation accuracy but also greatly reduces the training sequence overhead.

References

[1]

Ozdemir, M. K., & Arslan, H. (2007). Channel estimation for wireless OFDM systems. IEEE Communications Surveys and Tutorials, 9(2), 18–48. (second quarter).

Digital Library

[2]

Larsson, E. G., Tufvesson, F., Edfors, O., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186–195.

[3]

Marzetta, T. L. (2015). Massive MIMO: An introduction. Bell Labs Technical Journal, 20, 11–12.

[4]

Dai, L., Wang, Z., & Yang, Z. (2013). Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems. IEEE Journal on Selected Areas in Communications, 31(2), 251–263.

[5]

Bajwa, W., Haupt, J., Sayeed, A., & Nowak, R. (2010). Compressed channel sensing: A new approach to estimating sparse multipath channels. Proceedings of the IEEE, 98(6), 1058–1076.

[6]

Choi, J. W., Shim, B., Ding, Y., Rao, B., & Kim, D. I. (2017). Compressed sensing for wireless communications: Useful tips and tricks. IEEE Communications Surveys and Tutorials, 19(3), 1527–1550. (third quarter).

Digital Library

[7]

Tropp, Joel A. (2004). Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 50(10), 2231–2242.

Digital Library

[8]

Dai, W., & Milenkovic, O. (2009). Subspace pursuit for compressive sensing signal reconstruction. IEEE Transactions on Information Theory, 55(5), 2230–2249.

Digital Library

[9]

Do, T., Lu, G., Nam, N., & Tran, T. (2008). Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In Proceedings of 42nd Asilomar conference on signals, systems, and computers, Pacific Grove, CA, USA, Oct 2008 (pp. 581–587).

[10]

Dai, Linglong, Wang, Zhaocheng, & Yang, Zhixing. (2013). Compressive sensing based time domain synchronous OFDM transmission for vehicular communications. IEEE Journal on Selected Areas in Communications, 31(9), 460–469.

[11]

Dai, L., & Wang, J. (2013). Spectrum- and energy-efficient OFDM based on simultaneous multi-channel reconstruction. IEEE Transactions on Signal Processing, 61(23), 6047–6059.

Digital Library

[12]

Ding, Wenbo, Yang, Fang, Dai, Wei, & Song, Jian. (2015). Time–frequency joint sparse channel estimation for MIMO-OFDM systems. IEEE Communications Letters, 19(1), 58–61.

[13]

Gao, Z., Dai, L., Wang, Z., & Chen, S. (2015). Priori-information aided iterative hard threshold: A low-complexity high-accuracy compressive sensing based channel estimation for TDS-OFDM. IEEE Transactions on Wireless Communications, 14(1), 242–251.

[14]

Rao, X., & Lau, V. K. N. (2015). Compressive sensing with prior support quality information and application to massive MIMO channel estimation with temporal correlation. IEEE Transactions on Signal Processing, 63(18), 4914–4924.

Digital Library

[15]

Ding, W., Yang, F., Liu, S., & Song, J. (2016). Structured compressive sensing-based non-orthogonal time-domain training channel state information acquisition for multiple input multiple output systems. IET Communs, 10(6), 685–690.

[16]

Gao, Z., Dai, L., Wang, Z., Dai, W., Shim, B., & Wang, Z. (2016). Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO. IEEE Transactions on Communications, 64(2), 601–617.

[17]

Duarte, M., & Eldar, Y. (2011). Structured compressed sensing: From theory to applications. IEEE Transactions on Signal Processing, 59(9), 4053–4085.

Digital Library

[18]

Wimalajeewa, Thakshila, Eldar, Yonina C., & Varshney, Pramod K. (2015). Subspace recovery from structured union of subspaces. IEEE Transactions on Information Theory, 61(4), 2101–2114.

Digital Library

[19]

Stojnic, M., Parvaresh, F., & Hassibi, B. (2008). On the reconstruction of block-sparse signals with an optimal number of measurements. IEEE Transactions on Signal Processing, 59(9), 3075–3085.

[20]

Eldar, Yonina C., Kuppinger, Patrick, & Bölcskei, Helmut. (2010). Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing, 58(6), 3042–3054.

Digital Library

[21]

Wan, F., Zhu, W. P., & Swamy, M. N. S. (2010). Semi-blind most significant tap detection for sparse channel estimation of OFDM systems. EEE Transactions on Circuits and Systems, 57(3), 703–713.

Digital Library

[22]

Telatar, I., & Tse, D. (2000). Capacity and mutual information of wideband multipath fading channels. IEEE Transactions on Information Theory, 46(4), 1384–1400.

Digital Library

[23]

Zhang, Y., Venkatesan, R., Dobre, O. A., & Li, C. (2016). Novel compressed sensing-based channel estimation algorithm and near-optimal pilot placement scheme. IEEE Transactions on Wireless Communications, 15(4), 2590–2603.

Digital Library

[24]

Han, Yonghee, Lee, Jungwoo, & Love, David J. (2017). Compressed sensing-aided downlink channel training for FDD massive MIMO systems. IEEE Transactions on Communications, 65(7), 2852–2862.

[25]

Li, C.-P., & Huang, W.-C. (2007). A constructive representation for the fourier dual of the Zadoff-Chu sequences. IEEE Transactions on Information Theory, 53(11), 4221–4224.

Digital Library

[26]

Zhou, Xiao, Yang, Fang, & Song, Jian. (2012). Novel transmit diversity scheme for TDS-OFDM system with frequency-shift m-sequence padding. IEEE Transactions on Broadcasting, 58(2), 317–324.

[27]

Ma, Xu, Yang, Fang, Ding, Wenbo, & Song, Jian. (2016). Novel approach to design time-domain training sequence for accurate sparse channel estimation. IEEE Transactions on Broadcasting, 62(3), 512–520.

[28]

Chen, Jie, & Huo, Xiaoming. (2006). Theoretical results on sparse representations of multiple-measurement vectors. IEEE Transactions on Signal Processing, 54(12), 4634–4643.

Digital Library

[29]

Gao, Zhen, Dai, Linglong, Wang, Zhaocheng, & Chen, Sheng. (2015). Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Transactions on Signal Processing, 63(23), 6169–6183.

Digital Library

Cited By

Affum EAdigun MBoateng KAjagbe SAddo E(2022)Enhancing UAV Communication Performance: Analysis Using Interference Based Geometry Stochastic Model and Successive Interference CancellationComputational Science and Its Applications – ICCSA 202210.1007/978-3-031-10522-7_17(232-245)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-10522-7_17

Index Terms

Structured Compressive Sensing Based Block-Sparse Channel Estimation for MIMO-OFDM Systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Pilot Optimization for Structured Compressive Sensing Based Channel Estimation in Large-Scale MIMO Systems with Superimposed Pilot Pattern

Compressive sensing (CS) has attracted much attention in wireless communications due to its ability to attain acceptable channel estimates with a small number of pilots. To further reduce the pilot overhead in multi-input multi-output (MIMO) systems, CS-...
Pilot-assisted channel estimation for STBC-based wireless MIMO-OFDM systems
IWCMC '07: Proceedings of the 2007 international conference on Wireless communications and mobile computing

With equipping multiple antennas at both transmitter and receiver ends, the desired signals in the OFDM wireless communications could be transmitted and received through multiple uncorrelated channels for achieving twofold benefits and high flexibility. ...
Pilot-symbol-assisted channel estimation for space-time coded OFDM systems

Space-time coded orthogonal frequency division multiplexing (OFDM) transmitter diversity techniques have been shown to provide an efficient means of achieving near optimal diversity gain in frequency-selective fading channels. For these systems, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Wireless Personal Communications: An International Journal

Wireless Personal Communications: An International Journal Volume 108, Issue 4

Oct 2019

637 pages

ISSN:0929-6212

Issue’s Table of Contents

Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Affum EAdigun MBoateng KAjagbe SAddo E(2022)Enhancing UAV Communication Performance: Analysis Using Interference Based Geometry Stochastic Model and Successive Interference CancellationComputational Science and Its Applications – ICCSA 202210.1007/978-3-031-10522-7_17(232-245)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-10522-7_17

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents