Time-varying channel equalization in underwater acoustic OFDM communication system

Anfu Zhu

doi:10.3103/S0735272720080038

Authors

Anfu Zhu North China University of Water Resources and Electric Power, Zhengzhou, China https://orcid.org/0000-0003-3412-264X

DOI:

https://doi.org/10.3103/S0735272720080038

Keywords:

underwater acoustic communication, UWA, OFDM, time-varying channel equalization, LDL^H decomposition

Abstract

In this paper, three time-varying channel equalization schemes are studied in the underwater acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication system. The equalization algorithms are the zero-forcing (ZF) equalization algorithm, and the minimum mean square error equalization (MMSE) algorithm and the serial interference cancellation (SIC) equalization algorithm. Among the schemes, there is a problem of needing a large amount of operation when obtaining the inversion of the channel matrix. Then, to reduce the computation complexity of channel matrix inversion, the band approximation of the channel matrix, the serial equalization and the LDL^H decomposition are also studied. To evaluate the efficacy of the algorithms studied in this paper, numerical simulation and the field experiment are both conducted. The simulation results proof that each equalization algorithm can work appropriately under different time-varying conditions, and valid the reliability of each simplified algorithm under the same Doppler factor. The results of two sets of field experiment also prove that the simplified algorithm eliminates the influence of the residual narrow band Doppler to a certain extent, and a better effect is obtained while a channel estimation algorithm with higher accuracy is combined.

References

M. K. Tsatsanis, G. B. Giannakis, “Adaptive methods for equalization of rapidly fading channels,” in Proceedings of MILCOM ’93 - IEEE Military Communications Conference, 2002, vol. 2, pp. 639–643, doi: https://doi.org/10.1109/MILCOM.1993.408590.

I. Barhumi, G. Leus, M. Moonen, “Estimation and direct equalization of doubly selective channels,” EURASIP J. Adv. Signal Process., vol. 2006, no. 1, p. 062831, 2006, doi: https://doi.org/10.1155/ASP/2006/62831.

S. Tomasin, A. Gorokhov, H. Yang, J.-P. Linnartz, “Iterative interference cancellation and channel estimation for mobile ofdm,” IEEE Trans. Wirel. Commun., vol. 4, no. 1, pp. 238–245, 2005, doi: https://doi.org/10.1109/TWC.2004.840194.

K. A. D. Teo, S. Ohno, “Optimal mmse finite parameter model for doubly-selective channels,” in GLOBECOM ’05. IEEE Global Telecommunications Conference, 2005, 2005, vol. 6, pp. 5 pp. – 3507, doi: https://doi.org/10.1109/GLOCOM.2005.1578424.

T. Zemen, C. F. Mecklenbrauker, “Time-variant channel estimation using discrete prolate spheroidal sequences,” IEEE Trans. Signal Process., vol. 53, no. 9, pp. 3597–3607, 2005, doi: https://doi.org/10.1109/TSP.2005.853104.

G. Taubock, F. Hlawatsch, “A compressed sensing technique for ofdm channel estimation in mobile environments: exploiting channel sparsity for reducing pilots,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 2885–2888, doi: https://doi.org/10.1109/ICASSP.2008.4518252.

J. Jin, Y. Gu, S. Mei, “An introduction to compressive sampling and its applications,” J. Electron. Inf. Technol., vol. 32, no. 2, pp. 470–475, 2010, doi: https://doi.org/10.3724/SP.J.1146.2009.00497.

Y. Zhou, Y. Wu, D. Chen, F. Tong, “Compressed sensing estimation of underwater acoustic mimo channels based on temporal joint sparse recovery,” Dianzi Yu Xinxi Xuebao/Journal Electron. Inf. Technol., vol. 38, no. 8, pp. 1920–1927, 2016, doi: https://doi.org/10.11999/JEIT151158.

H. Huang, W. Su, X. Jiang, “An improved compressed sensing reconstruction algorithm used in sparse channel estimation,” in 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2016, pp. 1–4, doi: https://doi.org/10.1109/ICSPCC.2016.7753737.

L. Jing, C. He, L. Zhang, Q. Meng, J. Huang, Q. Zhang, “Iterative block decision feedback equalizer with soft detection for underwater acoustic channels,” Dianzi Yu Xinxi Xuebao/Journal Electron. Inf. Technol., vol. 38, no. 4, pp. 885–891, 2016, doi: https://doi.org/10.11999/JEIT150669.

Y. Xie, L. Zhou, J. Liu, “Linearly time-varying channel estimation method with ici mitigation for ofdm systems,” Inf. Technol., no. 1, pp. 67–70, 2018, doi: https://doi.org/10.13274/j.cnki.hdzj.2018.01.016.

J. Zhao, K. Huo, Y. Liu, X. Yang, “Cyclic prefix based phase-coded ofdm radar doppler offset estimation and compensation,” Dianzi Yu Xinxi Xuebao/Journal Electron. Inf. Technol., vol. 39, no. 4, pp. 938–944, 2017, doi: https://doi.org/10.11999/JEIT160549.

S. Sarowa, H. Singh, S. Agrawal, B. S. Sohi, “A novel energy-efficient ici cancellation technique for bandwidth improvements through cyclic prefix reuse in an ofdm system,” Front. Inf. Technol. Electron. Eng., vol. 18, no. 11, pp. 1892–1899, 2017, doi: https://doi.org/10.1631/FITEE.1601333.

T. Hrycak, G. Matz, “Low-complexity time-domain ici equalization for ofdm communications over rapidly varying channels,” in 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006, pp. 1767–1771, doi: https://doi.org/10.1109/ACSSC.2006.355065.

J. Huang, J. Huang, C. R. Berger, S. Zhou, P. Willett, “Iterative sparse channel estimation and decoding for underwater mimo-ofdm,” EURASIP J. Adv. Signal Process., vol. 2010, no. 1, p. 460379, 2010, doi: https://doi.org/10.1155/2010/460379.

L. Rugini, P. Banelli, G. Leus, “Simple equalization of time-varying channels for ofdm,” IEEE Commun. Lett., vol. 9, no. 7, pp. 619–621, 2005, doi: https://doi.org/10.1109/LCOMM.2005.1461683.