|Table of Contents|

Citation:
　Xiukun Li,Ji Wang,Dexin Zhao.Enhancement Channel Estimation Using Outer-Product Decomposition Algorithm Based on Frequency Transformation[J].Journal of Marine Science and Application,2020,(2):283-292.[doi:10.1007/s11804-020-00148-0]
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Enhancement Channel Estimation Using Outer-Product Decomposition Algorithm Based on Frequency Transformation

Info

Title:
Enhancement Channel Estimation Using Outer-Product Decomposition Algorithm Based on Frequency Transformation
Author(s):
Xiukun Li1; 2; 3;  Ji Wang1; 2; 3;  Dexin Zhao4
Affilations:
Author(s):
Xiukun Li1; 2; 3;  Ji Wang1; 2; 3;  Dexin Zhao4
1 Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China;
2 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
Blind identification; Outer-product decomposition algorithm; Bandpass white signal; Chirp signal; Second-order statistics
分类号:
-
DOI:
10.1007/s11804-020-00148-0
Abstract:
The outer-product decomposition algorithm (OPDA) performs well at blindly identifying system function. However, the direct use of the OPDA in systems using bandpass source will lead to errors. This study proposes an approach to enhance the channel estimation quality of a bandpass source that uses OPDA. This approach performs frequency domain transformation on the received signal and obtains the optimal transformation parameter by minimizing the p-norm of an error matrix. Moreover, the proposed approach extends the application of OPDA from a white source to a bandpass white source or chirp signal. Theoretical formulas and simulation results show that the proposed approach not only reduces the estimation error but also accelerates the algorithm in a bandpass system, thus being highly feasible in practical blind system identification applications.

References:

A.-J V, Paulraj A, Veen V (1996) An analytical constant modulus algorithm. IEEE Trans Signal Process 44(5):1136-1155. https://doi.org/10.1109/78.502327
Abdallah S, Psaromiligkos IN (2011) Blind channel estimation for amplify-and-forward two-way relay networks employing M-PSK modulation. IEEE Trans Signal Process 60(7):3604-3615. https://doi.org/10.1109/TSP.2012.2193577
Abed-Meraim K, Loubaton P, Moulines E (2002) A subspace algorithm for certain blind identification problems. IEEE Trans Inf Theory 43(2):499-511. https://doi.org/10.1109/18.556108
Boussé M, Debals O, Lathauwer LD (2017) Tensor-based large-scale blind system identification using segmentation. IEEE Trans Signal Process 99:1-1. https://doi.org/10.1109/EUSIPCO.2016.7760602
Comon P, Luciani X, Stegeman A (2012) CONFAC decomposition approach to blind identification of underdetermined mixtures based on generating function derivatives. IEEE Trans Signal Process 60(11):5698-5713. https://doi.org/10.1109/tsp.2012.2208956
Ding Z (1996) An outer-product decomposition algorithm for multichannel blind identification. IEEE Signal Processing Workshop on Statistical Signal and Array Processing 8:132-135. https://doi.org/10.1109/SSAP.1996.534836
Ding Z (1997) Matrix outer-product decomposition method for blind multiple channel identification. IEEE Trans Signal Process 45(12):3054-3061. https://doi.org/10.1109/78.650264
Duhamel P, Gesbert D (1997) Robust blind identification and equalization based on multi-step predictors. IEEE Trans Signal Process 45(12):3054-3061. https://doi.org/10.1109/ICASSP.1997.604650
Friedlander B, Porat B (1990) Asymptotically optimal estimation of MA and ARMA parameters of non-Gaussian processes from high-order moments. IEEE Trans Autom Control 35(1):27-35. https://doi.org/10.1109/9.45140
Gao C, Tai L, Zhuang J (2009) Underwater acoustic channel blind identification based on matrix outer-product decomposition algorithm. 2009 2nd International Congress on Image and Signal Processing, Tianjin pp. 1-4. https://doi.org/10.1109/CISP.2009.5304059
Ghofrani P, Schmeink A, Wang T (2018) A fast converging channel estimation algorithm for wireless sensor networks. IEEE Trans Signal Process (99):1-1. https://doi.org/10.1109/TSP.2018.2813301
Golub G, Kahan W (1965) Calculating the singular values and pseudo-inverse of a matrix. SIAM J Num Anal Ser B 2(2):205-224. https://doi.org/10.1137/0702016
Jia S, Meng W, Zhao J (2009) Sparse underwater acoustic OFDM channel estimation based on superimposed training. J Mar Sci Appl (1):65-70. https://doi.org/10.1007/s11804-009-8015-2
Kailath T, Tong L, Xu G (1994) Blind identification and equalization based on second-order statistics: a time domain approach. IEEE transactions on information theory 340-349. https://doi.org/10.1109/18.312157
Karakutuk S, Tuncer TE (2011) Channel matrix recursion for blind effective channel order estimation. IEEE Trans Signal Process 59(4):1642-1653. https://doi.org/10.1109/tsp.2010.2100384
Nayebi E, Rao BD (2018) Semi-blind channel estimation for multi-user massive MIMO systems. IEEE Trans Signal Process 99:1-1. https://doi.org/10.1109/TSP.2017.2771725
Qiao G, Ma L, Babar Z (2017) MIMO-OFDM underwater acoustic communication systems-a review. Phys Commun 23:56-64. https://doi.org/10.1016/j.phycom.2017.02.007
Sato Y (1975) A method of self-recovery equalization for multilevel amplitude-modulation. IEEE Trans Commun 23:679-682. https://doi.org/10.1109/TCOM.1975.1092854
Tong L, Zhao Q (1999) Blind channel estimation by least squares smoothing. IEEE Trans Signal Process 47(11):3000-3012. https://doi.org/10.1109/ICASSP.1998.681564
Xie L, Yu C, Zhang C (2012) Blind identification of multi-channel ARMA models based on second-order statistics. IEEE Trans Signal Process 60(8):4415-4420. https://doi.org/10.1109/TSP.2012.2196698
Yu C, Xie L, Zhang C (2014) Deterministic blind identification of ⅡR systems with output-switching operations. IEEE Trans Signal Process 62(7):1740-1749. https://doi.org/10.1109/TSP.2014.2300061
Zhang Y (2011) Adaptive MMSE equalization of underwater acoustic channels based on the linear prediction method. J Mar Sci Appl 1:113-120. https://doi.org/10.1007/s11804-011-1050-9

Memo

Memo:
Received date:2019-01-13;Accepted date:2019-07-08。
Foundation item:This study is supported by the Natural Science Foundation of China (NSFC) under Grant Nos. 11774073 and 51279033.
Corresponding author:Dexin Zhao,zhaodx2008@163.com
Last Update: 2020-11-07