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Journal articles on the topic 'Blind equalization'

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Published: 4 June 2021
Last updated: 28 January 2023

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1

Litwin, L. R. "Blind channel equalization." IEEE Potentials 18, no. 4 (1999): 9–12. http://dx.doi.org/10.1109/45.796095.

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2

Icart, Sylvie, Pierre Comon, and Ludwig Rota. "Blind paraunitary equalization." Signal Processing 89, no. 3 (March 2009): 283–90. http://dx.doi.org/10.1016/j.sigpro.2008.08.014.

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3

Xiao, Ying, and Rui Ruan. "CMA Blind Equalization with Variable Momentum Based on Nonlinear Transformation Function." Applied Mechanics and Materials 602-605 (August 2014): 2658–61. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2658.

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For the contradiction between convergence rate and convergence precision in the CMA blind equalization with the fixed momentum factor, a variable momentum CMA blind equalization is proposed The output error power of the blind equalizer is acted as the parameter, which control the adjustment of the momentum factor adaptively based on nonlinear transformation function. The algorithm can obtain faster convergence rate and higher convergence precision, also the performance of the blind equalization is improved. The simulation results show that, compared with the CMA blind equalization with the fixed momentum factor, CMA blind equalization with variable momentum based on nonlinear transform can obtain better performance
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4

Cai, Wei Ju. "Improved Blind Equalization Algorithm and Simulation." Applied Mechanics and Materials 325-326 (June 2013): 1645–48. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1645.

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This paper focuses on the constant modulus Busgang blind equalization algorithm (CMA blind equalization algorithm in Constant, The Modulus Algorithm). Analysis of the convergence performance of the traditional CMA blind equalization algorithm, the fixed step size, convergence speed and convergence of mutual constraint between the precision of its application under great restrictions is demonstrated in the paper. In order to solve this contradiction, this paper presents a CMA blind equalization algorithm based on the mean square error (MSE Mean Square Error).
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5

Ghosh, Monisha. "Maximum-likelihood blind equalization." Optical Engineering 31, no. 6 (1992): 1224. http://dx.doi.org/10.1117/12.57516.

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6

Gustafsson, F., and B. Wahlberg. "Identifiability in Blind Equalization." IFAC Proceedings Volumes 26, no. 2 (July 1993): 409–12. http://dx.doi.org/10.1016/s1474-6670(17)48296-6.

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7

Itoh, Katsuko, Tetsuya Shimamura, and Jouji Suzuki. "Prefiltering for blind equalization." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 78, no. 9 (September 1995): 1–11. http://dx.doi.org/10.1002/ecjc.4430780901.

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8

Sun, Yongjun, Liangting Zhu, Dongmin Li, and Zujun Liu. "Convex Combination of SISO Equalization and Blind Source Separation for MIMO Blind Equalization." Wireless Personal Communications 106, no. 3 (March 13, 2019): 1397–409. http://dx.doi.org/10.1007/s11277-019-06221-4.

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9

Xiao, Ying, and Rui Ruan. "CMA Blind Equalization with Quasi-Newton Algorithm in Underwater Acoustic Channels Based on Simplified Cost Function." Advanced Materials Research 989-994 (July 2014): 1865–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1865.

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The CMA cost function is simplified to meet the second norm form, and a new CMA blind equalization based on quasi-newton algorithm is proposed. Since the CMA cost function does not meet the second norm form, it is difficult to use quasi-newton algorithm to update the blind equalizer directly based on the cost function of CMA. If the cost function is simplified to meet the second norm form, it can use quasi-newton algorithm to update the blind equalizer directly. Thus, the convergence rate and convergence precision of CMA blind equalization can be improved effectively. Simulation results under the acoustic channels show that CMA blind equalization with quasi-newton algorithm based on the simplified cost function has faster convergence rate and less steady state residual error, which has practical value in the blind equalization of fast time-varying underwater acoustic channels
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10

Pan, Zihao, Chen Xie, Heng Wang, Yimin Wei, and Daoxing Guo. "Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things." Sensors 22, no. 17 (August 29, 2022): 6508. http://dx.doi.org/10.3390/s22176508.

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With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for an excellent complexity/power efficiency trade-off and channel fading resistance at the physical layer. In this paper, we consider the blind equalization of short-continuous-phase-modulated (CPM) burst for UAV-aided IoT. To solve the problems of the high complexity and poor convergence of short-burst CPM blind equalization, a novel turbo blind equalization algorithm is proposed based on establishing a new expectation–maximization Viterbi (EMV) algorithm and turbo scheme. Firstly, a low complexity blind equalization algorithm is obtained by applying the soft-output Lazy Viterbi algorithm within the EM algorithm iteration. Furthermore, a set of initializers that achieves a high global convergence probability is designed by the blind channel-acquisition (BCA) method. Meanwhile, a soft information iterative process is used to improve the system performance. Finally, the convergence, bit error rate, and real-time performance of iterative detection can be further improved effectively by using improved exchange methods of extrinsic information and the stopping criterion. The analysis and simulation results show that the proposed algorithm achieves a good blind equalization performance and low complexity.
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11

Liu, Shun Lan, and Lin Wang. "New Hybrid Blind Equalization Algorithms." Applied Mechanics and Materials 182-183 (June 2012): 1810–15. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1810.

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A novel decision-directed Modified Constant Modulus Algorithm (DD-MCMA) was proposed firstly. Then a constellation matched error (CME) function was added to the cost function of DD-MCMA and CME-DD-MCMA algorithm was presented. Furthermore, we improve the CME-DD-MCMA by replacing the fixed step with variable step size, that is VSS-CME-DD-MCMA algorithm. The simulation results show that the proposed new blind equalization algorithms can tremendously accelerate the convergence speed and achieve lower residual inter-symbol interference (ISI) than MCMA, and among the three proposed algorithms, VSS-CME-DD-MCMA has the fastest convergence speed and the lowest residual ISI, but it has the largest computation complexity.
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12

GENG, Tian-yu, Qin SHU, and DA-li YING. "New mixed blind equalization algorithm." Journal of Computer Applications 32, no. 3 (April 2, 2013): 783–86. http://dx.doi.org/10.3724/sp.j.1087.2012.00783.

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13

Verdu, S., B. D. O. Anderson, and R. A. Kennedy. "Blind equalization without gain identification." IEEE Transactions on Information Theory 39, no. 1 (1993): 292–97. http://dx.doi.org/10.1109/18.179377.

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14

Bai, Erwei. "Blind System Identification and Equalization." IFAC Proceedings Volumes 33, no. 15 (June 2000): 343–48. http://dx.doi.org/10.1016/s1474-6670(17)39774-4.

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15

Liu, Ruey-wen. "Direct multiple-channel blind equalization." Circuits System and Signal Processing 17, no. 1 (January 1998): 117–22. http://dx.doi.org/10.1007/bf01213973.

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16

Elkassimi, Said, Said Safi, and B. Manaut. "Blind Channel Estimation and Equalization." International Journal of Multimedia and Ubiquitous Engineering 11, no. 12 (December 31, 2016): 191–206. http://dx.doi.org/10.14257/ijmue.2016.11.12.18.

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17

Werner, J. J., Jian Yang, D. D. Harman, and G. A. Dumont. "Blind equalization for broadband access." IEEE Communications Magazine 37, no. 4 (April 1999): 87–93. http://dx.doi.org/10.1109/35.755455.

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18

Buchoux, Vincent, Lisa Perros-Meilhac, Olivier Cappé, and Eric Moulines. "Blind and semi-blind equalization: methods and algorithms." Annales Des Télécommunications 53, no. 11-12 (November 1998): 449–65. http://dx.doi.org/10.1007/bf02998591.

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19

Zhang, Xiao Qin, Yong Sheng Hu, and Li Yi Zhang. "Sixth-Second Order Normalized Cumulants Blind Equalization Algorithm Based on T/4 Oversampling." Applied Mechanics and Materials 719-720 (January 2015): 994–99. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.994.

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Most existing blind equalization algorithms rely on partial or complete channel identification, but the channel order estimation is always a difficult task. In this paper, higher order normalized cumulants analysis is applied to the blind equalization, and a new sixth-second order normalized cumulants blind equalization algorithm based on oversampling is proposed. The proposed method recovers the transmitted sequence adopting optimization algorithm of sixth-second order normalized cumulants without channel identification and channel order estimation. Simulation results show the algorithm's effectiveness.
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20

Guo, Ye Cai, and Zheng Xin Liu. "Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation." Applied Mechanics and Materials 44-47 (December 2010): 4146–50. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4146.

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To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.
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21

Zhao, Xiang, Jin Yong Sun, and Han Hong Tan. "A Variable Step Size Blind Equalization Algorithm Based on Gamma Distribution." Advanced Materials Research 457-458 (January 2012): 961–67. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.961.

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A variable step size Constant Modulus Algorithm (CMA) based on the gamma distribution is implemented as solutions to optimize the problem of blind equalization. The factor of step size in blind equalization algorithm is varied with gamma variable, in terms of the characteristics of which, the algorithm can search for the globe optimal equalizer weight vector. Simulation results indicate that the convergence rate and the steady Mean Square Errors (MSE) performances of the algorithm proposed are much better than conventional CMA and modified CMA blind equalization algorithms.
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22

Xiao, Ying, and Zhen Xing Li. "Adaptive Blind Equalization with Variable Step-Size Modified by Attenuation Function." Key Engineering Materials 474-476 (April 2011): 1792–96. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1792.

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Step-size is one of the important parameters which influence the performance of blind equalization, a variable step-size method control by attenuation function according to instantaneous gradient and iterative times is proposed in this paper. Initially, set larger step-size to obtain faster convergence, with iterative times increasing, step-size would decrease control by attenuation function for instantaneous gradient reducing. Step-size changes according to this method consistent with the demand variable step-size adaptive algorithm that can obtain combination of convergence rate and convergence precision to improve the performance of blind equalization. Simulations show that this method performs better than conventional blind equalization with constant step-size.
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23

Yang, Howard Hua. "On-line blind equalization via on-line blind separation." Signal Processing 68, no. 3 (August 1998): 271–81. http://dx.doi.org/10.1016/s0165-1684(98)00077-2.

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24

Cao, Fengchu, Mingyi Gao, Pengfei Wang, Xiaodi You, and Gangxiang Shen. "Optimized blind equalization for probabilistically shaped high-order QAM signals." Chinese Optics Letters 20, no. 8 (2022): 080601. http://dx.doi.org/10.3788/col202220.080601.

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25

Dong, Zheng, Ke Xian Gong, and Lin Dong Ge. "A Joint Timing and Fractionally-Space Blind Equalization Algorithm." Advanced Materials Research 765-767 (September 2013): 605–10. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.605.

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In this paper, we proposed a joint timing and fractionally-spaced blind equalization algorithm. It adopts Gardner algorithm instead of simple down-sampling in conventional fractionally-spaced equalization in order to overcome the problem of that the filter can’t compensate timing error. The simulations show that the performance of the proposed fractionally-spaced equalization algorithm is better than CMA in different SNR and in different number of channel taps.
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26

Abrar, Shafayat, and Roy A. ,. Jr Axford. "Sliced Multi-modulus Blind Equalization Algorithm." ETRI Journal 27, no. 3 (June 10, 2005): 257–66. http://dx.doi.org/10.4218/etrij.05.0104.0027.

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27

Kim, Yongguk, and Heung-Gyoon Ryu. "Blind Turbo Equalization System with Beamforming." Journal of Korea Information and Communications Society 38A, no. 10 (October 31, 2013): 850–57. http://dx.doi.org/10.7840/kics.2013.38a.10.850.

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28

Vembu, S., S. Verdu, R. A. Kennedy, and W. Sethares. "Convex cost functions in blind equalization." IEEE Transactions on Signal Processing 42, no. 8 (1994): 1952–60. http://dx.doi.org/10.1109/78.301833.

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29

ODA, Hiroyoshi, and Yoichi SATO. "High Speed Convergence of Blind Equalization." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 7 (1993): 305–18. http://dx.doi.org/10.5687/iscie.6.305.

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30

Jelonnek, Björn, Dieter Boss, and Karl-Dirk Kammeyer. "Generalized eigenvector algorithm for blind equalization." Signal Processing 61, no. 3 (September 1997): 237–64. http://dx.doi.org/10.1016/s0165-1684(97)00108-4.

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31

Fang, Yong, Tommy W. S. Chow, and K. T. Ng. "Linear neural network based blind equalization." Signal Processing 76, no. 1 (July 1999): 37–42. http://dx.doi.org/10.1016/s0165-1684(98)00245-x.

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32

Klein, Andrew G., C. Richard Johnson, and Pierre Duhamel. "On Blind Equalization of Biorthogonal Signaling." IEEE Transactions on Signal Processing 55, no. 4 (April 2007): 1421–35. http://dx.doi.org/10.1109/tsp.2006.889981.

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33

Ross, F. J., and D. P. Taylor. "An enhancement to blind equalization algorithms." IEEE Transactions on Communications 39, no. 5 (May 1991): 636–39. http://dx.doi.org/10.1109/26.87152.

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34

Rao, Wei, Ye Cai Guo, Min Chen, Wen Qun Tan, Jian Bing Liu, Fei Xia, Li Fan, and Hui Jun Xu. "New Concurrent Fractionally-Spaced Blind Equalization." Advanced Materials Research 108-111 (May 2010): 363–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.363.

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The paper proposes a concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme for fractionally-spaced blind equalization. The proposed algorithm makes full use of the advantages of CMA and DD algorithm. A novel rule to control the adjustment of DD’s tap weights vector is proposed which avoids the hard switch between CMA and DD in practice. Simulations with underwater acoustic channels are used to compare the proposed algorithm with the famous CMA. And the simulation results show that the proposed algorithm has faster convergence rate and lower steady state mean square error.
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35

Abrar, Shafayat, and Ijaz Mansoor Qureshi. "Blind Equalization of Cross-QAM Signals." IEEE Signal Processing Letters 13, no. 12 (December 2006): 745–48. http://dx.doi.org/10.1109/lsp.2006.879828.

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36

Satorius, E. H., and J. J. Mulligan. "An Alternative Methodology for Blind Equalization." Digital Signal Processing 3, no. 3 (July 1993): 199–209. http://dx.doi.org/10.1006/dspr.1993.1025.

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37

Dogan, Mithat C., and Jerry M. Mendel. "Blind deconvolution (equalization): Some new results." Signal Processing 53, no. 2-3 (September 1996): 109–16. http://dx.doi.org/10.1016/0165-1684(96)00080-1.

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38

Sun, Yunshan, Liyi Zhang, Jin Zhang, and Lijuan Shi. "Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/743546.

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A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
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39

Gao, Canghao. "Research on Blind equalization method of variable step size neural network in underwater acoustic communication." Advances in Engineering Technology Research 3, no. 1 (November 9, 2022): 199. http://dx.doi.org/10.56028/aetr.3.1.199.

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In the construction and development of modern society, how to ensure the high efficiency and safety of underwater communication is the main issue discussed by researchers. Among them, the underwater acoustic channel as a very complex variable parameter channel, the actual frequency band is limited, the signal fluctuation and fading is more serious, which directly affects the efficiency and quality of underwater information transmission. As one of the effective methods to solve this problem, channel equalization technology is the main content of the empirical analysis of scholars. Therefore, on the basis of understanding the theory of underwater acoustic communication and blind equalization technology, this paper compares and analyzes the convergence performance of the traditional constant mode blind equalization algorithm and the new variable step constant mode blind equalization algorithm. The final research results show that the improved algorithm has higher convergence than the traditional algorithm, and can reduce the transmission error and improve the quality of aquatic communication.
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40

Shouyu, Sun. "Blind Adaptive Channel Equalization Using Modified CMA." Advanced Materials Research 658 (January 2013): 537–40. http://dx.doi.org/10.4028/www.scientific.net/amr.658.537.

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The constant modulus algorithm (CMA) equalizer is perhaps the best known and the most popular scheme for blind adaptive channel equalization. In this paper, a modified constant modulus algorithm (modified CMA or MCMA) is proposed by modifying its error function. We have discussed the MCMA to blind channel equalization for baud-rat sampling in single-user case. Computer simulations are provided for 8PSK signals in noise environments under frequency selective channels. Results demonstrate that the MCMA displays much superior performance to the CMA for both convergence-time and intersymbol interference (ISI) or mean square error (MSE).
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41

Han, Soo-Whan. "Self-Organizing Map for Blind Channel Equalization." Journal of information and communication convergence engineering 8, no. 6 (December 31, 2010): 609–17. http://dx.doi.org/10.6109/jicce.2010.8.6.609.

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42

Xu, Jun, Fu-ping Wang, and Zan-ji Wang. "Reusing Data in Bussgang Blind Equalization Algorithm." Journal of Electronics & Information Technology 30, no. 9 (April 6, 2011): 2174–77. http://dx.doi.org/10.3724/sp.j.1146.2007.00326.

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43

Gómez, Juan C., Enrique Baeyens, and Kameshwar Poolla. "Subspace based Nonlinear Multichannel Blind Identification/Equalization." IFAC Proceedings Volumes 42, no. 10 (2009): 1692–97. http://dx.doi.org/10.3182/20090706-3-fr-2004.00281.

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44

SHI, Xiao-lin. "Adaptive blind equalization for ultra wideband system." Journal of Computer Applications 29, no. 5 (July 23, 2009): 1238–40. http://dx.doi.org/10.3724/sp.j.1087.2009.01238.

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45

Mannerkoski, J., and D. P. Taylor. "Blind equalization using least-squares lattice prediction." IEEE Transactions on Signal Processing 47, no. 3 (March 1999): 630–40. http://dx.doi.org/10.1109/78.747771.

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46

Gesbert, D., and P. Duhamel. "Unbiased blind adaptive channel identification and equalization." IEEE Transactions on Signal Processing 48, no. 1 (2000): 148–58. http://dx.doi.org/10.1109/78.815485.

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47

Qingyu Li, Er-Wei Bai, and Yinyu Ye. "Blind channel equalization and ε-approximation algorithms." IEEE Transactions on Signal Processing 49, no. 11 (2001): 2823–31. http://dx.doi.org/10.1109/78.960429.

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48

Byoung-Jo Kim and D. C. Cox. "Blind equalization for short burst wireless communications." IEEE Transactions on Vehicular Technology 49, no. 4 (July 2000): 1235–47. http://dx.doi.org/10.1109/25.875234.

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49

Weerackody, V., and S. A. Kassam. "Dual-mode type algorithms for blind equalization." IEEE Transactions on Communications 42, no. 1 (1994): 22–28. http://dx.doi.org/10.1109/26.275296.

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50

Yuang Lou. "Channel estimation standard and adaptive blind equalization." IEEE Transactions on Communications 43, no. 2/3/4 (February 1995): 182–86. http://dx.doi.org/10.1109/26.380032.

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