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Journal articles on the topic 'Blind decorrelation'

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Author: Grafiati

Published: 10 March 2023

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1

Tuzlukov, V. P. "Two approaches to multiuser detection over fading channels." Doklady BGUIR 19, no. 1 (February 23, 2021): 11–20. http://dx.doi.org/10.35596/1729-7648-2021-19-1-11-20.

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In this paper, two different receiver structures to multiuser detection that are appropriate for the code-division multiple-access systems with antenna arrays in fading channels are investigated and compared. We analyze and compare the performance of the two different multiuser detection structures for uplink or downlink channels. The number of elements of receiving antenna array may be limited in the downlink channel due to the small size of receivers. We assume a synchronous system, but it can be easily extended to an asynchronous system. The first approach is based on the distributed decorrelator where the signal decorrelation is performed by each receiving antenna element independently and decorrelated outputs are combined according to the maximum ratio. The second approach is the central decorrelator where the signal decorrelation is performed once collectively on the outputs from all elements of receiving antenna array. Both decorrelators provide the same performance in the additive white Gaussian noise channels. The distributed decorrelator provides the better performance in flat fading channels. We employ the decorrelator to demonstrate our results. The results discussed in the present paper can be extended to other configurations such as the blind adaptive space-time multiuser detection.

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2

Lapini, Alessandro, Tiziano Bianchi, Fabrizio Argenti, and Luciano Alparone. "Blind Speckle Decorrelation for SAR Image Despeckling." IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (February 2014): 1044–58. http://dx.doi.org/10.1109/tgrs.2013.2246838.

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3

Douglas, S. C., and A. Cichocki. "Neural networks for blind decorrelation of signals." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2829–42. http://dx.doi.org/10.1109/78.650109.

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4

Mei, Tiemin, and Fuliang Yin. "Blind separation of convolutive mixtures by decorrelation." Signal Processing 84, no. 12 (December 2004): 2297–313. http://dx.doi.org/10.1016/j.sigpro.2004.07.024.

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5

Rui Wang, Jing Lu, and Yue He. "Variable Step Size Algorithm for Adaptive Blind Decorrelation." International Journal of Digital Content Technology and its Applications 7, no. 6 (March 31, 2013): 1209–16. http://dx.doi.org/10.4156/jdcta.vol7.issue6.138.

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6

Yin, Fuliang, Tiemin Mei, and Jun Wang. "Blind-Source Separation Based on Decorrelation and Nonstationarity." IEEE Transactions on Circuits and Systems I: Regular Papers 54, no. 5 (May 2007): 1150–58. http://dx.doi.org/10.1109/tcsi.2007.895510.

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7

Chen, Guo Jun, and Han Ying Hu. "New Blind Adaptive Channel Estimation Schemes Based on OFDM Systems." Advanced Materials Research 709 (June 2013): 370–73. http://dx.doi.org/10.4028/www.scientific.net/amr.709.370.

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Two new blind estimation schemes based on decorrelation LMS（DLMS）and time-domain orthogonal LMS（TDO-LMS） algorithms are proposed to solve the problem of convergence in the estimation scheme based on LMS algorithm in wireless mobile communication OFDM systems. These schemes are improved on the traditional LMS algorithm.The factor of TDO-LMS algorithm changes along with the power of signal ,and the decorrelation LMS removes the correlation of the signal.Two methods can get faster convergence and better estimation performance combining with alterable step in the channel estimation of OFDM systems.Simulation shows that the convergence of the schemes proposed is better than that in the channel estimation based on the traditional LMS algorithm obviously.

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8

Schobben, D. W. E., and P. W. Sommen. "A frequency domain blind signal separation method based on decorrelation." IEEE Transactions on Signal Processing 50, no. 8 (August 2002): 1855–65. http://dx.doi.org/10.1109/tsp.2002.800417.

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9

Krstić, Vladimir R. "The blind decision feedback equalizer with the entropic: Leaky decorrelation algorithm." Tehnika 70, no. 1 (2015): 105–11. http://dx.doi.org/10.5937/tehnika1501105k.

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10

Li, Jiahui. "The Study of Blind Source Separation Based on Sparsity and Decorrelation." IOP Conference Series: Materials Science and Engineering 394 (August 7, 2018): 052019. http://dx.doi.org/10.1088/1757-899x/394/5/052019.

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11

Schiessl, I., M. Stetter, J. E. W. Mayhew, N. McLoughlin, J. S. Lund, and K. Obermayer. "Blind signal separation from optical imaging recordings with extended spatial decorrelation." IEEE Transactions on Biomedical Engineering 47, no. 5 (May 2000): 573–77. http://dx.doi.org/10.1109/10.841327.

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12

Ha Quang Minh and Laurenz Wiskott. "Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation." IEEE Transactions on Image Processing 22, no. 7 (July 2013): 2737–50. http://dx.doi.org/10.1109/tip.2013.2257808.

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13

Anemüller, Jörn, and Birger Kollmeier. "Convolutive blind source separation of speech signals based on amplitude modulation decorrelation." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2630. http://dx.doi.org/10.1121/1.4743792.

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14

Jun’an, Yang, He Xuefan, and Tan Ying. "An improved fast blind deconvolution algorithm based on decorrelation and block matrix1." Journal of Electronics (China) 25, no. 5 (September 2008): 577–82. http://dx.doi.org/10.1007/s11767-007-0081-5.

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15

Kocinski, Jedrzej, Pawel Libiszewski, and Aleksander Sek. "Spatial efficiency of blind source separation based on decorrelation – subjective and objective assessment." Speech Communication 53, no. 3 (March 2011): 390–402. http://dx.doi.org/10.1016/j.specom.2010.11.002.

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16

Liu, Jie. "A Time Domain Blind Decorrelation Method of Convolutive Mixtures Based on an IIR Model." Journal of Computational Mathematics 28, no. 3 (June 2010): 371–85. http://dx.doi.org/10.4208/jcm.2009.10-m2900.

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17

Harmeling, Stefan, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller. "Kernel-Based Nonlinear Blind Source Separation." Neural Computation 15, no. 5 (May 1, 2003): 1089–124. http://dx.doi.org/10.1162/089976603765202677.

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We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.

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18

Abrudan, Traian Emanuel, and Visa Koivunen. "Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria." Wireless Personal Communications 43, no. 4 (June 29, 2007): 1151–72. http://dx.doi.org/10.1007/s11277-007-9291-2.

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19

Zhang, Yunpu, Ganlin Shan, Rui Zhang, and Xiusheng Duan. "Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information." Journal of Sensors 2022 (February 3, 2022): 1–17. http://dx.doi.org/10.1155/2022/8555692.

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Tracking ground moving target with sensors proves to be a challenge due to the uncertainty of target motion area, the existence of Doppler blind zone (DBZ), and the complex terrain. In this paper, a multisensor management method based on DBZ information is presented, in which the available sensors are selected to obtain the best operational revenues for ground moving target tracking. First, the ground target motion model is established considering the off-road/on-road state based on road topology information. Second, the sensor measurement model is given combined with the DBZ information, and a decorrelation method of measurement noise is proposed. Third, a target state estimation algorithm is derived using particle filter, in which the DBZ information is regarded as prior information. Then, combined with the variable structure interacting multiple model method, an estimation algorithm for tracking maneuvering target is proposed. Furthermore, an optimization model of nonmyopic sensor management is constructed to obtain the best sensor management scheme. Finally, the advancement and effectiveness of the proposed management method are verified in the simulations.

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20

Wong, K. T., and S. K. Cheung. "“Fractional Self-Decorrelation” to Enhance Single-User-Type DS-CDMA Detectors' Output SINR in Blind Space-Time RAKE Receivers." IEEE Communications Letters 8, no. 6 (June 2004): 336–38. http://dx.doi.org/10.1109/lcomm.2004.827424.

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21

Girolami, Mark, and Colin Fyfe. "Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm." International Journal of Neural Systems 08, no. 05n06 (October 1997): 661–78. http://dx.doi.org/10.1142/s0129065797000586.

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Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.

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22

Lang Tong, A. van der Veen, P. Dewilde, and Youngchul Sung. "Blind decorrelating rake receivers for long-code WCDMA." IEEE Transactions on Signal Processing 51, no. 6 (June 2003): 1642–55. http://dx.doi.org/10.1109/tsp.2003.811230.

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23

Ulukus, S., and R. D. Yates. "A blind adaptive decorrelating detector for CDMA systems." IEEE Journal on Selected Areas in Communications 16, no. 8 (1998): 1530–41. http://dx.doi.org/10.1109/49.730459.

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24

Yingbo Hua, Senjian An, and Yong Xiang. "Blind identification of FIR MIMO channels by decorrelating subchannels." IEEE Transactions on Signal Processing 51, no. 5 (May 2003): 1143–55. http://dx.doi.org/10.1109/tsp.2003.810295.

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25

Zhang, Gaonan, Guoan Bi, and Qian Yu. "Blind intersymbol decorrelating detector for asynchronous multicarrier CDMA system." Signal Processing 85, no. 8 (August 2005): 1511–22. http://dx.doi.org/10.1016/j.sigpro.2005.02.006.

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26

KIMURA, Y., K. SHIBATA, T. SAKAI, and A. NAKAGAKI. "A Blind Adaptive Decorrelating Detector Using Spatial Signature Estimation." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 10 (October 1, 2006): 2686–89. http://dx.doi.org/10.1093/ietfec/e89-a.10.2686.

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27

Kim, Hyeong Jeong, and Hwang Soo Lee. "Blind adaptive decorrelating detector for DS-CDMA systems: auxiliary-vector detector." Electronics Letters 35, no. 20 (1999): 1701. http://dx.doi.org/10.1049/el:19991165.

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28

Po-Rong Chang, Chih-Chien Lee, and Chin-Feng Lin. "Blind adaptive energy estimation for decorrelating decision-feedback CDMA multiuser detection using learning-type stochastic approximations." IEEE Transactions on Vehicular Technology 48, no. 2 (March 1999): 542–52. http://dx.doi.org/10.1109/25.752579.

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29

Djendi, Mohamed, and Abdelhak Cheffi. "New symmetric decorrelating set-membership NLMS adaptive algorithms for blind speech intelligibility enhancement." SN Applied Sciences 1, no. 12 (November 9, 2019). http://dx.doi.org/10.1007/s42452-019-1641-7.

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30

Jayaweera, Sudharman K., and H. Vincent Poor. "Blind Adaptive Decorrelating RAKE (DRAKE) Downlink Receiver for Space-Time Block Coded Multipath CDMA." EURASIP Journal on Advances in Signal Processing 2002, no. 12 (January 2, 2003). http://dx.doi.org/10.1155/s111086570221001x.

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