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Journal articles on the topic 'Constant Modulus Algorithm'

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Published: 4 June 2021
Last updated: 30 January 2023

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1

van der Veen, A. J., and A. Paulraj. "An analytical constant modulus algorithm." IEEE Transactions on Signal Processing 44, no. 5 (May 1996): 1136–55. http://dx.doi.org/10.1109/78.502327.

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2

Benesty, J., and P. Duhamel. "Fast constant modulus adaptive algorithm." IEE Proceedings F Radar and Signal Processing 138, no. 4 (1991): 379. http://dx.doi.org/10.1049/ip-f-2.1991.0049.

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3

Zarzoso, V., and P. Comon. "Optimal Step-Size Constant Modulus Algorithm." IEEE Transactions on Communications 56, no. 1 (January 2008): 10–13. http://dx.doi.org/10.1109/tcomm.2008.050484.

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4

Xu, C., and J. Li. "A Batch Processing Constant Modulus Algorithm." IEEE Communications Letters 8, no. 9 (September 2004): 582–84. http://dx.doi.org/10.1109/lcomm.2004.835330.

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5

Manioudakis, Stylianos. "Linearly precoded analytical constant modulus algorithm for non-constant modulus space-time codes." AEU - International Journal of Electronics and Communications 60, no. 9 (October 2006): 659–62. http://dx.doi.org/10.1016/j.aeue.2005.12.002.

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6

Ahmad, Zeeshan, Zain ul Abidin Jaffri, Najam ul Hassan, and Meng Chen. "Robust adaptive beamforming using modified constant modulus algorithms." Journal of Electrical Engineering 73, no. 4 (August 1, 2022): 248–57. http://dx.doi.org/10.2478/jee-2022-0033.

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Abstract This paper addresses the self-nulling phenomenon also known as the self-cancellation in adaptive beamformers. Optimum beamforming requires knowledge of the desired signal characteristics, either its statistics, its direction-of-arrival, or its response vector. Inaccuracies in the required information lead the beamformer to attenuate the desired signal as if it were interference. Self-nulling is caused by the desired signal having large power (high SNR) relative to the interference signal in case of the minimum variance distortion less response beamformer, and low power desired signal in the case of the constant modulus algorithm (CMA) beamformer, which leads the beamformer to suppress the desired signal and lock onto the interference signal. The least-square constant modulus algorithm is a prominent blind adaptive beamforming algorithm. We propose two CMA-based algorithms which exploit the constant modularity as well as power or DOA of the desired signal to avoid self-nulling in beamforming. Simulations results verify the effectiveness of the proposed algorithms.
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7

Yang, Heng, Jing Wang, Jing Guan, and Wei Lu. "Momentum Factor Constant Modulus Algorithm and Theory." Applied Mechanics and Materials 263-266 (December 2012): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1058.

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The traditional constant modulus algorithm (CMA) has the disadvantage of slow convergence in blind equalization algorithm. This paper studied one improved algorithm based on momentum factor constant modulus algorithm(MCMA) to solve this problem, momentum factor was added to the weight vector iteration formula of CMA to improve the convergence speed. theoretical analysis and simulation showed that: in the case of the same equalization effect, the MCMA converges faster than the traditional constant modulus algorithm, and also different momentum factors have different convergence effects. The larger the momentum factor , the better convergence effect in the defined domain of the momentum factor.
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8

Dabeer, O., and E. Masry. "Convergence analysis of the constant modulus algorithm." IEEE Transactions on Information Theory 49, no. 6 (June 2003): 1447–64. http://dx.doi.org/10.1109/tit.2003.811903.

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9

Ram Babu., T., and Dr P. Rajesh Kumar. "Blind Equalization using Constant Modulus Algorithm and Multi-Modulus Algorithm in Wireless Communication Systems." International Journal of Computer Applications 1, no. 3 (February 25, 2010): 50–55. http://dx.doi.org/10.5120/86-184.

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10

Guo, Ye Cai, and Kang Fan. "Blind Equalization Algorithm Based on Adaptive Genetic Algorithm and Wavelet Transform." Applied Mechanics and Materials 44-47 (December 2010): 3215–19. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3215.

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Constant Modulus Algorithm(CMA) has slow convergence speed and easily immerges in local minimum owing to lack of initialization theory. Aiming at these disadvantages, adaptive genetic parameters are introduced into constant modulus blind equalization algorithm based on Genetic Algorithm and Wavelet Transform(GAWT-CMA), and Constant Modulus blind equali- zation Algorithm based on Adaptive Genetic Algorithm and Wavelet Transform(AGAWT-CMA) was proposed. The proposed algorithm processes genetic parameters adaptively, which can not only save the excellence individual with large probability but also avoid stagnancy during the evolution process. So it is propitious to search the whole optimum solution in overall range. The performance of the proposed algorithm was verified by computer simulation with underwater acoustic channels.
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11

Daumont, Steredenn, and Daniel Le Guennec. "An Analytical Multimodulus Algorithm for Blind Demodulation in a Time-Varying MIMO Channel Context." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/307927.

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This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AMMA and adaptive-ASCMA, respectively, are compared with the adaptive (Analytical Constant Modulus Algorithm) ACMA and the MMA (Multi-Modulus Algorithm). The adaptive-AMMA and adaptive-ASCMA achieve a lower residual intersymbol interference and bit error rate than those of the adaptive-ACMA and MMA.
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12

Xia, Tai Wu, and Hong Jun Liu. "Multiuser Beamforming Using Weighted Orthogonal Constant Modulus Algorithm." Advanced Materials Research 204-210 (February 2011): 2162–66. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.2162.

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Beamforming is a core technique in intelligent antenna signal processing and has been receiving a prevalent focus by researchers. Common orthogonal constant modulus algorithm (OCMA) doesn’t guarantee that the weights of different beamformers converge to corresponding signals when the array of receivers takes in signals with same feature. Thereby a weighted orthogonal constant modulus algorithm (WOCMA) is proposed in which weights of different beamformers are ensured to be disparate by controlling their relevance down to a low level, which concludes different weight converges to corresponding signal. Simulation has proved that WOCMA possesses fast convergent speed and favorable facility in receiving signals.
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13

van der Veen, A. J. "Asymptotic properties of the algebraic constant modulus algorithm." IEEE Transactions on Signal Processing 49, no. 8 (2001): 1796–807. http://dx.doi.org/10.1109/78.934150.

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14

Johannisson, Pontus, Martin Sjödin, Magnus Karlsson, Henk Wymeersch, Erik Agrell, and Peter A. Andrekson. "Modified constant modulus algorithm for polarization-switched QPSK." Optics Express 19, no. 8 (April 6, 2011): 7734. http://dx.doi.org/10.1364/oe.19.007734.

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15

Lopez-Valcarce, R., and F. Perez-Gonzalez. "Efficient reinitialization of the prewhitened constant modulus algorithm." IEEE Communications Letters 5, no. 12 (December 2001): 488–90. http://dx.doi.org/10.1109/4234.974495.

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16

Kamel, R. E., and Y. Bar-Ness. "Anchored blind equalization using the constant modulus algorithm." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44, no. 5 (May 1997): 397–403. http://dx.doi.org/10.1109/82.580849.

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17

Nascimento, V. H., and M. T. M. Silva. "Stochastic Stability Analysis for the Constant-Modulus Algorithm." IEEE Transactions on Signal Processing 56, no. 10 (October 2008): 4984–89. http://dx.doi.org/10.1109/tsp.2008.928101.

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18

Evans, S., and Lang Tong. "Online adaptive reinitialization of the constant modulus algorithm." IEEE Transactions on Communications 48, no. 4 (April 2000): 537–39. http://dx.doi.org/10.1109/26.843118.

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19

Regalia, Phillip A. "Blind turbo equalization using the constant modulus algorithm." IFAC Proceedings Volumes 36, no. 16 (September 2003): 561–66. http://dx.doi.org/10.1016/s1474-6670(17)34821-8.

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20

Özen, A., I. Kaya, and B. Soysal. "A Supervised Constant Modulus Algorithm for Blind Equalization." Wireless Personal Communications 62, no. 1 (June 4, 2010): 151–66. http://dx.doi.org/10.1007/s11277-010-0045-1.

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21

Lang Tong and H. H. Zeng. "Channel surfing reinitialization for the constant modulus algorithm." IEEE Signal Processing Letters 4, no. 3 (March 1997): 85–87. http://dx.doi.org/10.1109/97.558646.

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22

Guo, Ye Cai, and Zhi Chao Zhang. "Wavelet Vector Machines Blind Equalization Algorithm Based on Variable Segmentation Error Function." Applied Mechanics and Materials 44-47 (December 2010): 3210–14. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3210.

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To overcome the disadvantage of constant modulus algorithm's slow convergence and local minimum, this paper presents a wavelet vector machine blind equalization algorithm based on variable segmentation error function. This proposed algorithm uses support vector machine to optimize the initial weight vector, then, it switches to Wavelet Constant Modulus blind equalization Algorithm(WCMA) with odd symmetry variable segmentation error function. The computer simulation with underwater acoustic channel demonstrates that the proposed algorithm has fast convergence rate and small mean square error.
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23

Yasin, Muhammad, and Muhammad Junaid Hussain. "A Novel Adaptive Algorithm Addresses Potential Problems of Blind Algorithm." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5983924.

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A hybrid algorithm called constant modulus least mean square (CMLMS) algorithm is proposed in order to address the potential problems existing with constant modulus algorithm (CMA) about its convergence. It is a two-stage adaptive filtering algorithm and based on least mean square (LMS) algorithm followed by CMA. A hybrid algorithm is theoretically developed and the same is verified through MatLab Software. Theoretical model is verified through simulation and its performance is evaluated in smart antenna in presence of a cochannel interfering signal and additive white Gaussian noise (AWGN) of zero mean. This is also tested in Rayleigh fading channel using digital modulation technique for Bit Error Rate (BER). Finally, a few computer simulations are presented in order to substantiate the theoretical findings with respect to proposed model. Corresponding results obtained with the use of only CMA and LMS algorithms are also presented for further comparison.
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24

Yang, Da-long, Da-hai Chen, Jian Zhang, and Zhi-qiang Chen. "Constant Modulus Equalization Algorithm for Higher-order General Constellations." Journal of Electronics & Information Technology 34, no. 12 (July 13, 2013): 2855–61. http://dx.doi.org/10.3724/sp.j.1146.2012.00619.

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25

Kundu, Anindya, and Ajay Chakraborty. "FRACTIONALLY SPACED CONSTANT MODULUS ALGORITHM FOR WIRELESS CHANNEL EQUALIZATION." Progress In Electromagnetics Research B 4 (2008): 237–48. http://dx.doi.org/10.2528/pierb08010802.

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26

Jani, U. G., E. M. Dowling, R. M. Golden, and Z. Wang. "Multiuser interference suppression using block Shanno constant modulus algorithm." IEEE Transactions on Signal Processing 48, no. 5 (May 2000): 1503–6. http://dx.doi.org/10.1109/78.840003.

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27

Boonpoonga, A., P. Sirisuk, M. Chongcheawchamnan, S. Patisang, and M. Krairiksh. "Hardware-assisted initialisation for constant modulus algorithm adaptive antenna." IET Microwaves, Antennas & Propagation 2, no. 4 (June 1, 2008): 303–11. http://dx.doi.org/10.1049/iet-map:20070130.

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28

van der Veen, A. J. "Statistical performance analysis of the algebraic constant modulus algorithm." IEEE Transactions on Signal Processing 50, no. 12 (December 2002): 3083–97. http://dx.doi.org/10.1109/tsp.2002.805502.

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29

Ochi, Hiroshi, Ken Onaga, and Sigenori Kinjo. "Generalized block constant modulus algorithm and its VLSI architecture." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 441. http://dx.doi.org/10.1016/s0920-5489(99)90907-8.

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30

Yang, V. Y., and D. L. Jones. "A vector constant modulus algorithm for shaped constellation equalization." IEEE Signal Processing Letters 5, no. 4 (April 1998): 89–91. http://dx.doi.org/10.1109/97.664175.

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31

Al-Bayati, A. K. S., and S. Prasad. "Modified constant modulus algorithm for blind DS/CDMA detection." Electronics Letters 35, no. 23 (1999): 2005. http://dx.doi.org/10.1049/el:19991379.

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32

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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33

Prakriya, Shankar. "Blind Feedforward Timing Estimation for Constant-Modulus Signalling in Time-Varying Fading Channels." Journal of Electrical and Computer Engineering 2010 (2010): 1–4. http://dx.doi.org/10.1155/2010/620789.

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A simple low-complexity algorithm is proposed for blind feedforward symbol timing estimation in time-varying flat-fading channels, assuming constant modulus signalling. Its important properties are brought out using asymptotic analysis. It is shown that at high Signal-to-Noise Ratios (SNRs), the algorithm results in a timing estimate with negligible bias. It also has a Mean-Square-Error (MSE) performance that is superior for short data records and small roll-off factors to that of the Square-Law-(SQL-) and the CycloStationarity-(CS-) based algorithms available to date. Computer simulations are presented to demonstrate effectiveness of the algorithm.
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34

Guo, Jun, Ye Cai Guo, Qu Chen, and Yi Bo Zhao. "Wavelet Frequency Domain Constant Modulus Blind Equalization Algorithm Based on Fractional Lower Order Statistics." Applied Mechanics and Materials 198-199 (September 2012): 1497–500. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1497.

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In order to improve the performance of constant modulus algorithm(CMA) in the noise which obeys α-stable distribution, wavelet frequency domain constant modulus blind equalization algorithm based on fractional lower order statistics(WT-FLOSFCMA) is proposed. This proposed algorithm uses fractional lower order statistics to restrain α-stable noise, and its computational loads can be greatly reduced by using FFT technique and the overlapping retention law. In the proposed algorithm, according to the minimum dispersion coefficient criterion, the CMA is optimized, orthogonal wavelet transform is used to improve the convergence rate. The computer simulations in underwater acoustic channels show that the proposed algorithm outperforms than CMA, orthogonal wavelet transform constant modulus algorithm(WT-CMA),and frequency domain blind equalization algorithm based on fractional lower order statistics(FLOSFCMA) in suppressing with inter-symbol interfere, improving convergence rate, and reducing mean square error.
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35

Hu, Wanru, Zhugang Wang, Ruru Mei, and Meiyan Lin. "A Simple and Robust Equalization Algorithm for Variable Modulation Systems." Electronics 10, no. 20 (October 14, 2021): 2496. http://dx.doi.org/10.3390/electronics10202496.

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This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz.
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36

El-Barbary, K., K. Moustafa, H. Hassan, and M. Alshol. "Interference Suppression with Total Least Square (TLS) Algorithm and Constant Modulus Algorithm (CMA)." International Conference on Electrical Engineering 6, no. 6 (May 1, 2008): 1–12. http://dx.doi.org/10.21608/iceeng.2008.34638.

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37

Mwangosi, Emmanuel Anania, Cang Yan, and Naveed Ur Rehman. "Improved Constant Modulus Algorithm for Blind Equalization of QAM Signals." Applied Mechanics and Materials 631-632 (September 2014): 824–29. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.824.

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The paper present new approach for improving the steady-state error performance of Constant Modulus Algorithm (CMA), it is well known that for higher level modulations such as QAM, CMA does not perform well. Several techniques have been proposed in recent years to deal with slow convergence and MSE performance of CMA. Constellation matched error has been seen to offer best performance by providing the cost function with the knowledge of the constellation symbols. New constellation match error function is studied, simulation is performed, it can be witnessed that 4dB improvement stead state error performance.
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38

Benedetto, Francesco, Gaetano Giunta, and Markku Renfors. "A Spectrum Sensing Algorithm for Constant Modulus Primary Users Signals." IEEE Communications Letters 20, no. 2 (February 2016): 400–403. http://dx.doi.org/10.1109/lcomm.2015.2500579.

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39

Al-Jazzar, Saleh O., Zoubir Hamici, and Sami Aldalahmeh. "Two-Dimensional AOA Estimation Based on a Constant Modulus Algorithm." International Journal of Antennas and Propagation 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/3214021.

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We propose a two-dimensional (2D) angle of arrival (AOA) estimator using the algebraic constant modulus algorithm (ACMA). This algorithm was originally introduced to estimate the one-dimensional (1D) AOA. An extension to estimate and automatically pair the elevation and azimuth angles for different sources is derived and proved in this paper. The ACMA method factorises a matrix into two different matrices; one is of constant modulus and contains the azimuth AOA information; however the second was previously ignored. In this paper we will prove that this second matrix contains the elevation AOA information. Thus, 2D AOA estimation is proved possible using the ACMA method. Simulation results are presented to illustrate the proposed method’s performances under different conditions.
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40

Treichler, J., and M. Larimore. "New processing techniques based on the constant modulus adaptive algorithm." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 2 (April 1985): 420–31. http://dx.doi.org/10.1109/tassp.1985.1164567.

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41

Liu, Dan, and Lang Tong. "An analysis of constant modulus algorithm for array signal processing." Signal Processing 73, no. 1-2 (January 1999): 81–104. http://dx.doi.org/10.1016/s0165-1684(98)00186-8.

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42

Chen, Y., T. Le-Ngoc, B. Champagne, and C. Xu. "Recursive Least Squares Constant Modulus Algorithm for Blind Adaptive Array." IEEE Transactions on Signal Processing 52, no. 5 (May 2004): 1452–56. http://dx.doi.org/10.1109/tsp.2004.826167.

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43

Özen, A., İ. Kaya, and B. Soysal. "Variable Step-Size Constant Modulus Algorithm Employing Fuzzy Logic Controller." Wireless Personal Communications 54, no. 2 (April 18, 2009): 237–50. http://dx.doi.org/10.1007/s11277-009-9723-2.

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44

Manioudakis, Stylianos. "Regularised analytical constant modulus algorithm for space–time coded systems." AEU - International Journal of Electronics and Communications 60, no. 1 (January 2006): 80–84. http://dx.doi.org/10.1016/j.aeue.2005.02.007.

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45

Li, S., and T. Sh Qiu. "Tracking performance analysis of fractional lower order constant modulus algorithm." Electronics Letters 45, no. 11 (2009): 545. http://dx.doi.org/10.1049/el.2009.0561.

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46

Tsai, Y. L., and T. Chen. "Transformation Media in Acoustics with Constant Bulk Modulus or Constant Density Tensor." Journal of Mechanics 32, no. 3 (August 19, 2015): 313–23. http://dx.doi.org/10.1017/jmech.2015.65.

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AbstractThis work is to present a formulation of cloaking or concentrating device in acoustics in which the transformed material could be either having uniform bulk modulus or having homogeneous density tensor. The transformed material parameters, depending on the mapping of physical and virtual coordinates, are often position-varying and anisotropic. This often adds substantial complexity in practical implementation. Here we present a theoretical algorithm that allows us to design a transformation field that could have either uniform bulk modulus or constant density tensor. For cloaking devices with constant bulk modulus, analytical and numerical results are presented for circular as well as for non-circular cloaking devices. Specifically, elliptical and twin-cloak devices are exemplified. To achieve the effect of constant density tensor, we consider only circular geometry. Devices with cloaking or concentrating effects can be exactly formulated. We note, however, that it seems unlikely at this moment to have a transformation device that has constant bulk modulus and constant density tensor at the same time. Nevertheless, we remark the present results are of still sufficient merit in that the uniform material parameters, in either set of material parameters, indeed greatly simplify the practice in real implementations.
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47

Gao, Hui Juan, and Yan Liu. "New Dual-Mode FSE Suitable for High-Order QAM Signals." Advanced Materials Research 760-762 (September 2013): 478–82. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.478.

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In the digital transmission system, constant modulus algorithm (CMA) is a famous blind equalization to overcome the inter-symbol interference without the aid of training sequences. But for the non-constant modulus signals such as higher-order QAM signals, the CMA just achieve moderate steady-state mean square error (MSE). So a new dual-mode fractionally-spaced equalization (FSE) suitable for high-order QAM signals is proposed, which makes full use of the character which is that the high-order QAM signals have the different modulus. This algorithm uses the FSE based on CMA as the basal mode and in the second mode it uses the FSE based on variable modulus algorithm. The simulation results show that compared with CMA the proposed algorithm has faster convergence rate and lower steady-state mean square error.
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48

Sarna, Szymon, and Robert Czerwinski. "Small Prime Divisors Attack and Countermeasure against the RSA-OTP Algorithm." Electronics 11, no. 1 (December 28, 2021): 95. http://dx.doi.org/10.3390/electronics11010095.

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One-time password algorithms are widely used in digital services to improve security. However, many such solutions use a constant secret key to encrypt (process) one-time plaintexts. A paradigm shift from constant to one-time keys could introduce tangible benefits to the application security field. This paper analyzes a one-time password concept for the Rivest–Shamir–Adleman algorithm, in which each key element is hidden, and the value of the modulus is changed after each encryption attempt. The difference between successive moduli is exchanged between communication sides via an unsecure channel. Analysis shows that such an approach is not secure. Moreover, determining the one-time password element (Rivest–Shamir–Adleman modulus) can be straightforward. A countermeasure for the analyzed algorithm is proposed.
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49

Wang, Zhen, and Ye Cai Guo. "Blind Equalization Algorithm Based on the Orthogonal Wavelet Transform for SIMO Systems." Applied Mechanics and Materials 198-199 (September 2012): 1493–96. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1493.

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In order to improve the equalization effects of the constant modulus blind equalization algorithm (CMA) for Single-Input and Multiple-Output (SIMO) systems, orthogonal wavelet transform constant modulus algorithm (WT-CMA) based on SIMO is proposed. This proposed algorithm uses the orthogonal wavelet transform to decrease the autocorrelation of the input signals to accelerate the convergence rate and reduce the steady-state error. Theoretical analysis and computer simulations shows that the proposed algorithm has better performance and smaller steady-state error in SIMO systems, it is very easy to achieve in engineering.
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50

Zhu, Ting Ting, and Xiao Tao Jiao. "A New Blind Equalization Algorithm Suitable for Sparse Underwater Acoustic Channel." Applied Mechanics and Materials 195-196 (August 2012): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.149.

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The Constant Modulus Algorithms is a mature algorithm for a long time. The researchers are fond of its solidity. But in the area of shallow sea or blue sea, the CMA has poor constringency. In this passage, based on a improved blind equalization algorithm, we proposed a new algorithm which suitable for sparse underwater acoustic channel. The computer simulation results demonstrate fast convergence and fewer accounts.
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