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Journal articles on the topic 'Minimum mean square error'

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Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Petra, Nicola, Davide De Caro, Valeria Garofalo, Ettore Napoli, and Antonio G. M. Strollo. "Truncated squarer with minimum mean-square error." Microelectronics Journal 45, no. 6 (June 2014): 799–804. http://dx.doi.org/10.1016/j.mejo.2014.02.018.

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2

Piegorsch, Walter W., and A. John Bailer. "Minimum mean-square error quadrature." Journal of Statistical Computation and Simulation 46, no. 3-4 (May 1993): 217–34. http://dx.doi.org/10.1080/00949659308811504.

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3

Schmidt, David A., Michael Joham, and Wolfgang Utschick. "Minimum mean square error vector precoding." European Transactions on Telecommunications 19, no. 3 (2008): 219–31. http://dx.doi.org/10.1002/ett.1192.

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4

Chong, Edwin K. P. "Well-Conditioned Linear Minimum Mean Square Error Estimation." IEEE Control Systems Letters 6 (2022): 2431–36. http://dx.doi.org/10.1109/lcsys.2022.3162404.

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5

Wen, Chao-Kai, Jung-Chieh Chen, and Pangan Ting. "A Shrinkage Linear Minimum Mean Square Error Estimator." IEEE Signal Processing Letters 20, no. 12 (December 2013): 1179–82. http://dx.doi.org/10.1109/lsp.2013.2283725.

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6

Kullaa, Jyrki. "Sensor validation using minimum mean square error estimation." Mechanical Systems and Signal Processing 24, no. 5 (July 2010): 1444–57. http://dx.doi.org/10.1016/j.ymssp.2009.12.001.

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7

Liski, Erkki P., Helge Toutenburg, and Götz Trenkler. "Minimum mean square error estimation in linear regression." Journal of Statistical Planning and Inference 37, no. 2 (November 1993): 203–14. http://dx.doi.org/10.1016/0378-3758(93)90089-o.

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8

Purczyński, Jan. "Unbiased estimator versus minimum mean square error estimator." Studia i Prace WNEiZ 45 (2016): 61–70. http://dx.doi.org/10.18276/sip.2016.45/2-05.

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9

Shah, M. C., R. Parmar, and V. P. Gupta. "Some techniques of minimum mean square error estimation." Microelectronics Reliability 28, no. 5 (January 1988): 689–91. http://dx.doi.org/10.1016/0026-2714(88)90004-2.

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10

Weixian Qian, 钱惟贤, 陈钱 Qian Chen, and 顾国华 Guohua Gu. "Minimum mean square error method for stripe nonuniformity correction." Chinese Optics Letters 9, no. 5 (2011): 051003–51005. http://dx.doi.org/10.3788/col201109.051003.

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11

Merhav, Neri. "Rate–Distortion Function via Minimum Mean Square Error Estimation." IEEE Transactions on Information Theory 57, no. 6 (June 2011): 3196–206. http://dx.doi.org/10.1109/tit.2011.2143850.

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12

Marsh, Lawrence C., and Michael J. Radzicki. "Minimum mean square error of a generalized Stein estimator." Economics Letters 21, no. 4 (January 1986): 333–35. http://dx.doi.org/10.1016/0165-1765(86)90199-0.

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13

Heravi, Ahmad Reza, and Ghosheh Abed Hodtani. "Where Does Minimum Error Entropy Outperform Minimum Mean Square Error? A New and Closer Look." IEEE Access 6 (2018): 5856–64. http://dx.doi.org/10.1109/access.2018.2792329.

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14

Sonartiya, Tamashri, and Deepak Pancholi. "Performance Analysis of V-Blast MIMO System Using Minimum Mean Square Error Equalizer Technique with BPSK." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 310–12. http://dx.doi.org/10.31142/ijtsrd15804.

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15

Kou, Gang, Changsheng Lin, Yi Peng, Guangxu Li, and Yang Chen. "Minimum Mean Square Error Estimators for the Exponential SSALT Model." Informatica 27, no. 4 (January 1, 2016): 755–65. http://dx.doi.org/10.15388/informatica.2016.110.

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16

Guo, D., S. Shamai, and S. Verdu. "Mutual Information and Minimum Mean-Square Error in Gaussian Channels." IEEE Transactions on Information Theory 51, no. 4 (April 2005): 1261–82. http://dx.doi.org/10.1109/tit.2005.844072.

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17

Ran, Rong, Jiaheng Wang, Seong Keun Oh, and Song Nam Hong. "Sparse-Aware Minimum Mean Square Error Detector for MIMO Systems." IEEE Communications Letters 21, no. 10 (October 2017): 2214–17. http://dx.doi.org/10.1109/lcomm.2017.2723362.

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18

Sari, H., L. Desperben, and S. Moridi. "Minimum Mean-Square Error Timing Recovery Schemes for Digital Equalizers." IEEE Transactions on Communications 34, no. 7 (July 1986): 694–702. http://dx.doi.org/10.1109/tcom.1986.1096601.

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19

Elrefaie, A., and L. Kurz. "A Minimum Mean-Square Error Equalizer for Nonlinear Satellite Channels." IEEE Transactions on Communications 35, no. 5 (1987): 556–60. http://dx.doi.org/10.1109/tcom.1987.1096808.

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20

Radfar, M. H., A. H. Banihashemi, R. M. Dansereau, and A. Sayadiyan. "Nonlinear minimum mean square error estimator for mixture-maximisation approximation." Electronics Letters 42, no. 12 (2006): 724. http://dx.doi.org/10.1049/el:20060510.

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21

Wu, Yihong, and Sergio Verdu. "Functional Properties of Minimum Mean-Square Error and Mutual Information." IEEE Transactions on Information Theory 58, no. 3 (March 2012): 1289–301. http://dx.doi.org/10.1109/tit.2011.2174959.

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22

Krall, C., K. Witrisal, G. Leus, and H. Koeppl. "Minimum Mean-Square Error Equalization for Second-Order Volterra Systems." IEEE Transactions on Signal Processing 56, no. 10 (October 2008): 4729–37. http://dx.doi.org/10.1109/tsp.2008.928167.

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23

Sudeep, P. V., P. Palanisamy, Chandrasekharan Kesavadas, and Jeny Rajan. "Nonlocal linear minimum mean square error methods for denoising MRI." Biomedical Signal Processing and Control 20 (July 2015): 125–34. http://dx.doi.org/10.1016/j.bspc.2015.04.015.

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24

Lei Zhang and Xiaolin Wu. "Color demosaicking via directional linear minimum mean square-error estimation." IEEE Transactions on Image Processing 14, no. 12 (December 2005): 2167–78. http://dx.doi.org/10.1109/tip.2005.857260.

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25

Ephraim, Yariv. "Minimum mean‐square error speech enhancement using hidden Markov models." Journal of the Acoustical Society of America 86, S1 (November 1989): S68. http://dx.doi.org/10.1121/1.2027609.

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26

Koloda, Jan, Jurgen Seiler, Antonio M. Peinado, and Andre Kaup. "Scalable Kernel-Based Minimum Mean Square Error Estimator for Accelerated Image Error Concealment." IEEE Transactions on Broadcasting 63, no. 1 (March 2017): 59–70. http://dx.doi.org/10.1109/tbc.2016.2619581.

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27

Xinwang Gui, Xinwang Gui, Michael Anthony Galle Michael Anthony Galle, Li Qian Li Qian, Weilong Liang Weilong Liang, Ciming Zhou Ciming Zhou, Yiwen Ou Yiwen Ou, and Dian Fan Dian Fan. "Demodulation method combining virtual reference interferometry and minimum mean square error for fiber-optic Fabry–Perot sensors." Chinese Optics Letters 16, no. 1 (2018): 010606. http://dx.doi.org/10.3788/col201816.010606.

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28

Shao, Teng. "Distributed filtering in sensor networks based on linear minimum mean square error criterion with limited sensing range." International Journal of Distributed Sensor Networks 18, no. 7 (July 2022): 155013292211108. http://dx.doi.org/10.1177/15501329221110810.

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Abstract:

One of the fundamental problems in sensor networks is to estimate and track the target states of interest that evolve in the sensing field. Distributed filtering is an effective tool to deal with state estimation in which each sensor only communicates information with its neighbors in sensor networks without the requirement of a fusion center. However, in the majority of the existing distributed filters, it is assumed that typically all sensors possess unlimited field of view to observe the target states. This is quite restrictive since practical sensors have limited sensing range. In this article, we consider distributed filtering based on linear minimum mean square error criterion in sensor networks with limited sensing range. To achieve the optimal filter and consensus, two types of strategies based on linear minimum mean square error criterion are proposed, that is, linear minimum mean square error filter based on measurement and linear minimum mean square error filter based on estimate, according to the difference of the neighbor sensor information received by the sensor. In linear minimum mean square error filter based on measurement, the sensor node collects measurement from its neighbors, whereas in linear minimum mean square error filter based on estimate, the sensor node collects estimate from its neighbors. The stability and computational complexity of linear minimum mean square error filter are analyzed. Numerical experimental results further verify the effectiveness of the proposed methods.

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29

LI, Shi-ping, and Long WANG. "Improved sphere decoding detection algorithm combined with minimum mean square error." Journal of Computer Applications 32, no. 2 (March 12, 2013): 385–87. http://dx.doi.org/10.3724/sp.j.1087.2012.00385.

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30

Petra, Nicola, Davide De Caro, Valeria Garofalo, Ettore Napoli, and Antonio G. M. Strollo. "Truncated Binary Multipliers With Variable Correction and Minimum Mean Square Error." IEEE Transactions on Circuits and Systems I: Regular Papers 57, no. 6 (June 2010): 1312–25. http://dx.doi.org/10.1109/tcsi.2009.2033536.

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31

Zahorian, S. A., and A. J. Jagharghi. "Minimum mean-square error transformations of categorical data to target positions." IEEE Transactions on Signal Processing 40, no. 1 (1992): 13–23. http://dx.doi.org/10.1109/78.157177.

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32

Ledoux, Michel. "Heat Flow Derivatives and Minimum Mean-Square Error in Gaussian Noise." IEEE Transactions on Information Theory 62, no. 6 (June 2016): 3401–9. http://dx.doi.org/10.1109/tit.2016.2555809.

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33

Yang, Chuanchuan, Feng Yang, and Ziyu Wang. "Iterative Minimum Mean Square Error Equalization for Optical Fiber Communication Systems." IEEE Photonics Technology Letters 19, no. 19 (October 2007): 1571–73. http://dx.doi.org/10.1109/lpt.2007.905127.

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34

Dongning Guo, Yihong Wu, Shlomo Shamai, and Sergio Verdú. "Estimation in Gaussian Noise: Properties of the Minimum Mean-Square Error." IEEE Transactions on Information Theory 57, no. 4 (April 2011): 2371–85. http://dx.doi.org/10.1109/tit.2011.2111010.

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35

Lee, Jungsik, and Ravi Sankar. "Theoretical derivation of minimum mean square error of RBF based equalizer." Signal Processing 87, no. 7 (July 2007): 1613–25. http://dx.doi.org/10.1016/j.sigpro.2007.01.008.

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36

Yang, X., and Shihab A. Shamma. "Minimum mean square error estimation of connectivity in biological neural networks." Biological Cybernetics 65, no. 3 (July 1991): 171–79. http://dx.doi.org/10.1007/bf00198088.

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37

Nicolson, Aaron, and Kuldip K. Paliwal. "Deep learning for minimum mean-square error approaches to speech enhancement." Speech Communication 111 (August 2019): 44–55. http://dx.doi.org/10.1016/j.specom.2019.06.002.

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38

Hao, Xiaohui, Yan Liang, and Linfeng Xu. "Linear minimum mean square error filtering with stochastic linear equality constraints." International Journal of Systems Science 50, no. 9 (June 10, 2019): 1799–811. http://dx.doi.org/10.1080/00207721.2019.1626932.

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39

Fornaro, G., and A. M. Guarnieri. "Minimum mean square error space-varying filtering of interferometric SAR data." IEEE Transactions on Geoscience and Remote Sensing 40, no. 1 (2002): 11–21. http://dx.doi.org/10.1109/36.981345.

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40

Jianjun, Zhu. "Robust estimate with minimum mean squared error." Australian Surveyor 36, no. 2 (June 1991): 111–15. http://dx.doi.org/10.1080/00050326.1991.10438723.

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41

Lopez-Valcarce, R. "Realizable minimum mean-squared error channel shorteners." IEEE Transactions on Signal Processing 53, no. 11 (November 2005): 4354–62. http://dx.doi.org/10.1109/tsp.2005.857050.

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42

Stoughton, Roland, and Stewart Strait. "Source imaging with minimum mean‐squared error." Journal of the Acoustical Society of America 94, no. 2 (August 1993): 827–34. http://dx.doi.org/10.1121/1.408184.

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43

Dubey, Neha, and Ankit Pandit. "A Comprehensive Review on Channel Estimation in OFDM System." SMART MOVES JOURNAL IJOSCIENCE 5, no. 3 (March 28, 2019): 6. http://dx.doi.org/10.24113/ijoscience.v5i3.201.

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Abstract:

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

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44

Kassabian, Nazelie, Letizia Lo Presti, and Francesco Rispoli. "Augmented GNSS Differential Corrections Minimum Mean Square Error Estimation Sensitivity to Spatial Correlation Modeling Errors." Sensors 14, no. 6 (June 11, 2014): 10258–72. http://dx.doi.org/10.3390/s140610258.

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45

Servadio, Simone, and Renato Zanetti. "Recursive Polynomial Minimum Mean-Square Error Estimation with Applications to Orbit Determination." Journal of Guidance, Control, and Dynamics 43, no. 5 (May 2020): 939–54. http://dx.doi.org/10.2514/1.g004544.

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46

Gaucel, Jean‐Michel, Mireille Guillaume, and Salah Bourennane. "Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy." International Journal of Remote Sensing 29, no. 10 (April 29, 2008): 2943–61. http://dx.doi.org/10.1080/01431160701408428.

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47

Ma, Cong, Xuejun Sha, and Lin Mei. "Hybrid Carrier Order Selection Scheme Based on Minimum Mean Square Error Equalization." IEEE Communications Letters 21, no. 12 (December 2017): 2598–601. http://dx.doi.org/10.1109/lcomm.2017.2748941.

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48

Jia-Shu, Zhang. "Least mean square error difference minimum criterion for adaptive chaotic noise canceller." Chinese Physics 16, no. 2 (January 24, 2007): 352–58. http://dx.doi.org/10.1088/1009-1963/16/2/014.

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49

Martin, R. "Speech enhancement based on minimum mean-square error estimation and supergaussian priors." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 845–56. http://dx.doi.org/10.1109/tsa.2005.851927.

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50

Ephraim, Y., and D. Malah. "Speech enhancement using a minimum mean-square error log-spectral amplitude estimator." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 2 (April 1985): 443–45. http://dx.doi.org/10.1109/tassp.1985.1164550.

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