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Journal articles on the topic 'Blind channel estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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1

Baykal, B. "Blind Channel Estimation via Combining Autocorrelation and Blind Phase Estimation." IEEE Transactions on Circuits and Systems I: Regular Papers 51, no. 6 (2004): 1125–31. http://dx.doi.org/10.1109/tcsi.2004.829235.

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2

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

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3

Althahab, Awwab Qasim Jumaah, and Sameer Abdul Kadhim Alrufaiaat. "A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel." Journal of University of Babylon for Engineering Sciences 27, no. 1 (2019): 262–74. http://dx.doi.org/10.29196/jubes.v27i1.1995.

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The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy.

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4

Yatawatta, S., A. P. Petropulu, and R. Dattani. "Blind Channel Estimation Using Fractional Sampling." IEEE Transactions on Vehicular Technology 53, no. 2 (2004): 363–71. http://dx.doi.org/10.1109/tvt.2004.823551.

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5

Alireza Banani, Seyed, and Rodney G. Vaughan. "OFDM With Iterative Blind Channel Estimation." IEEE Transactions on Vehicular Technology 59, no. 9 (2010): 4298–308. http://dx.doi.org/10.1109/tvt.2010.2080295.

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6

Yih-Jyi Jeng and Chien-Chung Yeh. "Cluster-based blind nonlinear-channel estimation." IEEE Transactions on Signal Processing 45, no. 5 (1997): 1161–72. http://dx.doi.org/10.1109/78.575691.

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7

Xiaohua Li. "Blind sequence detection without channel estimation." IEEE Transactions on Signal Processing 50, no. 7 (2002): 1735–46. http://dx.doi.org/10.1109/tsp.2002.1011213.

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8

Peken, Ture, Garrett Vanhoy, and Tamal Bose. "Blind channel estimation for massive MIMO." Analog Integrated Circuits and Signal Processing 91, no. 2 (2017): 257–66. http://dx.doi.org/10.1007/s10470-017-0943-1.

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9

Tami, Abdelkader, Mokhtar Keche, and Boubaker S. Bouazza. "New OSTBC for Blind Channel Estimation and Tracking in MIMO-OFDM Systems." Journal of Telecommunications and Information Technology 3 (September 30, 2019): 49–57. http://dx.doi.org/10.26636/jtit.2019.133819.

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Applying orthogonal space time block coding (OSTBC) to multiple-input multiple-output (MIMO) systems helps reduce receiver complexity. However, this approach has been applied only to ﬂat fading channels, as when the channel is a frequency selective fading MIMO channel, OSTBC cannot be used directly since its orthogonal propriety may be lost. Furthermore, the MIMO channel is not always known. To deal with this problem, many techniques were proposed to estimate the channel using a training sequence. Unfortunately, these techniques reduce the useful spectral bandwidth. This paper proposes OSTBC for blind channel estimation and data detection in the case of a MIMO frequency selective channel. The aim of this new OSTBC is twofold: to solve the ambiguity of channel estimation and to reduce the complexity of the detector. By exploiting the well-known technique of orthogonal frequency division multiplexing (OFDM), the frequency selective fading MIMO channel is split into a set of ﬂat fading subchannels. Moreover, to accommodate the fact that a MIMO channel can be time varying, the steady state Kalman channel estimator (SS-KCE) is extended to track the channel’s fast variations. The performance of the proposed blind algorithm is related by the adequate choice of the number of subcarriers and it is compared with other existing approaches by means of Monte Carlo simulations.

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10

Li, Cong Ying, Gao Ming Huang, Jun Gao, Gao Qi Dou, Chun Quan He, and Yu Song Gao. "Semi-Blind Channel Estimation with Orthogonal Superimposed Training for SISO over Doubly Selective Channel." Applied Mechanics and Materials 701-702 (December 2014): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.1029.

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In this paper, a semi-blind estimator based on orthogonal superimposed training (OST) for the case of doubly selecting fading channel is proposed. With the aid of OST, the interference to channel estimation form data symbols can be eliminated completely. Moreover, data symbols can serve as pseudo-pilots to enhance the estimation performance. Simulation results illustrate the benefits of pseudo-pilots method.

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11

Karakutuk, Serkan, and T. Engin Tuncer. "Channel Matrix Recursion for Blind Effective Channel Order Estimation." IEEE Transactions on Signal Processing 59, no. 4 (2011): 1642–53. http://dx.doi.org/10.1109/tsp.2010.2100384.

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12

Banani, S. A., and R. G. Vaughan. "Blind channel estimation for equalisation in dispersive fading channel." IET Communications 5, no. 11 (2011): 1577–86. http://dx.doi.org/10.1049/iet-com.2010.0730.

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13

Alaghbari, Khaled Abdulaziz, Lim Heng Siong, and Alan W. C. Tan. "Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (2015): 962–78. http://dx.doi.org/10.1108/compel-08-2014-0199.

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Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

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14

Zhao, Zheng, Ying Jia, and Qinye Yin. "Blind channel estimation in delay diversity for frequency selective channels." Journal of Electronics (China) 20, no. 5 (2003): 326–36. http://dx.doi.org/10.1007/s11767-003-0042-6.

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15

Jiang, Yin Ping, and Tao Zhang. "On Improved Noise Variance Estimation for Aeronautical Communications." Applied Mechanics and Materials 336-338 (July 2013): 1688–93. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1688.

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The L-band Digital Aeronautical Communication System Type-1 (L-DACS1) has been considered as the potential standard for future aeronautical communications. As an important parameter for L-DACS1, the noise variance can be estimated via the well known Second-Moment-Fourth-Moment (M2M4) blind algorithm. However, due to influence from aeronautical fading channels, performance degradation is encountered by conventional M2M4 algorithm in L-DACS1. In this paper, channel responses obtained from L-DACS1 pilot symbols were carried out to compensate the estimation degradation brought by channel fading, which lead to an improved M2M4 algorithm for L-DACS1. It is shown through simulations that the proposed approach outperforms conventional M2M4 estimator in terms of estimation error for L-DACS1 in aeronautical fading channels.

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16

Host-Madsen, A., X. Wang, and S. Bahng. "Asymptotic Analysis of Blind Multiuser Detection with Blind Channel Estimation." IEEE Transactions on Signal Processing 52, no. 6 (2004): 1722–38. http://dx.doi.org/10.1109/tsp.2004.827158.

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17

Shin, Changyong, Robert W. Heath, and Edward J. Powers. "Blind Channel Estimation for MIMO-OFDM Systems." IEEE Transactions on Vehicular Technology 56, no. 2 (2007): 670–85. http://dx.doi.org/10.1109/tvt.2007.891429.

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18

Ayadi, Jaouhar, and Dirk T. M. Slock. "Blind channel estimation exploiting transmission filter knowledge." Signal Processing 80, no. 10 (2000): 2049–62. http://dx.doi.org/10.1016/s0165-1684(00)00069-4.

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19

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

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20

Doukopoulos, X. G., and G. V. Moustakides. "Blind adaptive channel estimation in ofdm systems." IEEE Transactions on Wireless Communications 5, no. 7 (2006): 1716–25. http://dx.doi.org/10.1109/twc.2006.1673083.

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21

Tanaka, Hironori, Hayato Sogo, Nobuo Iwasaki, Takanori Matsuzaki, Hiroshi Shiratsuchi, and Hiromu Gotanda. "Blind Channel Estimation for QAM-OFDM Systems." Journal of Signal Processing 18, no. 2 (2014): 77–88. http://dx.doi.org/10.2299/jsp.18.77.

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22

Dean, Thomas R., Mary Wootters, and Andrea J. Goldsmith. "Blind Joint MIMO Channel Estimation and Decoding." IEEE Transactions on Information Theory 65, no. 4 (2019): 2507–24. http://dx.doi.org/10.1109/tit.2018.2878016.

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23

Kimothi, Megha, Vivek Kumar Gupta, and S. C. Gupta S.C.Gupta. "Performance Enhancement of CPMIMO-OFDM Channel using Blind Channel Estimation." International Journal of Computer Applications 118, no. 21 (2015): 14–18. http://dx.doi.org/10.5120/20869-3299.

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24

Li, Xiaotian, Jing Lei, Wei Liu, Erbao Li, and Yanbin Li. "Blind Channel Estimation Based on Multilevel Lloyd-Max Iteration for Nonconstant Modulus Constellations." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/146207.

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In wireless communications, knowledge of channel coefficients is required for coherent demodulation. Lloyd-Max iteration is an innovative blind channel estimation method for narrowband fading channels. In this paper, it is proved that blind channel estimation based on single-level Lloyd-Max (SL-LM) iteration is not reliable for nonconstant modulus constellations (NMC). Then, we introduce multilevel Lloyd-Max (ML-LM) iteration to solve this problem. Firstly, by dividing NMC into subsets, Lloyd-Max iteration is used in multilevel. Then, the estimation information is transmitted from one level to another. By doing this, accurate blind channel estimation for NMC is achieved. Moreover, when the number of received symbols is small, we propose the lacking constellations equalization algorithm to reduce the influence of lacking constellations. Finally, phase ambiguity of ML-LM iteration is also investigated in the paper. ML-LM iteration can be more robust to the phase of fading coefficient by dividing NMC into subsets properly. As the signal-to-noise ratio (SNR) increases, numerical results show that the proposed method’s mean-square error curve converges remarkably to the least squares (LS) bound with a small number of iterations.

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25

Wang, Xu, Tao Yang, and Bo Hu. "Low-complexity fractional phase estimation for totally blind channel estimation." Journal of Systems Engineering and Electronics 26, no. 2 (2015): 232–40. http://dx.doi.org/10.1109/jsee.2015.00028.

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26

Matad, Sidramayya S., and Ramesha K. "Precoding Aided Data Correlation Scheme for Channel Estimation Technique in MIMO-OFDM System." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (February 23, 2021): 146–54. http://dx.doi.org/10.37394/23203.2021.16.11.

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Channel estimation is considered as an important phase in Multiple Input Multiple Output – Orthogonal Frequency Division Multiplexing (MIMO-OFDM) networks which can enhances the performance significantly. Channel estimation widely classified as pilot based, blind and semi-blind channel estimation. The pilot-based channel estimation decreases the data transmission rate and spectral efficiency. To overcome these issues of existing schemes, we present a novel blind channel estimation technique. According to proposed scheme, we transmit the data in a block-wise manner. The proposed scheme uses precoding technique to establish the correlation between these blocks. Further, we use channel correlation to solve the diagonal uncertainty of correlation matrix which helps to improve the system performance. We present a comparative analysis study which shows that proposed approach can achieve better performance in terms of Normalized Mean Square Error (NMSE) and Mean Square Error (MSE) when compared with existing techniques.

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27

Bhandari, Renuka, and Sangeeta Jadhav. "Spectral Efficient Blind Channel Estimation Technique for MIMO-OFDM Communications." International Journal of Advances in Applied Sciences 7, no. 3 (2018): 286. http://dx.doi.org/10.11591/ijaas.v7.i3.pp286-297.

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<p><em>With emerge of increasing research in the domain of future wireless communications, massive MIMO (multiple inputs multiple outputs) attracted most of researchers interests. Massive MIMO is high-speed wireless communication standards. A channel estimation technology plays the essential role in the MIMO systems. Efficient channel estimation leads to spectral efficient wireless communications. The critics of Inter-Symbol Interference (ISI) are the challenging tasks while designing the channel estimation methods. To mitigate the challenges of ISI, we proposed the novel blind channel estimation method which based on Independent component analysis (ICA) in this paper. Proposed channel estimation it works for both blind interference cancellation and ISI cancellation. The proposed Hybrid ICA (HICA) method depends on pulse shape filtering and ambiguity removal to improve the spectral efficiency and reliability for MIMO communications. The Kurtosis operation is used to measure the complex data at first to estimate the common signals. Then we exploited the advantages of 3rd and 4th order Higher Order Statistics (HOS) to priorities the common signals during the channel estimation. In this paper, we present the detailed design and evaluation of HICA blind channel estimation method. We showed the simulation results of HICA against the state-of-art techniques for channel estimation using BER, MSE, and PAPR.</em><em></em></p>

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28

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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29

Muquet, B., M. de Courville, and P. Duhamel. "Subspace-based blind and semi-blind channel estimation for OFDM systems." IEEE Transactions on Signal Processing 50, no. 7 (2002): 1699–712. http://dx.doi.org/10.1109/tsp.2002.1011210.

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30

Patil, Gajanan R., and Vishwanath K. Kokate. "Joint Channel Estimation and Data Detection for STBC MIMO-OFDM Wireless Communication System." Journal of Circuits, Systems and Computers 24, no. 04 (2015): 1550059. http://dx.doi.org/10.1142/s0218126615500590.

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This paper presents a joint channel estimation and data detection technique for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Initial estimate of the channel is obtained using semi-blind channel estimation (SBCE). The whitening rotation (WR)-based orthogonal pilot maximum likelihood (OPML) method is used to obtain the channel estimate. The estimate is further enhanced by extracting information through the received data symbols. The performance of the proposed estimator is studied under various channel models. The simulation study shows that this approach gives better performance over training-based channel estimation (TBCE) and OPML SBCE methods but at the cost of higher computational complexity.

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31

Badi, Imad, Mohammed Boutalline, and Said Safi. "Blind Identification of Transmission Channel with the method of Higher-OrderCummulants." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 2 (2013): 1294–301. http://dx.doi.org/10.24297/ijct.v10i2.6998.

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The modern telecommunication systems require very high transmission rates, in this context, the problem of channels identification is a challenge major. The use of blind techniques is a great interest to have the best compromise between a suitable bit rate and quality of the information retrieved. In this paper, we are interested to learn the algorithms for blind channel identification. We propose a hybrid method that performs a trade-off between two existing methods in order to improve the channel estimation.

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32

HALMI, Mohd Hairi, Mohd Yusoff ALIAS, and Teong Chee CHUAH. "Semi-Blind Channel Covariance Estimation for MIMO Precoding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A, no. 2 (2011): 833–37. http://dx.doi.org/10.1587/transfun.e94.a.833.

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33

Chen, Wei, and Weile Zhu. "Blind channel estimation for power line OFDM systems." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 25, no. 6 (2011): 512–15. http://dx.doi.org/10.3724/sp.j.1187.2011.00512.

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34

Yongming, Liang, Luo Hanwen, Wu Yadong, and Huang Jianguo. "Blind channel estimation for redundant precoded OFDM systems." Journal of Systems Engineering and Electronics 18, no. 4 (2007): 692–97. http://dx.doi.org/10.1016/s1004-4132(08)60005-5.

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35

Petropulu, A., R. Zhang, and R. Lin. "Blind OFDM Channel Estimation Through Simple Linear Precoding." IEEE Transactions on Wireless Communications 3, no. 2 (2004): 647–55. http://dx.doi.org/10.1109/twc.2003.821140.

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36

Jagannatham, A. K., and B. D. Rao. "Whitening-rotation-based semi-blind MIMO channel estimation." IEEE Transactions on Signal Processing 54, no. 3 (2006): 861–69. http://dx.doi.org/10.1109/tsp.2005.862908.

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37

Via, J., and I. Santamaria. "Correlation Matching Approaches for Blind OSTBC Channel Estimation." IEEE Transactions on Signal Processing 56, no. 12 (2008): 5950–61. http://dx.doi.org/10.1109/tsp.2008.929661.

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38

Torlak, M., and Guanghan Xu. "Blind multiuser channel estimation in asynchronous CDMA systems." IEEE Transactions on Signal Processing 45, no. 1 (1997): 137–47. http://dx.doi.org/10.1109/78.552212.

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39

Ma, Shuo, Gongpu Wang, Rongfei Fan, and Chintha Tellambura. "Blind Channel Estimation for Ambient Backscatter Communication Systems." IEEE Communications Letters 22, no. 6 (2018): 1296–99. http://dx.doi.org/10.1109/lcomm.2018.2817555.

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40

Tugnait, J. K. "Blind estimation of digital communication channel impulse response." IEEE Transactions on Communications 42, no. 2/3/4 (1994): 1606–16. http://dx.doi.org/10.1109/tcomm.1994.582855.

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41

Roy, S., and Hongbo Yan. "Blind channel estimation in multi-rate CDMA systems." IEEE Transactions on Communications 50, no. 6 (2002): 995–1004. http://dx.doi.org/10.1109/tcomm.2002.1010619.

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42

Yu, Chengpu, Lihua Xie, and Yeng Chai Soh. "Blind Channel and Source Estimation in Networked Systems." IEEE Transactions on Signal Processing 62, no. 17 (2014): 4611–26. http://dx.doi.org/10.1109/tsp.2014.2338837.

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43

Terré, M., L. Féty, I. Ahriz, R. Maoudj, L. Martinod, and P. Mege. "Blind channel estimation for FBMC-based PMR transmission." Transactions on Emerging Telecommunications Technologies 28, no. 3 (2016): e3080. http://dx.doi.org/10.1002/ett.3080.

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44

Harada, Koji, and Hideaki Sakai. "A Variational Bayes Approach to Blind Channel Estimation." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2010 (May 5, 2010): 95–99. http://dx.doi.org/10.5687/sss.2010.95.

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45

Sogo, Hayato, Hironori Tanaka, Takanori Matsuzaki, Hiroshi Shiratsuchi, and Hiromu Gotanda. "Semi-Blind Channel Estimation for QAM-OFDM Systems." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2014 (May 5, 2014): 337–45. http://dx.doi.org/10.5687/sss.2014.337.

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46

Qing Zhao and L. Tong. "Adaptive blind channel estimation by least squares smoothing." IEEE Transactions on Signal Processing 47, no. 11 (1999): 3000–3012. http://dx.doi.org/10.1109/78.796435.

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47

Abdallah, Saeed, and Abdollah Masoud Darya. "Semi-Blind Channel Estimation for Diffusive Molecular Communication." IEEE Communications Letters 24, no. 11 (2020): 2503–7. http://dx.doi.org/10.1109/lcomm.2020.3011108.

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48

Baykal, B., and A. G. Constantinides. "Matched filtering for CMA-based blind channel estimation." Electronics Letters 39, no. 17 (2003): 1285. http://dx.doi.org/10.1049/el:20030807.

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49

Gao, Chunyan, Ming Zhao, Shidong Zhou, and Yan Yao. "Blind channel estimation algorithm for MIMO-OFDM systems." Electronics Letters 39, no. 19 (2003): 1420. http://dx.doi.org/10.1049/el:20030918.

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50

Bhalani, J., D. Chauhan, Y. P. Kosta, and A. I. Trivedi. "Novel semi-blind channel estimation schemes for Rician fading MIMO channel." Radioelectronics and Communications Systems 55, no. 4 (2012): 149–56. http://dx.doi.org/10.3103/s0735272712040012.

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