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Journal articles on the topic 'Joint channel estimation and data detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Botsinis, Panagiotis, Dimitrios Alanis, Zunaira Babar, Soon Xin Ng, and Lajos Hanzo. "Joint Quantum-Assisted Channel Estimation and Data Detection." IEEE Access 4 (2016): 7658–81. http://dx.doi.org/10.1109/access.2016.2591903.

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2

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 thi

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3

Longoria-Gandara, Omar, Ramon Parra-Michel, Roberto Carrasco-Alvarez, and Eduardo Romero-Aguirre. "Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3723862.

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This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooper

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4

Tao Cui and C. Tellambura. "Joint data detection and channel estimation for OFDM systems." IEEE Transactions on Communications 54, no. 4 (2006): 670–79. http://dx.doi.org/10.1109/tcomm.2006.873075.

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5

Omidi, M. J., P. G. Gulak, and S. Pasupathy. "Parallel structures for joint channel estimation and data detection over fading channels." IEEE Journal on Selected Areas in Communications 16, no. 9 (1998): 1616–29. http://dx.doi.org/10.1109/49.737631.

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6

Cozzo, C., and B. L. Hughes. "Joint channel estimation and data detection in space-time communications." IEEE Transactions on Communications 51, no. 8 (2003): 1266–70. http://dx.doi.org/10.1109/tcomm.2003.815062.

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7

Jiang, Zhe, Xiaohong Shen, Haiyan Wang, and Zhi Ding. "Joint PSK Data Detection and Channel Estimation Under Frequency Selective Sparse Multipath Channels." IEEE Transactions on Communications 68, no. 5 (2020): 2726–39. http://dx.doi.org/10.1109/tcomm.2020.2975172.

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8

Yang, Ruo-Nan, Wei-Tao Zhang, and Shun-Tian Lou. "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems." Wireless Communications and Mobile Computing 2020 (July 2, 2020): 1–9. http://dx.doi.org/10.1155/2020/2508130.

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In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data

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9

Mohammadian, Amirhossein, and Chintha Tellambura. "Joint Channel and Phase Noise Estimation and Data Detection for GFDM." IEEE Open Journal of the Communications Society 2 (2021): 915–33. http://dx.doi.org/10.1109/ojcoms.2021.3073348.

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10

Sharma, Sanjay, Sanjay Attri, and R. C. Chauhan. "Joint channel estimation and data detection under fading on reconfigurable fabric." Integration 37, no. 3 (2004): 177–89. http://dx.doi.org/10.1016/j.vlsi.2004.01.006.

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11

Besseghier, Mokhtar, Ahmed Bouzidi Djebbar, Abdelhak Zouggaret, and Iyad Dayoub. "Joint channel estimation and data detection for OFDM based cooperative system." Telecommunication Systems 73, no. 4 (2019): 545–56. http://dx.doi.org/10.1007/s11235-019-00622-3.

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12

Jinho Choi. "An EM based Joint Data Detection and Channel Estimation Incorporating with Initial Channel Estimate." IEEE Communications Letters 12, no. 9 (2008): 654–56. http://dx.doi.org/10.1109/lcomm.2008.080731.

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13

Serpedin, Erchin. "Joint Blind Symbol Rate Estimation and Data Symbol Detection for Linearly Modulated Signals." Journal of Communications Software and Systems 5, no. 3 (2009): 101. http://dx.doi.org/10.24138/jcomss.v5i3.204.

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This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearlymodulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the loglikelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate est

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14

Xiang, Luping, Yusha Liu, Thien Van Luong, Robert G. Maunder, Lie-Liang Yang, and Lajos Hanzo. "Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation." IEEE Access 8 (2020): 191910–19. http://dx.doi.org/10.1109/access.2020.3032627.

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15

Kocian, A., and B. H. Fleury. "EM-Based Joint Data Detection and Channel Estimation of DS-CDMA Signals." IEEE Transactions on Communications 51, no. 10 (2003): 1709–20. http://dx.doi.org/10.1109/tcomm.2003.818091.

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16

Chen, Sheng, Xiao-Chen Yang, Lei Chen, and Lajos Hanzo. "Blind joint maximum likelihood channel estimation and data detection for SIMO systems." International Journal of Automation and Computing 4, no. 1 (2007): 47–51. http://dx.doi.org/10.1007/s11633-007-0047-y.

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17

Pham, The-Hanh, A. Nallanathan, and Ying-Chang Liang. "A Computationally Efficient Joint Channel Estimation and Data Detection for SIMO Systems." IEEE Transactions on Wireless Communications 7, no. 11 (2008): 4041–46. http://dx.doi.org/10.1109/t-wc.2008.060934.

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18

Assra, A., W. Hamouda, and A. Youssef. "EM-Based Joint Channel Estimation and Data Detection for MIMO-CDMA Systems." IEEE Transactions on Vehicular Technology 59, no. 3 (2010): 1205–16. http://dx.doi.org/10.1109/tvt.2009.2038925.

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19

Marzook, Ali K., A. Ismail, B. M. Ali, A. Sali, Mohannad H. Khalaf, and S. Khatun. "Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems." Wireless Personal Communications 69, no. 4 (2012): 1629–46. http://dx.doi.org/10.1007/s11277-012-0655-x.

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20

Bangash, Kifayatullah, Imran Khan, Jaime Lloret, and Antonio Leon. "A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems." Electronics 7, no. 10 (2018): 218. http://dx.doi.org/10.3390/electronics7100218.

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Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed alg

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21

Jomon, K. Charly, and S. Prasanth. "Joint channel estimation and data detection in MIMO-OFDM using distributed compressive sensing." Radioelectronics and Communications Systems 60, no. 2 (2017): 80–87. http://dx.doi.org/10.3103/s0735272717020029.

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22

Haidong Zhu, B. Farhang-Boroujeny, and C. Schlegel. "Pilot embedding for joint channel estimation and data detection in MIMO communication systems." IEEE Communications Letters 7, no. 1 (2003): 30–32. http://dx.doi.org/10.1109/lcomm.2002.807434.

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23

The-Hanh Pham, Ying-Chang Liang, and A. Nallanathan. "A joint channel estimation and data detection receiver for multiuser MIMO IFDMA systems." IEEE Transactions on Communications 57, no. 6 (2009): 1857–65. http://dx.doi.org/10.1109/tcomm.2009.06.070555.

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24

Vilaipornsawai, Usa, and Harry Leib. "Joint Data Detection and Channel Estimation for Fading Unknown Time-Varying Doppler Environments." IEEE Transactions on Communications 58, no. 8 (2010): 2277–91. http://dx.doi.org/10.1109/tcomm.2010.08.090511.

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25

Abuthinien, M., S. Chen, and L. Hanzo. "Semi-blind Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems." IEEE Signal Processing Letters 15 (2008): 202–5. http://dx.doi.org/10.1109/lsp.2007.911758.

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26

Akiba, Yuya, Takumi Ishihara, and Shinya Sugiura. "Variable-Block-Length Joint Channel Estimation and Data Detection for Spatial Modulation Over Time-Varying Channels." IEEE Transactions on Vehicular Technology 69, no. 11 (2020): 13964–69. http://dx.doi.org/10.1109/tvt.2020.3023718.

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27

Li, Zan, Fuhui Zhou, Xiaojun Chen, Yuquan Li, and Feifei Gao. "An Adaptive State Assignment Mechanism Based on Joint Data Detection and Channel Estimation on Fading Meteor Channel." IEEE Transactions on Vehicular Technology 66, no. 6 (2017): 4627–35. http://dx.doi.org/10.1109/tvt.2016.2618938.

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28

Castaneda, Oscar, Tom Goldstein, and Christoph Studer. "VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems." IEEE Transactions on Circuits and Systems I: Regular Papers 65, no. 3 (2018): 1120–32. http://dx.doi.org/10.1109/tcsi.2017.2756821.

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29

Ishihara, Takumi, and Shinya Sugiura. "Iterative Frequency-Domain Joint Channel Estimation and Data Detection of Faster-Than-Nyquist Signaling." IEEE Transactions on Wireless Communications 16, no. 9 (2017): 6221–31. http://dx.doi.org/10.1109/twc.2017.2721367.

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30

Moon, Jang-Wook, Tan F. Wong, and John M. Shea. "Pilot-Assisted and Blind Joint Data Detection and Channel Estimation in Partial-Time Jamming." IEEE Transactions on Communications 54, no. 11 (2006): 2092–102. http://dx.doi.org/10.1109/tcomm.2006.881400.

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31

Xiaohong Meng, J. K. Tugnait, and Shuangchi He. "Iterative Joint Channel Estimation and Data Detection Using Superimposed Training: Algorithms and Performance Analysis." IEEE Transactions on Vehicular Technology 56, no. 4 (2007): 1873–80. http://dx.doi.org/10.1109/tvt.2007.897186.

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32

Chen, Sheng, Shinya Sugiura, and Lajos Hanzo. "Semi-Blind Joint Channel Estimation and Data Detection for Space-Time Shift Keying Systems." IEEE Signal Processing Letters 17, no. 12 (2010): 993–96. http://dx.doi.org/10.1109/lsp.2010.2083654.

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33

Wang, Wenwen, and Saman S. Abeysekera. "Joint data detection and channel estimation for coded and uncoded continuous phase modulation signals." Wireless Communications and Mobile Computing 16, no. 2 (2014): 223–35. http://dx.doi.org/10.1002/wcm.2502.

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34

Jing Shen, Muqing Wu, and Linlin Luan. "An Improved Semi-blind Joint Data Detection and Channel Estimation Algorithm for MIMO-OFDM System." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 1 (2012): 371–80. http://dx.doi.org/10.4156/aiss.vol4.issue1.47.

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35

Hijazi, Hussein, and Laurent Ros. "Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains." IEEE Transactions on Communications 58, no. 1 (2010): 170–78. http://dx.doi.org/10.1109/tcomm.2010.01.080296.

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36

Freitas, Walter C., André L. F. de Almeida, and João Paulo C. L. da Costa. "Blind Joint Channel Estimation and Data Detection for Precoded Multi-Layered Space-Frequency MIMO Schemes." Circuits, Systems, and Signal Processing 33, no. 4 (2013): 1215–29. http://dx.doi.org/10.1007/s00034-013-9681-5.

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37

Prasad, Ranjitha, Chandra R. Murthy, and Bhaskar D. Rao. "Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning." IEEE Transactions on Signal Processing 62, no. 14 (2014): 3591–603. http://dx.doi.org/10.1109/tsp.2014.2329272.

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38

Prasad, Ranjitha, Chandra R. Murthy, and Bhaskar D. Rao. "Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach." IEEE Transactions on Signal Processing 63, no. 20 (2015): 5369–82. http://dx.doi.org/10.1109/tsp.2015.2451071.

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39

Kofidis, Eleftherios. "A Tensor-Based Approach to Joint Channel Estimation/Data Detection in Flexible Multicarrier MIMO Systems." IEEE Transactions on Signal Processing 68 (2020): 3179–93. http://dx.doi.org/10.1109/tsp.2020.2994385.

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40

Panayırcı, Erdal, Hakan Doğan, Hakan A. Çırpan, Alexander Kocian, and Bernard H. Fleury. "Iterative joint data detection and channel estimation for uplink MC-CDMA systems in the presence of frequency selective channels." Physical Communication 3, no. 2 (2010): 87–96. http://dx.doi.org/10.1016/j.phycom.2009.06.001.

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41

Di Renna, Roberto B., and Rodrigo C. de Lamare. "Joint Channel Estimation, Activity Detection and Data Decoding Based on Dynamic Message-Scheduling Strategies for mMTC." IEEE Transactions on Communications 70, no. 4 (2022): 2464–79. http://dx.doi.org/10.1109/tcomm.2022.3151775.

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42

Xu, Weiyu, Haider Ali Alshamary, Tareq Al-Naffouri, and Alam Zaib. "Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution." IEEE Transactions on Information Theory 66, no. 3 (2020): 1822–44. http://dx.doi.org/10.1109/tit.2019.2957084.

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43

Kuang, Jing-ming, Yuan Zhou, and Ze-song Fei. "Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems." Frontiers of Information Technology & Electronic Engineering 18, no. 6 (2017): 841–49. http://dx.doi.org/10.1631/fitee.1700025.

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44

Li, Tianya, Yongpeng Wu, Mengfan Zheng, et al. "Joint Device Detection, Channel Estimation, and Data Decoding With Collision Resolution for MIMO Massive Unsourced Random Access." IEEE Journal on Selected Areas in Communications 40, no. 5 (2022): 1535–55. http://dx.doi.org/10.1109/jsac.2022.3145914.

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45

Yuan, Weijie, Nan Wu, Qinghua Guo, Derrick Wing Kwan Ng, Jinhong Yuan, and Lajos Hanzo. "Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access." IEEE Transactions on Communications 68, no. 5 (2020): 2963–77. http://dx.doi.org/10.1109/tcomm.2020.2975169.

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46

Panayirci, E., H. Senol, and H. V. Poor. "Joint Channel Estimation, Equalization, and Data Detection for OFDM Systems in the Presence of Very High Mobility." IEEE Transactions on Signal Processing 58, no. 8 (2010): 4225–38. http://dx.doi.org/10.1109/tsp.2010.2048317.

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47

Kabaoglu, Nihat, Eylem Erdogan, and Erdogan Aydin. "Iterative Receiver with Low Complexity for Downlink Multicarrier Communications over Rapidly Time-Varying Channels." Wireless Communications and Mobile Computing 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/7292019.

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This study proposes an iterative, joint channel estimation, equalization, and data detection method in the presence of high mobility for a multicarrier downlink system that communicates over rapidly time-varying channels. The proposed method uses a basis expansion method (BEM) which has low computational complexity and helps to reduce the number of coefficients needed to represent a time-varying channel and therefore is extremely easy to implement practically. Unlike the current literature, which is almost entirely focused on the uplink communication systems due to their computational costs, t

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48

Novak, Clemens, Gerald Matz, and Franz Hlawatsch. "IDMA for the Multiuser MIMO-OFDM Uplink: A Factor Graph Framework for Joint Data Detection and Channel Estimation." IEEE Transactions on Signal Processing 61, no. 16 (2013): 4051–66. http://dx.doi.org/10.1109/tsp.2013.2261989.

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49

Hong, Xia, Junbin Gao, and Sheng Chen. "Semi-blind joint channel estimation and data detection on sphere manifold for MIMO with high-order QAM signaling." Journal of the Franklin Institute 357, no. 9 (2020): 5680–97. http://dx.doi.org/10.1016/j.jfranklin.2020.04.009.

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50

Rahman, Md Habibur, Mohammad Abrar Shakil Sejan, Seung-Geun Yoo, Min-A. Kim, Young-Hwan You, and Hyoung-Kyu Song. "Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System." Sensors 22, no. 18 (2022): 6994. http://dx.doi.org/10.3390/s22186994.

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Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep ne

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