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Journal articles on the topic 'Channel estimation and training'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Orozco-Lugo, A. G., M. M. Lara, and D. C. McLernon. "Channel Estimation Using Implicit Training." IEEE Transactions on Signal Processing 52, no. 1 (2004): 240–54. http://dx.doi.org/10.1109/tsp.2003.819993.

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2

González-Coma, José P., Pedro Suárez-Casal, Paula M. Castro, and Luis Castedo. "FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems." Sensors 20, no. 3 (2020): 930. http://dx.doi.org/10.3390/s20030930.

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A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the

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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

Huang, Sheng Bo, Yu Cang Wen, Wen Ye, and Tong Liang Fan. "A Simplified Time-Domain Channel Estimation Approach for OFDM System." Applied Mechanics and Materials 738-739 (March 2015): 1111–14. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1111.

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OFDM usually incorporates pilot tones in the frequency domain (FD) or training symbols in the time-domain (TD) to facilitate channel estimation algorithms. TD channel estimation becomes more attractive in quasi-static channels because channel estimation scheme will optimize the spectral efficiency by re-using the training symbols designated for FD channel estimation. A channel estimation method based on time domain averaging algorithm is proposed. Due to the principle of centralized energy in time domain, the effective channel impulse response length can be detected by setting of threshold for

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5

Xu, Xiaoyan, Jianjun Wu, Shubo Ren, Lingyang Song, and Haige Xiang. "Superimposed Training-Based Channel Estimation for MIMO Relay Networks." International Journal of Antennas and Propagation 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/698748.

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We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO) amplify-and-forward (AF) one-way relay network (OWRN) to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for th

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6

Meng, Xin, Ruo Mei Wang, and She Xiang Ma. "Training Sequence Design Based on Channel Estimation." Advanced Materials Research 787 (September 2013): 1097–104. http://dx.doi.org/10.4028/www.scientific.net/amr.787.1097.

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Channel estimation is an important research direction in wireless communications; channel estimation based on the training sequence is the most commonly used method. Different training sequence is very different from the performance of channel estimation, in order to improve channel estimation accuracy. This paper analyzes the common m sequence and from the optimal channel estimated performance point design of applicable in any given length of sequence. The sequence, respectively, under the traditional channel estimated algorithm and based on compressed sensing to channel estimation algorithm

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7

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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8

Wang, Wei, and Chunyan Han. "H∞Channel Estimation for DS-CDMA Systems: A Partial Difference Equation Approach." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/307342.

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In the communications literature, a number of different algorithms have been proposed for channel estimation problems with the statistics of the channel noise and observation noise exactly known. In practical systems, however, the channel parameters are often estimated using training sequences which lead to the statistics of the channel noise difficult to obtain. Moreover, the received signals are corrupted not only by the ambient noises but also by multiple-access interferences, so the statistics of observation noises is also difficult to obtain. In this paper, we will investigate theH∞channe

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9

Alameda-Hernández, E., D. C. McLernon, A. G. Orozco-Lugo, M. Lara, and M. Ghogho. "Synchronisation for superimposed training based channel estimation." Electronics Letters 41, no. 9 (2005): 565. http://dx.doi.org/10.1049/el:20050900.

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10

Beyene, Yihenew, Kalle Ruttik, and Riku Jäntti. "Angular Domain Data-Assisted Channel Estimation for Pilot Decontamination in Massive MIMO." Mobile Information Systems 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2785948.

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Massive Multiple-Input-Multiple-Output (M-MIMO) system is a promising technology that offers to mobile networks substantial increase in throughput. In Time-Division Duplexing (TDD), the uplink training allows a Base Station (BS) to acquire Channel State Information (CSI) for both uplink reception and downlink transmission. This is essential for M-MIMO systems where downlink training pilots would consume large portion of the bandwidth. In densely populated areas, pilot symbols are reused among neighboring cells. Pilot contamination is the fundamental bottleneck on the performance of M-MIMO syst

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11

Wu, Lin Jun. "Quantization Error's Influence on the Channel Estimation." Advanced Materials Research 846-847 (November 2013): 898–901. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.898.

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To improve the transmission performance of wireless channel, the improvement of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) channel estimation algorithm based on the superimposed training sequence is proposed. We take advantage of training and information sequences unrelated, without loss of bandwidth preliminary estimate the channel parameters; Then using the weighted factor of channel parameters between adjacent signal weighted average, get the final channel parameters. Compared with previous superimposed training sequence estimation method, usin

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12

Bandari, Shravan Kumar, Venkata Mani Vakamulla, and A. Drosopoulos. "Training Based Channel Estimation for Multitaper GFDM System." Mobile Information Systems 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4747256.

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Recent activities in the cellular network world clearly show the need to design new physical layer waveforms in order to meet future wireless requirements. Generalized Frequency Division Multiplexing (GFDM) is one of the leading candidates for 5G and one of its key features is the usage of circular pulse shaping of subcarriers to remove prototype filter transients. Due to the nonorthogonal nature of the conventional GFDM system, inherent interference will affect adversely channel estimation. With Discrete Prolate Spheroidal Sequences (DPSSs) or multitapers as prototype filters an improved orth

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13

Liu, Xia, and Marek E. Bialkowski. "Optimization of Training Signal Transmission for Estimating MIMO Channel under Antenna Mutual Coupling Conditions." International Journal of Antennas and Propagation 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/213576.

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This paper reports investigations on the effect of antenna mutual coupling on performance of training-based Multiple-Input Multiple-Output (MIMO) channel estimation. The influence of mutual coupling is assessed for two training-based channel estimation methods, Scaled Least Square (SLS) and Minimum Mean Square Error (MMSE). It is shown that the accuracy of MIMO channel estimation is governed by the sum of eigenvalues of channel correlation matrix which in turn is influenced by the mutual coupling in transmitting and receiving array antennas. A water-filling-based procedure is proposed to optim

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14

Imani, Maryam, and Hamidreza Bakhshi. "Training Based Channel Estimation in MIMO-OFDM Systems." Communications and Network 04, no. 01 (2012): 54–60. http://dx.doi.org/10.4236/cn.2012.41008.

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15

Chen, D., Y. Hong, and K. S. Trivedi. "Optimal Estimation of Training Interval for Channel Equalization." IEEE Transactions on Wireless Communications 3, no. 5 (2004): 1844–53. http://dx.doi.org/10.1109/twc.2004.828021.

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16

Hlaing Minn and N. Al-Dhahir. "Optimal training signals for MIMO OFDM channel estimation." IEEE Transactions on Wireless Communications 5, no. 5 (2006): 1158–68. http://dx.doi.org/10.1109/twc.2006.1633369.

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17

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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18

Mansoor, Babar, Moazzam Islam Tiwana, Syed Junaid Nawaz, Abrar Ahmed, Abdul Haseeb, and Ataul Aziz Ikram. "Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit." Elektronika ir Elektrotechnika 25, no. 4 (2019): 81–87. http://dx.doi.org/10.5755/j01.eie.25.4.23975.

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Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates

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19

Xiang, Changqing, Xi Yang, Xuan Zhou, Xiaojun Zhu, Jincan Xu, and Qiong He. "Improved Training Sequence Channel Estimation Scheme in 16QAM MB-OFDM UWBoF System." Advances in Condensed Matter Physics 2020 (October 13, 2020): 1–7. http://dx.doi.org/10.1155/2020/6273892.

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In this paper, an improved channel estimation scheme based on time-domain orthogonal gray complementary training sequence (Golay TS) is proposed to resist subcarrier mutual beat interference in 16-quadrature amplitude modulation multiband orthogonal-frequency-division-multiplexing ultrawide band over fiber (16QAM MB-OFDM UWBoF) systems. The simulation results showed that the performance of system with the improved Golay TS channel estimation scheme was improved by about 1 dB compared with the traditional Golay TS channel estimation scheme, at a bit error rate (BER) of 3.8 × 10−3 after 70 km st

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20

Huiling Xing, Dongxing Wei, and Fuliang Yin. "A Novel Orthogonal Superimposed Training-based channel estimation method for MIMO Channels." International Journal of Advancements in Computing Technology 4, no. 7 (2012): 10–17. http://dx.doi.org/10.4156/ijact.vol4.issue7.2.

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21

Yang, Jie, Hua Min Zhang, and Wu Xie. "An Improved Channel Estimation Algorithm Based on Superimposed Training Sequence." Advanced Materials Research 179-180 (January 2011): 482–89. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.482.

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A channel estimation algorithm based on superimposed training sequence is proposed in this paper. The algorithm does not need the allocation of time slot for superimposed training sequence, and it can estimate the channel coefficients without loss of bandwidth. The mean squared difference, Cramer-Rao bound and the lower bound of channel capacity are deduced based on the LS algorithm of channel estimation. It has simple structure, less computational requirement by using the LS algorithm. The result of simulation shows that the performance of system is improved to a large extent by using superim

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22

Carrasco-Alvarez, Roberto, R. Parra-Michel, Aldo G. Orozco-Lugo, and Jitendra K. Tugnait. "Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training." IEEE Transactions on Signal Processing 60, no. 8 (2012): 4439–43. http://dx.doi.org/10.1109/tsp.2012.2195658.

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23

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 f

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24

Longoria-Gandara, O., R. Parra-Michel, M. Bazdresch, and A. G. Orozco-Lugo. "Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation." International Journal of Digital Multimedia Broadcasting 2008 (2008): 1–9. http://dx.doi.org/10.1155/2008/535269.

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This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRS

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25

Yoon, Eunchul, Soonbum Kwon, Unil Yun, and Sun-Yong Kim. "Doppler Spread Estimation Based on Machine Learning for an OFDM System." Wireless Communications and Mobile Computing 2021 (September 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/5586029.

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In this paper, we propose a Doppler spread estimation approach based on machine learning for an OFDM system. We present a carefully designed neural network architecture to achieve good performance in a mixed-channel scenario in which channel characteristic variables such as Rician K factor, azimuth angle of arrival (AOA) width, mean direction of azimuth AOA, and channel estimation errors are randomly generated. When preprocessing the channel state information (CSI) collected under the mixed-channel scenario, we propose averaged power spectral density (PSD) sequence as high-quality training dat

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26

Fragouli, C., N. Al-Dhahir, and W. Turin. "Training-based channel estimation for multiple-antenna broadband transmissions." IEEE Transactions on Wireless Communications 2, no. 2 (2003): 384–91. http://dx.doi.org/10.1109/twc.2003.809454.

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27

Rousseaux, O., G. Leus, P. Stoica, and M. Moonen. "Gaussian maximum-likelihood channel estimation with short training sequences." IEEE Transactions on Wireless Communications 4, no. 6 (2005): 2945–55. http://dx.doi.org/10.1109/twc.2005.858353.

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28

Soltanalian, Mojtaba, Mohammad Mahdi Naghsh, Nafiseh Shariati, Petre Stoica, and Babak Hassibi. "Training Signal Design for Correlated Massive MIMO Channel Estimation." IEEE Transactions on Wireless Communications 16, no. 2 (2017): 1135–43. http://dx.doi.org/10.1109/twc.2016.2639485.

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29

Tchamk, A., and I. E. Telatar. "On the use of training sequences for channel estimation." IEEE Transactions on Information Theory 52, no. 3 (2006): 1171–76. http://dx.doi.org/10.1109/tit.2005.864468.

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30

Shariati, Nafiseh, Jiaheng Wang, and Mats Bengtsson. "Robust Training Sequence Design for Correlated MIMO Channel Estimation." IEEE Transactions on Signal Processing 62, no. 1 (2014): 107–20. http://dx.doi.org/10.1109/tsp.2013.2284763.

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31

Zhang, Chengjin, and Robert R. Bitmead. "Subspace system identification for training-based MIMO channel estimation." Automatica 41, no. 9 (2005): 1623–32. http://dx.doi.org/10.1016/j.automatica.2005.04.010.

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32

Liu, Beiyi, Guan Gui, Shin-ya Matsushita, and Li Xu. "Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (2017): 153–58. http://dx.doi.org/10.20965/jaciii.2017.p0153.

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Sparse multipath channel estimation has recently attracted significant attention due to the sparsity of the channel in broadband wireless communication. Many algorithms have been proposed for sparse multipath channel estimation. Among them, the least mean square (LMS) algorithm, based on adaptive filter, has attracted much attention due to its low complexity and high robustness. However, LMS is usually degraded by the long training signal, which needs large storage space. This paper proposes an improved method that transmits a circulating, short training signal, samples the received signal at

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33

N. Tran, Nguyen, Dang L. Khoa, Vo T. Tri, and Ha X. Nguyen. "Optimal SP Training and Precoding Designs for Channel Estimation and Symbol Detection in MIMO-OFDM Systems." Journal of Science and Technology: Issue on Information and Communications Technology 1 (August 31, 2015): 34. http://dx.doi.org/10.31130/jst.2015.9.

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Based on convex programming for optimization, a closed-from solution of superimposed (SP) training on linearly precoded data for jointly optimal channel estimation and symbol detection is proposed in this paper for MIMO-OFDM systems. The newly designed method not only efficiently identifies the frequency-selective fading channel but also effectively enhances the symbol detection. Although linearly precoding technique is employed and protects the transmitted data over the multi-path MIMO wireless channels, the precoded data is always arithmetically added to the training sequence to prevail tran

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34

Cui, Ying Hua, and Hua Wang. "The G_S Channel Estimation Algorithm of GPS Interference Suppression." Applied Mechanics and Materials 556-562 (May 2014): 4469–72. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4469.

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Global Positioning System (GPS) becomes one of the key technologies in precise navigation and position area. Interference Suppression technology of GPS plays an essential role in the accuracy of GPS positioning. In this paper, we proposed a more efficient scheme based on Gram-Schmidt algorithm with the training sequence, which is named the GSCE algorithm (the Gram-Schmidt Channel estimation algorithm). Compared with some other existing methods, the algorithm improves the positioning precision of the GPS by estimating channel parameters.

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35

Srivastav, Prateek Saurabh, Lan Chen, and Arfan Haider Wahla. "Precise Channel Estimation Approach for a mmWave MIMO System." Applied Sciences 10, no. 12 (2020): 4397. http://dx.doi.org/10.3390/app10124397.

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Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to expl

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36

Du, Jianhe, Jiaqi Li, Jing He, Yalin Guan, and Heyun Lin. "Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems." Electronics 9, no. 2 (2020): 301. http://dx.doi.org/10.3390/electronics9020301.

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For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm pro

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37

Yan, Hui, and Zhong Pei Zhang. "A Low-Complexity Algorithm of Joint Multiple Frequency Offsets and Channels Estimation in Cooperative Relay Systems." Advanced Materials Research 748 (August 2013): 1046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1046.

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A low complexity joint estimator of multiple CFOs and Channels is presented in cooperative relay systems. The new algorithm first utilizes correlation-based frequency estimator to get CFOs initial estimation, and then serial interference cancellation based on correlation properties of training sequence in different relays is done to obtain the initial channel estimation. Moreover, a parallel iteration scheme with interference cancellation is proposed to reduce time complexity of the traditional serial iteration. In the overall process, matrix inversion is avoided. Thus, the complexity of the p

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38

Chen, Jin Lun. "An Adaptive Multiple LS Channel Estimation Method for MIMO System." Applied Mechanics and Materials 239-240 (December 2012): 1084–88. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1084.

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Accurate channel estimation plays a key role in multiple-input and multiple-output (MIMO) communication systems. In this paper, we firstly discuss the popular linear least squares (LS) channel estimation and the multiple LS (MLS) channel estimation. Then an adaptive multiple LS (AMLS) channel estimate approach is proposed. Using numerical simulation, it is found that the proposed estimation method outperforms LS and MLS for a wide range of training SNRs.

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39

Chang, Miao Miao, Jin He Zhou, and Ju Rong Wang. "Research of Improved Algorithm for MIMO Channel Estimation." Applied Mechanics and Materials 475-476 (December 2013): 893–99. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.893.

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We introduced an improved singular value decomposition (SVD) channel estimation algorithm for multiple-input multiple-output (MIMO) wireless communication system. The algorithm is supposed to solve the issue that the channel estimation result is not accurate when the training sequences have some 0 elements. The improvement is also applicable in the other channel estimation algorithms. We made some comparisons between the linear least squares (LS) and the linear minimum mean square error (LMMSE) channel estimation, the traditional singular value decomposition and the improved SVD algorithm to d

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40

TAKEUCHI, Keigo, Ralf R. MÜLLER, and Mikko VEHKAPERÄ. "Bias-Based Training for Iterative Channel Estimation and Data Decoding in Fast Fading Channels." IEICE Transactions on Communications E94-B, no. 7 (2011): 2161–65. http://dx.doi.org/10.1587/transcom.e94.b.2161.

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41

Lu, Zhen, and Jian-hua Ge. "Time-domain training sequences design for MIMO OFDM channel estimation." Journal of Zhejiang University-SCIENCE A 9, no. 4 (2008): 464–69. http://dx.doi.org/10.1631/jzus.a071444.

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42

Estrada-Jimenez, Juan Carlos, Borja Genoves Guzman, M. Julia Fernandez-Getino Garcia, and Victor P. Gil Jimenez. "Superimposed Training-Based Channel Estimation for MISO Optical-OFDM VLC." IEEE Transactions on Vehicular Technology 68, no. 6 (2019): 6161–66. http://dx.doi.org/10.1109/tvt.2019.2909428.

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43

Xu, Weizhang, Xinle Yu, Yanfei Li, Lu Si, and Zhanxin Yang. "A novel training sequence applied to DCS-based channel estimation." China Communications 15, no. 11 (2018): 70–78. http://dx.doi.org/10.1109/cc.2018.8543050.

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44

He, Shuangchi, Jitendra K. Tugnait, and Xiaohong Meng. "On Superimposed Training for MIMO Channel Estimation and Symbol Detection." IEEE Transactions on Signal Processing 55, no. 6 (2007): 3007–21. http://dx.doi.org/10.1109/tsp.2007.893941.

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45

Ciblat, Philippe, Pascal Bianchi, and Mounir Ghogho. "Training Sequence Optimization for Joint Channel and Frequency Offset Estimation." IEEE Transactions on Signal Processing 56, no. 8 (2008): 3424–36. http://dx.doi.org/10.1109/tsp.2008.921712.

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46

Chiang, Chin-Te, and Carrson C. Fung. "Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation." IEEE Transactions on Vehicular Technology 60, no. 7 (2011): 2882–94. http://dx.doi.org/10.1109/tvt.2011.2161640.

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47

Tugnait, J. K., and Weilin Luo. "On channel estimation using superimposed training and first-order statistics." IEEE Communications Letters 7, no. 9 (2003): 413–15. http://dx.doi.org/10.1109/lcomm.2003.817325.

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48

Gedik, Berna, Osama Amin, and Murat Uysal. "Power allocation for cooperative systems with training-aided channel estimation." IEEE Transactions on Wireless Communications 8, no. 9 (2009): 4773–83. http://dx.doi.org/10.1109/twc.2009.081382.

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49

Bogale, Tadilo Endeshaw, Long Bao Le, and Xianbin Wang. "Hybrid Analog-Digital Channel Estimation and Beamforming: Training-Throughput Tradeoff." IEEE Transactions on Communications 63, no. 12 (2015): 5235–49. http://dx.doi.org/10.1109/tcomm.2015.2495191.

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Ghogho, M. "Channel and DC-offset estimation using data-dependent superimposed training." Electronics Letters 41, no. 22 (2005): 1250. http://dx.doi.org/10.1049/el:20052346.

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