Relevant bibliographies by topics / Channel estimation and training

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Channel estimation and training'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Channel estimation and training"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Channel estimation and training"

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Potter, Chris. "Modeling Channel Estimation Error in Continuously Varying MIMO Channels." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604490.

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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>The accuracy of channel estimation plays a crucial role in the demodulation of data symbols sent across an unknown wireless medium. In this work a new analytical expression for the channel estimation error of a multiple input multiple output (MIMO) system is obtained when the wireless medium is continuously changing in the temporal domain. Numerical examples are provided to illustrate our find
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Moosvi, Syed Mohsin Abbas. "Advances in superimposed training for channel estimation and equalisation." Thesis, University of Leeds, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444041.

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Whitworth, Timothy. "Channel estimation, data detection and training design for wireless communications." Thesis, University of Leeds, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493283.

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With modern wireless communications seeking to attain higher data rates, possibly over time-varying channels, there is an urgent need for fast and efficient techniques for accurate channel estimation and data detection. This thesis proposes a number of novel solutions to the aforementioned problem.
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He, Shuangchi. "Doubly-selective channel estimation and equalization using superimposed training and expansion models." Auburn, Ala., 2007. http://repo.lib.auburn.edu/07M%20Dissertations/HE_SHUANGCHI_5.pdf.

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Siyau, M. F. "A Novel training-based MIMO channel estimation scheme for layered space-time systems in frequency selective wireless channels." Thesis, Department of Aerospace, Power & Sensors, 2009. http://hdl.handle.net/1826/3464.

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New development in wireless technology using multiple antennas with appropriate space-time processing has recently become the new frontier of wireless communication systems due to the potential for providing very high spectral efficiency and enormous capacity improvement over the conventional wireless radio communications. The technical advances in using the multiple-input multiple-output (MIMO) wireless links present a promising breakthrough in resolving the bottleneck of current capacity limitation for future intensive wireless networks. The MIMO wireless systems utilize multiple antennas at
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Siyau, Ming Fei. "A novel training-based MIMO channel estimation scheme for layered space-time systems in frequency selective wireless channels." Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/3464.

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New development in wireless technology using multiple antennas with appropriate space-time processing has recently become the new frontier of wireless communication systems due to the potential for providing very high spectral efficiency and enormous capacity improvement over the conventional wireless radio communications. The technical advances in using the multiple-input multiple-output (MIMO) wireless links present a promising breakthrough in resolving the bottleneck of current capacity limitation for future intensive wireless networks. The MIMO wireless systems utilize multiple antennas at
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Nguyen, Nam Tran Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Training signal and precoder dsigns for channel estimation and symbol detection in MIMO and OFDM systems." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/43243.

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Research in wireless communications has been actively carried out in recent years. In order to enable a high data transmission rate, multiple-input multiple-output (MIMO) communications has been proposed and commonly adopted. Accurate channel identification and reliable data detection are major challenges in the implementation of a communications system operating over a wireless fading channel. These issues become even more challenging in MIMO systems since there are many more parameters involved in the estimation processes. This thesis, consisting of four major parts, focuses on applying conv
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Luong, Dung Viet Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Optimal training sequence design for MIMO-OFDM in spatially correlated fading environments." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44513.

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Multiple Input Multiple Output with Orthogonal Frequency Division Multiplexing (MIMOOFDM) has been widely adopted as one of the most promising air interface solutions for future broadband wireless communication systems due to its high rate transmission capability and robustness against multipath fading. However, these MIMO-OFDM advantages cannot be achieved unless the channel state information (CSI) can be obtained accurately and promptly at the receiver to assist coherent detection of data symbols. Channel estimation and training sequence design are, therefore, still open challenges of great
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Kudathanthirige, Dhanushka Priyankara. "DESIGN AND ANALYSIS OF TRANSMISSION STRATEGIES FOR TRAINING-BASED MASSIVE MIMO SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1864.

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The next-generation wireless technologies are currently being researched to address the ever-increasing demands for higher data rates, massive connectivity, improved reliability, and extended coverage. Recently, massive multiple-input multiple-output (MIMO) has gained significant attention as a new physical-layer transmission technology that can achieve unprecedented spectral and energy efficiency gains via aggressive spatial multiplexing. Thus, massive MIMO has been one of the key enabling technologies for the fifth-generation and subsequent wireless standards. This dissertation thus focuses
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Pagadarai, Srikanth. "Wireless Communications and Spectrum Characterization in Impaired Channel Environments." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/33.

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The demand for sophisticated wireless applications capable of conveying information content represented in various forms such as voice, data, audio and video is ever increasing. In order to support such applications, either additional wireless spectrum is needed or advanced signal processing techniques must be employed by the next-generation wireless communication systems. An immediate observation that can be made regarding the first option is that radio frequency spectrum is a limited natural resource. Moreover, since existing spectrum allocation policies of several national regulatory agenci
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Books on the topic "Channel estimation and training"

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Ruprecht, Jürg. Maximum-likelihood estimation of multipath channel. Hartung-Gorre, 1989.

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Casey, Donal. Channel estimation techniques for mobile communications. University College Dublin, 1995.

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Coasts, Oceans, Ports and Rivers Institute (American Society of Civil Engineers). Task Committee on Inland Navigation, ed. Inland navigation: Channel training works. American Society of Civil Engineers, 2013.

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Gao, Feifei, Chengwen Xing, and Gongpu Wang. Channel Estimation for Physical Layer Network Coding Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11668-6.

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Roth, Donald K. Estimation of flood peaks from channel characteristics in Ohio. U.S. Dept. of the Interior, Geological Survey, 1986.

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Marc, Moeneclaey, and Fechtel Stefan, eds. Digital communication receivers: Synchronization, channel estimation and signal processing. Wiley, 1997.

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Meyr, Heinrich, Marc Moeneclaey, and Stefan A. Fechtel. Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing. John Wiley & Sons, Inc., 2001. http://dx.doi.org/10.1002/0471200573.

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Jiang, Yufei, Xu Zhu, Eng Gee Lim, Yi Huang, and Hai Lin. Semi-Blind Carrier Frequency Offset Estimation and Channel Equalization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24984-1.

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Meyr, Heinrich. Digital communication receivers: Synchronization, channel estimation, and signal processing. Wiley, 1998.

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Yin, Xuefeng, and Xiang Cheng. Propagation Channel Characterization, Parameter Estimation, and Modeling for Wireless Communications. John Wiley & Sons Singapore Pte. Ltd, 2018. http://dx.doi.org/10.1002/9781118188248.

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Book chapters on the topic "Channel estimation and training"

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Xing, Huiling, Dongxing Wei, and Fuliang Yin. "Channel Estimation and Tracking Algorithm Using Superimposed Training for Time-Varying MIMO Channels." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_85.

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Narayanarao, Potnuru, Jampana Siddartha Varma, and Kasi Murali Krishna. "New Training Pattern for DFT-Based Channel Estimation in OFDM System." In Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4280-5_64.

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Yang, Zhigang. "Research on the New Channel Estimation Algorithm for MIMO-OFDM System Based on Training Sequence." In Intelligence Computation and Evolutionary Computation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2_88.

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Parihar, Meenal, and Asif Sayeed Khan. "Contemplation and Analysis of MIMO-OFDM Technology with the Augmentation of Training-Based Channel Estimation Techniques." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_42.

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Zhang, Ying-Jun Angela, Congmin Fan, and Xiaojun Yuan. "Scalable Channel Estimation." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15884-2_3.

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Yang, Jie, Shi Jin, Chao-Kai Wen, and Tao Jiang. "Massive MIMO Channel Estimation." In Encyclopedia of Wireless Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_145.

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Yang, Jie, Shi Jin, Chao-Kai Wen, and Tao Jiang. "Massive MIMO Channel Estimation." In Encyclopedia of Wireless Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32903-1_145-1.

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Gao, Feifei, Chengwen Xing, and Gongpu Wang. "Background on Channel Estimation." In Channel Estimation for Physical Layer Network Coding Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11668-6_2.

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Rodríguez, Luis, and Teodoro Sánchez. "Tailrace channel, Commissioning, Staff training." In Designing and Building Mini and Micro Hydro Power Schemes. Practical Action Publishing, 2011. http://dx.doi.org/10.3362/9781780440552.013.

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Ancora, Andrea, Stefania Sesia, and Alex Gorokhov. "Reference Signals and Channel Estimation." In LTE - The UMTS Long Term Evolution. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470978504.ch8.

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Conference papers on the topic "Channel estimation and training"

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Xiaohong Meng and J. K. Tugnait. "MIMO channel estimation using superimposed training." In 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577). IEEE, 2004. http://dx.doi.org/10.1109/icc.2004.1313014.

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Bjornson, Emil, and Bjorn Ottersten. "Training-based Bayesian MIMO channel and channel norm estimation." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960180.

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Wang, Gongpu, Feifei Gao, and Chintha Tellambura. "Channel estimation with amplitude constraint: Superimposed training or conventional training ?" In 2011 12th Canadian Workshop on Information Theory (CWIT). IEEE, 2011. http://dx.doi.org/10.1109/cwit.2011.5872154.

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Whitworth, Tim, Mounir Ghogho, and Desmond C. McLernon. "Training design for doubly-selective channel estimation." In 2007 IEEE 8th Workshop on Signal Processing Advances in Wireless Communications. IEEE, 2007. http://dx.doi.org/10.1109/spawc.2007.4401389.

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Chang, Tsung-Hui, Y. W. Peter Hong, and Chong-Yung Chi. "Training Signal Design for Discriminatory Channel Estimation." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5425919.

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Biguesh, M., and A. B. Gershman. "MIMO channel estimation: optimal training and tradeoffs between estimation techniques." In 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577). IEEE, 2004. http://dx.doi.org/10.1109/icc.2004.1313013.

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Xia Liu, Marek Bialkowski, and Shiyang Lu. "Investigations into training-based MIMO channel estimation for spatial correlated channels." In 2007 IEEE Antennas and Propagation Society International Symposium. IEEE, 2007. http://dx.doi.org/10.1109/aps.2007.4395466.

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Vergara, Victor M., Silvio E. Barbin, and Ramiro Jordan. "Improving MIMO Channel Estimation Through Training Symbols Redundancy." In 2008 IEEE 68th Vehicular Technology Conference (VTC 2008-Fall). IEEE, 2008. http://dx.doi.org/10.1109/vetecf.2008.74.

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Nooralizadeh, Hamid, and Shahriar Shirvani Moghaddam. "Training-based MIMO channel Rice factor estimation algorithms." In 2010 10th International Conference on Signal Processing (ICSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icosp.2010.5656950.

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Wu, Shaohan, and Brian L. Hughes. "Training-based joint channel and antenna impedance estimation." In 2018 52nd Annual Conference on Information Sciences and Systems (CISS). IEEE, 2018. http://dx.doi.org/10.1109/ciss.2018.8362280.

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Reports on the topic "Channel estimation and training"

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Satrk, Wayne E. Channel Coding and Estimation for Ultra-Wideband Radios. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421863.

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Muckler, Frederick A., and Dorothy L. Finley. Applying Training System Estimation Models to Army Training. Volume 1. Analysis of the Literature. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada283021.

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Muckler, Frederick A., and Dorothy L. Finley. Applying Training System Estimation Models to Army Training. Volume 2. An Annotated Bibliography 1970-1990. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada283022.

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Reising, Jack D., and Elizabeth L. Martin. Distance Estimation Training with Night Vision Goggles Under Low Illumination. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada291338.

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Palmer, Richard L. Single Channel Ground and Airborne Radio System (SINCGARS) Basic Operator Training Evaluation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada396163.

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Niall, Keith K., Jack D. Reising, Elizabeth L. Martin, and Marcus H. Gregory. Distance Estimation with Night Vision Goggles: A Direct Feedback Training Method. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328758.

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Young, Derek P., Michael A. Forman, and Donald Ryan Dowdle. The generation of shared cryptographic keys through channel impulse response estimation at 60 GHz. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1008128.

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Nicholson, Nigel R., and Louis W. Buckalew. Single Channel Ground and Airborne Radio System (SINCGARS) Maintainer Training and Performance Evaluation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada396151.

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Pearson, Charles E., and Allen R. Saltus. Remote Sensing Survey and Evaluation of the American Pass and Blue Point Chute Weirs, Atchafalaya Channel Training Project, Louisiana. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada225282.

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Serneels, Pieter, and Stefan Dercon. Aspirations, Poverty and Education: Evidence from India. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/053.

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This paper investigates whether aspirations matter for education, which offers a common route out of poverty. We find that mother aspirations are strongly related to the child’s grade achieved at age 18. The relation is nonlinear, suggesting there is a threshold, and depends on caste, household income and the village setting. The coefficients remain large and significant when applying control function estimation, using firstborn son as instrument. A similar strong relation is observed with learning outcomes, including local language, English and maths test results, and with attending school, b
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