Semi-Blind Channel Estimation of MIMO-OFDM Systems Based on Improved PSO-Hopfield neural Network
Author:
Affiliation:
1. Northwest Minzu University, School of Electrical Engineering and Lanzhou university of technology, School of computer and communication, Lanzhou, China
Publisher
ACM Press
Reference20 articles.
1. H. Bolcskei. 2004. Principles of MIMO-OFDM Wireless Systems, Signal Processing for Mobile Communications, New York.
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3. A. Ladaycia, A. Mokraoui and K. Abed-Meraim. 2015. Optimal pilot-aided semi blind channel estimation for MIMO-OFDM system. 2015 Global Conference on Communication Technologies(GCCT), 17(Apr. 2015). 290--293. DOI= https://doi.org/10.1109/GCCT. 2015.7342669.
4. A. Zaib, S. Talwar, J. Tellado, et al. 2016. Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems. IEEE Transactions on Communications, 64(Jul. 2016), 4607--4621. DOI= https://doi.org/10.1109/TCOMM.2016.2593924.
5. E. Eraslan and B. Daneshrad. 2017. Low-Complexity Link Adaptation for Energy Efficiency Maximization in MIMO-OFDM Systems. IEEE Transactions on Wireless Communications, 16(May. 2017), 5102--5114.DOI= https://doi.org/10.1109/TWC.2017.2705706.
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1. Channel Estimation Scheme for MIMO Communication Using Generalized Cholesky Decomposition and Back Substitution Methods;Radioelectronics and Communications Systems;2021-06
2. Integrating Independent Component Analysis with Hopfield Recurrent Neural Network to Estimate the Channel of MIMO-OFDM System;Proceedings of the 2nd International Conference on Vision, Image and Signal Processing;2018-08-27
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