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Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method

At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of g...

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Autores principales: Xu, Tongtong, Xiang, Zheng, Yang, Hua, Chen, Yun, Luo, Jun, Zhang, Yutao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8222806/
https://www.ncbi.nlm.nih.gov/pubmed/34177513
http://dx.doi.org/10.3389/fnbot.2021.699221
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author Xu, Tongtong
Xiang, Zheng
Yang, Hua
Chen, Yun
Luo, Jun
Zhang, Yutao
author_facet Xu, Tongtong
Xiang, Zheng
Yang, Hua
Chen, Yun
Luo, Jun
Zhang, Yutao
author_sort Xu, Tongtong
collection PubMed
description At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique.
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spelling pubmed-82228062021-06-25 Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method Xu, Tongtong Xiang, Zheng Yang, Hua Chen, Yun Luo, Jun Zhang, Yutao Front Neurorobot Neuroscience At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique. Frontiers Media S.A. 2021-06-10 /pmc/articles/PMC8222806/ /pubmed/34177513 http://dx.doi.org/10.3389/fnbot.2021.699221 Text en Copyright © 2021 Xu, Xiang, Yang, Chen, Luo and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Xu, Tongtong
Xiang, Zheng
Yang, Hua
Chen, Yun
Luo, Jun
Zhang, Yutao
Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title_full Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title_fullStr Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title_full_unstemmed Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title_short Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method
title_sort concurrent modified constant modulus algorithm and decision directed scheme with barzilai-borwein method
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8222806/
https://www.ncbi.nlm.nih.gov/pubmed/34177513
http://dx.doi.org/10.3389/fnbot.2021.699221
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