Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model

This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logi...

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Published in:	Heliyon Vol. 10; no. 4; p. e26085
Main Authors:	Sundar, Ramesh, Amir, Mohammad, Subramanian, Ranjith, Prabakar, D., Giri, Jayant, Balachandran, G., Ahmad, Furkan
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 29.02.2024 Elsevier
Subjects:	6G communication algorithms Analog precoder Beamforming DDN Digital precoder Dual deep network energy Energy balancing Hybrid network architecture mathematical models MIMO mmWave Dual deep network mmWave Beamforming 6G communication Hybrid network architecture Digital precoder MIMO Analog precoder Energy balancing DDN
ISSN:	2405-8440, 2405-8440
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Abstract	This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced.
AbstractList	This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced. This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced.This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced.
ArticleNumber	e26085
Author	Ahmad, Furkan Prabakar, D. Amir, Mohammad Giri, Jayant Sundar, Ramesh Balachandran, G. Subramanian, Ranjith
Author_xml	– sequence: 1 givenname: Ramesh surname: Sundar fullname: Sundar, Ramesh email: rameshs5@srmist.edu.in organization: Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology Kattankulathur, Chennai, India – sequence: 2 givenname: Mohammad surname: Amir fullname: Amir, Mohammad email: md.amir@ieee.org organization: Department of Electrical Engineering, Indian Institute of Technology (IIT), Delhi, India – sequence: 3 givenname: Ranjith surname: Subramanian fullname: Subramanian, Ranjith organization: Department of Electronic and Communication Engineering Jeppiaar Engineering College, Chennai, India – sequence: 4 givenname: D. surname: Prabakar fullname: Prabakar, D. organization: Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India – sequence: 5 givenname: Jayant surname: Giri fullname: Giri, Jayant organization: Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India – sequence: 6 givenname: G. surname: Balachandran fullname: Balachandran, G. organization: Department of Electronic and Communication Engineering Jeppiaar Engineering College, Chennai, India – sequence: 7 givenname: Furkan orcidid: 0000-0002-1176-1759 surname: Ahmad fullname: Ahmad, Furkan email: fuahmad@hbku.edu.qa organization: Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Keywords	Dual deep network mmWave Beamforming 6G communication Hybrid network architecture Digital precoder MIMO Analog precoder Energy balancing DDN
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Snippet	This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper,...
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SubjectTerms	6G communication algorithms Analog precoder Beamforming DDN Digital precoder Dual deep network energy Energy balancing Hybrid network architecture mathematical models MIMO mmWave
Title	Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model
URI	https://dx.doi.org/10.1016/j.heliyon.2024.e26085 https://www.ncbi.nlm.nih.gov/pubmed/38390065 https://www.proquest.com/docview/3153833071 https://www.proquest.com/docview/3182878392 https://pubmed.ncbi.nlm.nih.gov/PMC10881838 https://doaj.org/article/fd7acc601a3c4f6498e761953ada7e20
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