Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model
<p>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 mm...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| _version_ | 1864513509350440960 |
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| author | Ramesh Sundar (19326046) |
| author2 | Mohammad Amir (12418899) Ranjith Subramanian (19326049) D. Prabakar (19326052) Jayant Giri (18520811) G. Balachandran (19326055) Furkan Ahmad (709809) |
| author2_role | author author author author author author |
| author_facet | Ramesh Sundar (19326046) Mohammad Amir (12418899) Ranjith Subramanian (19326049) D. Prabakar (19326052) Jayant Giri (18520811) G. Balachandran (19326055) Furkan Ahmad (709809) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ramesh Sundar (19326046) Mohammad Amir (12418899) Ranjith Subramanian (19326049) D. Prabakar (19326052) Jayant Giri (18520811) G. Balachandran (19326055) Furkan Ahmad (709809) |
| dc.date.none.fl_str_mv | 2024-02-17T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.heliyon.2024.e26085 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Spectral_energy_balancing_system_with_massive_MIMO_based_hybrid_beam_forming_for_wireless_6G_communication_using_dual_deep_learning_model/26491180 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Machine learning MIMO Dual deep network DDN Beamforming Hybrid network architecture Digital precoder 6G communication Energy balancing mmWave |
| dc.title.none.fl_str_mv | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Heliyon<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.heliyon.2024.e26085" target="_blank">https://dx.doi.org/10.1016/j.heliyon.2024.e26085</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6d010f2e75098b9008deb11f4f3ab443 |
| identifier_str_mv | 10.1016/j.heliyon.2024.e26085 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26491180 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning modelRamesh Sundar (19326046)Mohammad Amir (12418899)Ranjith Subramanian (19326049)D. Prabakar (19326052)Jayant Giri (18520811)G. Balachandran (19326055)Furkan Ahmad (709809)EngineeringCommunications engineeringInformation and computing sciencesMachine learningMIMODual deep networkDDNBeamformingHybrid network architectureDigital precoder6G communicationEnergy balancingmmWave<p>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.</p><h2>Other Information</h2> <p> Published in: Heliyon<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.heliyon.2024.e26085" target="_blank">https://dx.doi.org/10.1016/j.heliyon.2024.e26085</a></p>2024-02-17T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.heliyon.2024.e26085https://figshare.com/articles/journal_contribution/Spectral_energy_balancing_system_with_massive_MIMO_based_hybrid_beam_forming_for_wireless_6G_communication_using_dual_deep_learning_model/26491180CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264911802024-02-17T09:00:00Z |
| spellingShingle | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model Ramesh Sundar (19326046) Engineering Communications engineering Information and computing sciences Machine learning MIMO Dual deep network DDN Beamforming Hybrid network architecture Digital precoder 6G communication Energy balancing mmWave |
| status_str | publishedVersion |
| title | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| title_full | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| title_fullStr | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| title_full_unstemmed | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| title_short | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| title_sort | Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model |
| topic | Engineering Communications engineering Information and computing sciences Machine learning MIMO Dual deep network DDN Beamforming Hybrid network architecture Digital precoder 6G communication Energy balancing mmWave |