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Published in: Optical and Quantum Electronics 1/2024

01-01-2024

Massive MIMO based beamforming by optical multi-hop communication with energy efficiency for smart grid IoT 5G application

Authors: Asha Rajiv, Pankaj Kuamr Goswami, Rajesh Gupta, Suraj Malik, Usha Chauhan, Anil Agarwal

Published in: Optical and Quantum Electronics | Issue 1/2024

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Abstract

Operators are forced to investigate various capacity enhancement options as a result of the rapid increase in mobile network data volume. As a result, modern 5G networks became more difficult to deploy and manage. As a result, self-organizing capabilities must be enabled in order to simplify network design and management. Massive MIMO (multiple input, multiple output) network-based beamforming analysis and network energy efficiency are the goals of this study. The proposed model uses optical multi-hop communication and a single cell encoder-based hybrid convolutional outlier extreme learning to develop the Beamforming analysis for the 5G network in massive MIMO. The organization energy proficiency is upgraded by savvy matrix IoT (Internet of things) engineering. In terms of Signal to Noise Ratio (SNR), Bit Error Rate (BER), Computational Time, Spectrum Efficiency, and Energy Efficiency, the experimental analysis is carried out. the proposed technique attained SNR of 46%, BER of 43%, Computational time of 53%, Spectrum efficiency of 96%, Energy efficiency of 98%.

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Literature
go back to reference Ahmed, I., Shahid, M.K., Faisal, T.: Deep reinforcement learning based beam selection for hybrid beamforming and user grouping in massive MIMO-NOMA system. IEEE Access 10, 89519–89533 (2022)CrossRef Ahmed, I., Shahid, M.K., Faisal, T.: Deep reinforcement learning based beam selection for hybrid beamforming and user grouping in massive MIMO-NOMA system. IEEE Access 10, 89519–89533 (2022)CrossRef
go back to reference Arjoune, Y., Faruque, S.: Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications. Phys. Commun. 51, 101534 (2022)CrossRef Arjoune, Y., Faruque, S.: Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications. Phys. Commun. 51, 101534 (2022)CrossRef
go back to reference Devnikar, R., & Hendre, V.: Comprehensive literature survey for mm-wave massive mimo using machine learning for 6G. In: ICCCE 2021: Proceedings of the 4th international conference on communications and cyber physical engineering. Springer Nature, Singapore. pp. 765–774 (2022 May) Devnikar, R., & Hendre, V.: Comprehensive literature survey for mm-wave massive mimo using machine learning for 6G. In: ICCCE 2021: Proceedings of the 4th international conference on communications and cyber physical engineering. Springer Nature, Singapore. pp. 765–774 (2022 May)
go back to reference Fowdur, T.P., Doorgakant, B.: A review of machine learning techniques for enhanced energy efficient 5G and 6G communications. Eng. Appl. Artif. Intell. 122, 106032 (2023)CrossRef Fowdur, T.P., Doorgakant, B.: A review of machine learning techniques for enhanced energy efficient 5G and 6G communications. Eng. Appl. Artif. Intell. 122, 106032 (2023)CrossRef
go back to reference Ghiasi, N., Mashhadi, S., Farahmand, S., Razavizadeh, S.M., Lee, I.: Energy efficient AP selection for cell-free massive MIMO systems: Deep reinforcement learning approach. IEEE Trans. Green Commun. Netw. 7(1), 29–41 (2022)CrossRef Ghiasi, N., Mashhadi, S., Farahmand, S., Razavizadeh, S.M., Lee, I.: Energy efficient AP selection for cell-free massive MIMO systems: Deep reinforcement learning approach. IEEE Trans. Green Commun. Netw. 7(1), 29–41 (2022)CrossRef
go back to reference Gkonis, P.K.: A survey on machine learning techniques for massive mimo configurations: application areas, performance limitations and future challenges. IEEE Access 11, 67–88 (2022)CrossRef Gkonis, P.K.: A survey on machine learning techniques for massive mimo configurations: application areas, performance limitations and future challenges. IEEE Access 11, 67–88 (2022)CrossRef
go back to reference Hasan, M.K., Hosain, M.S., Saha, T., Islam, S., Paul, L.C., Khatak, S., Hassan, R.: Energy efficient data detection with low complexity for an uplink multi-user massive MIMO system. Comput. Elect. Eng. 101, 108045 (2022)CrossRef Hasan, M.K., Hosain, M.S., Saha, T., Islam, S., Paul, L.C., Khatak, S., Hassan, R.: Energy efficient data detection with low complexity for an uplink multi-user massive MIMO system. Comput. Elect. Eng. 101, 108045 (2022)CrossRef
go back to reference Iliadis, L.A., Zaharis, Z.D., Sotiroudis, S., Sarigiannidis, P., Karagiannidis, G.K., Goudos, S.K.: The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems. EURASIP J. Wirel. Commun. Netw. 2022(1), 68 (2022)CrossRef Iliadis, L.A., Zaharis, Z.D., Sotiroudis, S., Sarigiannidis, P., Karagiannidis, G.K., Goudos, S.K.: The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems. EURASIP J. Wirel. Commun. Netw. 2022(1), 68 (2022)CrossRef
go back to reference Imoize, A.L., Obakhena, H.I., Anyasi, F.I., Sur, S.N.: A review of energy efficiency and power control schemes in ultra-dense cell-free massive mimo systems for sustainable 6g wireless communication. Sustainability 14(17), 11100 (2022)CrossRef Imoize, A.L., Obakhena, H.I., Anyasi, F.I., Sur, S.N.: A review of energy efficiency and power control schemes in ultra-dense cell-free massive mimo systems for sustainable 6g wireless communication. Sustainability 14(17), 11100 (2022)CrossRef
go back to reference Lavdas, S., Gkonis, P.K., Zinonos, Z., Trakadas, P., Sarakis, L., Papadopoulos, K.: A machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multicellular networks. IEEE Access 10, 91597–91609 (2022a)CrossRef Lavdas, S., Gkonis, P.K., Zinonos, Z., Trakadas, P., Sarakis, L., Papadopoulos, K.: A machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multicellular networks. IEEE Access 10, 91597–91609 (2022a)CrossRef
go back to reference Lavdas, S., Gkonis, P., Zinonos, Z., Trakadas, P., & Sarakis, L.: Throughput based adaptive beamforming in 5G millimeter wave massive MIMO cellular networks via machine learning. In: 2022b IEEE 95th Vehicular Technology Conference: (VTC2022b-Spring) (pp. 1–7). IEEE (2022b, June) Lavdas, S., Gkonis, P., Zinonos, Z., Trakadas, P., & Sarakis, L.: Throughput based adaptive beamforming in 5G millimeter wave massive MIMO cellular networks via machine learning. In: 2022b IEEE 95th Vehicular Technology Conference: (VTC2022b-Spring) (pp. 1–7). IEEE (2022b, June)
go back to reference Lu, Z., Zhang, X., He, H., Wang, J., Song, J.: Binarized aggregated network with quantization: flexible deep learning deployment for CSI feedback in massive MIMO systems. IEEE Trans. Wireless Commun. 21(7), 5514–5525 (2022)CrossRef Lu, Z., Zhang, X., He, H., Wang, J., Song, J.: Binarized aggregated network with quantization: flexible deep learning deployment for CSI feedback in massive MIMO systems. IEEE Trans. Wireless Commun. 21(7), 5514–5525 (2022)CrossRef
go back to reference Mathur, S., Chaba, Y., Noliya, A.: Performance analysis of support vector machine learning based carrier aggregation resource scheduling in 5g mobile communication. Procedia Comput. Sci. 218, 2776–2785 (2023)CrossRef Mathur, S., Chaba, Y., Noliya, A.: Performance analysis of support vector machine learning based carrier aggregation resource scheduling in 5g mobile communication. Procedia Comput. Sci. 218, 2776–2785 (2023)CrossRef
go back to reference Murshed, R. U., Ashraf, Z. B., Hridhon, A. H., Munasinghe, K., Jamalipour, A., & Hossain, M. D.: A CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming (2022) Murshed, R. U., Ashraf, Z. B., Hridhon, A. H., Munasinghe, K., Jamalipour, A., & Hossain, M. D.: A CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming (2022)
go back to reference Nouri, M., Behroozi, H., Bastami, H., Moradikia, M., Jafarieh, A., Abdelhadi, A., & Han, Z.: Hybrid Precoding Based on Active Learning for mmWave Massive MIMO Communication Systems. IEEE Transactions on Communications (2023) Nouri, M., Behroozi, H., Bastami, H., Moradikia, M., Jafarieh, A., Abdelhadi, A., & Han, Z.: Hybrid Precoding Based on Active Learning for mmWave Massive MIMO Communication Systems. IEEE Transactions on Communications (2023)
go back to reference Nwachukwu, S. E., Chepkoech, M., Lysko, A. A., Awodele, K., Mwangama, J., Burger, C. R. Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: a review. In: 2022 IEEE 7th forum on research and technologies for society and industry innovation (RTSI) (pp. 154–160) (2022, August). IEEE. (2022b, June) Nwachukwu, S. E., Chepkoech, M., Lysko, A. A., Awodele, K., Mwangama, J., Burger, C. R. Integration of massive MIMO and machine learning in the present and future of power consumption in wireless networks: a review. In: 2022 IEEE 7th forum on research and technologies for society and industry innovation (RTSI) (pp. 154–160) (2022, August). IEEE. (2022b, June)
go back to reference Özbay, E.: Deep learning aided parametric channel covariance matrix estimation for millimeter wave hybrid massive mimo (2022). Özbay, E.: Deep learning aided parametric channel covariance matrix estimation for millimeter wave hybrid massive mimo (2022).
go back to reference Sharma, H., Kumar, N.: Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: a survey. Physical Communication, 102002 (2023) Sharma, H., Kumar, N.: Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: a survey. Physical Communication, 102002 (2023)
go back to reference Yadav, S. S., Hiremath, S., Surisetti, P., Kumar, V., Patra, S. K.: Application of Machine Learning Framework for Next‐Generation Wireless Networks: Challenges and Case Studies. handbook of intelligent computing and optimization for sustainable development 81–99. (2022), Yadav, S. S., Hiremath, S., Surisetti, P., Kumar, V., Patra, S. K.: Application of Machine Learning Framework for Next‐Generation Wireless Networks: Challenges and Case Studies. handbook of intelligent computing and optimization for sustainable development 81–99. (2022),
Metadata
Title
Massive MIMO based beamforming by optical multi-hop communication with energy efficiency for smart grid IoT 5G application
Authors
Asha Rajiv
Pankaj Kuamr Goswami
Rajesh Gupta
Suraj Malik
Usha Chauhan
Anil Agarwal
Publication date
01-01-2024
Publisher
Springer US
Published in
Optical and Quantum Electronics / Issue 1/2024
Print ISSN: 0306-8919
Electronic ISSN: 1572-817X
DOI
https://doi.org/10.1007/s11082-023-05286-7

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