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

Ramesh Sundar; Mohammad Amir; Ranjith Subramanian; D Prabakar; Jayant Giri; G Balachandran; Furkan Ahmad

doi:10.1016/j.heliyon.2024.e26085

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

Heliyon. 2024 Feb 14;10(4):e26085. doi: 10.1016/j.heliyon.2024.e26085. eCollection 2024 Feb 29.

Authors

Ramesh Sundar¹, Mohammad Amir², Ranjith Subramanian³, D Prabakar⁴, Jayant Giri⁵, G Balachandran³, Furkan Ahmad⁶

Affiliations

¹ Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology Kattankulathur, Chennai, India.
² Department of Electrical Engineering, Indian Institute of Technology (IIT), Delhi, India.
³ Department of Electronic and Communication Engineering Jeppiaar Engineering College, Chennai, India.
⁴ Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
⁵ Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.
⁶ Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

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.

Keywords: 6G communication; Analog precoder; Beamforming; DDN; Digital precoder; Dual deep network; Energy balancing; Hybrid network architecture; MIMO; mmWave.