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

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%.