Performance Analysis of Adaptive Beamforming in a MIMO-millimeter Wave
5G Heterogeneous Wireless Network using Machine Learning
Abstract
Beamforming determines the quality of received signal by an antenna
array using Signal-to-Noise-Interference Ratio (SINR) in cellular base
stations. This paper will help in the installation of current
heterogeneous wireless networks. Here, adaptive BF is implemented on the
Machine Learning (ML) platform. The applicable ML methods to estimate
the SINR of Multiple-Input-Multiple-Output (MIMO-mm-Wave) 5G wireless
network are explored. The significant BF features are used in predicting
the SINR. The cross-validation experiment is performed to assess the
robustness of the best predictive method. The performance analysis
parameters’ result shows the maximum value of accuracy, in value having
the acceptable error on the data set.