Abstract
Beamforming (BF) is a smart antenna technique to provide a summation of the weighted signal over multi-users to produce the more concentrated transmitted signal from massive MIMO antenna arrays deployed in a Millimeter-Wave (mm-Wave) 5G heterogeneous wireless network. It adjusts the amplitudes and phases of the signals received over different antennas in an optimum manner in the form of directional radiation. This paper will help in the installation of 5G and 6G mm-Wave heterogeneous wireless networks. Here, adaptive BF is designed and being implemented on the Machine Learning (ML) platform using Signal-to-Noise-Interference Ratio (SINR). The four ML methods having six BF properties to estimate the SINR of Multiple-Input-Multiple-Output (MIMO) - mm-Wave 5G wireless network are explored. The proposed algorithm suppresses noise plus interference and can reduce the power consumption. The python package pyArgus focusing on the BF and direction finding algorithms has been used for 20,000 simulations. The BF features namely noise variance, number of antenna elements, distance between antenna elements, azimuth angular range of receiving array, elevation angular range of receiving array and Direction of Arrival (DOA) of signal i.e. incident angle of Signal-of-Interest (SOI) are used in predicting the SINR. The 10-fold cross-validation experiment is performed to assess the robustness of the best predictive method. By conducting the rigorous simulations, it has been observed that Random Forest (RF) method outperforms over the three other ML methods such as Tree model i.e. rpart, Generalized Linear Model (glm) and Neural Network (nnet), which does the prediction inexpensive and faster. The performance analysis parameters’ result represents that the prediction of Mean Absolute Error (MAE) by RF is lowest 70.73 in value, and its Accuracy is maximum 86.40%, in value having the acceptable error on the training-testing data set.
Performance Analysis of Adaptive Beamforming in a MIMO-millimeter Wave 5G Heterogeneous Wireless Network using Machine Learning
