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.
Millimeter Wave (mm-Wave) - massive Multiple-Input Multiple-Output (MIMO) technology has been a subject of today’s growing interest in both industry and academia for future wireless standards and has significant potential to provide considerable gains in data rates, link reliability and Energy Efficiency (EE).Sparse recovery has great capability in Channel Estimation (CE) for mm-Wave - Massive-MIMO (Ma-MIMO) heterogeneous wireless networks and in this context; the existing better candidate “Orthogonal Matching Pursuit” (OMP) algorithm is modified for CE in such networks. This paper will provide an opportunity in setting up of such a network, and the practical observation of the effect of a change in threshold, sparsity and noise levels as well as quantity of Radio Frequency (RF) chain systems. The performance analysis of CE accuracy in terms of Normalized Mean Square Error (NMSE)verses a given Signal-to-Noise (SNR) range in dB is calculated using this modified algorithm, much better than the existing OMP methods and compared against the ideal Genie case under a wide range of noise encountered. It has been observed that in a very high noise environment, NMSE of this noise resistant algorithm is approximate (100.5 to 10-3.5) in low SNR range (-10 to 30) dB and approximate (10-1.5 to10-5.5) in high SNR range (10 to 50) dB. It comes out to be approximate (10-1 to more than 10-5) in case of combined effect involvingthe reduction of quantity of RF chain systems to half and 10 times enhancement of threshold level in a very high noise environment and low SNR range.
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