Design Analysis of Adaptive Beamforming in a MIMO-millimeter Wave 5G Heterogeneous Wireless Network using Machine Learning Models

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.

Multi-Band Heterogeneous Wireless Network Architecture for Industrial Automation: A Techno-Economic Analysis

To attain automation across different applications, industries are beginning to leverage advancements in wireless communication technologies. A "one-size-fits-all" solution cannot be applied since wireless technologies are selected according to application needs, quality of service requirements, and economic restrictions. To balance the trade-off between technical and economic requirements, a multi-band heterogeneous wireless network architecture is presented and discussed in this paper. Wireless local area network (WLAN) and distributed antenna system (DAS) with Long Term Evolution (LTE) are considered as the backbone for the multi-band heterogeneous network into which other wireless technologies can be integrated. The technical and economic feasibility of the network are evaluated through a techno-economic analysis (TEA). The economic feasibility of the proposed network is measured in terms of net present value while the technical feasibility is measured in terms of network throughput and latency. Finally, network performance for DAS with LTE and WLAN are verified using an NS3 simulator for machine-to-machine, real-time video, and high-definition video data transmissions. The TEA analysis showed that the number of DAS units required to achieve technical feasibility is less than WLAN units, but the overall cost of DAS units are higher compared to WLAN units, even without taking into consideration industrial, scientific, and medical band technologies.