Performance Analysis of Initialization Algorithms of Deep Neural Network Based Coordinated Beamforming System for mmWave

As mmWave has a wide range of applications, it has drawn a significant amount of attention in recent years. It has already been introduced in the next generation wireless communication system. In practice, it shows some shortcomings and most of these are eliminated by introducing beamforming which utilizes the spatial diversity enabled by Massive MIMO. Still, there are a few challenges in designing an efficient system for highly mobile users and making sure proper coverage and reliability. In this research, a machine learning-based coordinated beamforming technique has been explored that supports highly mobile applications in mmWave systems with massive antenna arrays. The optimization of the deep learning model itself can increase the system performance as well as reduce the computational time complexity. The purpose of this work was to optimize the deep learning model and recommend proper initialization method to maximize the system performance. We found that for Xavier normal initialization algorithm the effective achievable rate is the highest for the least amount of data.