?Generic Datasets, Beamforming Vectors Prediction of 5G Celleular Networks

Abstract

The early stages of 5G evolution revolves around delivering higher data speeds, latency improvements and the functional redesign of mobile networks to enable greater agility, efficiency and openness. The millimeter-wave (mmWave) massive multiple-input-multiple-output (massive MIMO) system is one of the dominant technology that consistently features in the list of the 5G enablers and opens up new frontiers of services and applications for next-generation 5G cellular networks. The mmWave massive MIMO technology shows potentials to significantly raise user throughput, enhances spectral and energy efficiencies and increases the capacity of mobile networks using the joint capabilities of the huge available bandwidth in the mmWave frequency bands and high multiplexing gains achievable with massive antenna arrays.

In this report, we present the preliminary outcomes of research on mmWave massive MIMO (as research on this subject is still in the exploratory phase) and study two papers related to the Millimeter Wave (mmwave) and massive MIMO for next-gen 5G wireless systems. We focus on how a generic dataset uses accurate real-world measurements using ray tracing data and how machine learning/Deep learning can find correlations for better beam prediction vectors through this ray tracing data. We also study a generated deep learning model to be trained using TensorFlow and Google Collaboratory.