Machine Learning for RF Slicing Using CSI Prediction in Software Defined Large-Scale MIMO Wireless Networks

Abstract

In this paper, we investigate the machine learning approaches (sparse Bayesian linear regression (SBLR) and support vector machine (SVM)) for channel state information (CSI) prediction and dynamic radio frequency (RF) slicing for software defined virtual wireless networks in large-scale multi-input multi-output (MIMO) wireless networks. Specifically, a subset of the antennas of virtual wireless networks transmits pilot symbols for estimating the CSI and use the estimated CSI dataset to train and estimate the remaining channels and future CSI for virtual networks using machine learning algorithms. This helps not only to predict the CSI with least overhead and fulfills the service demands of users but also to reduce the power consumption and computation overhead in the network. Predicted CSI is leveraged for RF slicing for virtual wireless networks. Simulation results show that the proposed SBLR for predicting CSI results in lower BER and higher data rate for the wireless users. Furthermore, SBLR outperforms the other approaches when we have sparse CSI information and we need to generalize the prediction process.