Estimation of Signal to Noise Ratio in 5G Communication through ML based Predictive Models

In wireless communication, the Signal to Noise Ratio of the received signal plays an instrumental role in deciding the quality of reception. So, for network optimization, its accurate estimation is paramount. The prime goal of this work is predictive modelling pertaining to SNR in 5G communication using Machine Learning (ML) algorithms. Two different ML techniques which have been employed in this work using MATLAB are Gaussian Process Regression (GPR) and Least Squares Boosting (LSBoost). The GPR is a robust technique based on probabilistic predictions, on the other LSBoost belongs to the family of ensemble learning techniques. As there are several parameters on which the SNR of a 5G system depends, the corresponding dataset is highly non-linear and complex. So, the conventional techniques of estimation can’t give promising results in this case. This research elaborates the application of machine learning algorithms in making accurate predictions in such cases. The findings of this work could be effectively employed in optimization and resource allocation in 5G networks.