Enhancement of Signal to Interference plus Noise Ratio Prediction (SINR) in 5G Networks using a Machine Learning Approach

Enhancement of Signal to Interference plus Noise Ratio Prediction (SINR) in 5G Networks using a Machine Learning Approach

© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : N'Goran Franck Raoul Olivier Konan, Elijah Mwangi, Ciira Maina
DOI : 10.14445/22315381/IJETT-V70I10P231

How to Cite?

N'Goran Franck Raoul Olivier Konan, Elijah Mwangi, Ciira Maina, "Enhancement of Signal to Interference plus Noise Ratio Prediction (SINR) in 5G Networks using a Machine Learning Approach," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 319-328, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P231

Due to the increasing number of connected users and devices, 5G networks require higher data rates for various applications. However, its applications or services and other next-generation wireless networks still need resources such as large bandwidth, less latency, and reduced interferences to improve their efficiency. The efficient management of resources is one of the main challenges in the network itself. Monitoring an important parameter like the Signal-to Interference plus NoiseRatio helps to minimize the wastage of radio resources due to poor channel conditions. It is worthwhile to take advantage of machine learning for predicting channel conditions. Therefore, knowing the channel conditions helps efficiently utilize the network's resources. There are existing papers in the literature dealing with the same prediction of Signal-to-Interference plus Noise Ration, but the presented results of their models are insufficient enough for a real-world scenario of a 5G network. The proposed method is based on supervised learning using logistic regression. Batch Gradient Descent has been applied as an optimization algorithm for better results. The application of this algorithm helped to obtain minimum loss and great accuracy. The research objective is to obtain higher accuracy and minimize the loss function of the prediction model compared to the most recently reported works in the literature. The predicted Signal-to-Interference-plus-Noise-Ratio is a discrete value for each established connection between the mobile station and the base station. After a simulation of 1000 epochs, results obtained showed an accuracy of 0.90 and an error of 0.1, indicating some significant improvement over other works reported in the literature.

5G, Artificial Intelligence, Logistic Regression, Machine learning, Signal to Interference plus Noise Ratio.

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