Handover forecasting in 5G using machine learning

Abstract

Communication plays a major role in human's life. Without network communication can't be done, to achieve proper and effective communication different generations of networks are introduced. Each generation has its own features and perspective of communication, but till now there is no network properly makes people to communicate. Many researches says 5G network will rule the network world as it satisfies all the effective network goals. This paper is proposed to obtain all the goals of a communication network by making proper handover with the help of machine learning. Here we have used two main algorithms to make our 5G handover process by clustering and classifying. Clustering is a process of making the datasets into single units of every users and classification is a process of classifying user's clustered datasets into common path using prediction and forecasting. For clustering we are using K-means and for classification we are using Random Forest algorithm. By using the algorithms the datasets which is being predicted and forecasted is stored in the cloud. Here cloud technology is used as a platform for developing datasets associated with internet. 5G network adapts to any form technology easier and here we have used all the essential technologies under machine learning. This paper deals with all the above methodologies effectively with newer combinations of algorithms along with proper solutions.

An Adaptive TTT Handover (ATH) Mechanism for Dual Connectivity (5G mmWave—LTE Advanced) during Unpredictable Wireless Channel Behavior

“…Kumar et al [30] presented two main methods, known as clustering and classifying, to build the 5G handover technique. Clustering is a process of grouping the datasets into separate units, and classification is a process of classifying user's clustered datasets into a common path, using prediction and forecasting.…”

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

“…Traditional cell-to-cell handovers rely on measurements of target cell radio strength, which necessitates frequent measurement gaps. Therefore, a handover solution based on machine learning was proposed in [27][28][29]; also, prediction-based handover strategies are recommended to reduce the number of measurement windows as in [30], an ultrafast and effective XGBoostbased predictive handover technique for next-generation mobile communications was presented. Another fascinating study about conditional handover was proposed in [31].…”