Handover forecasting in 5G using machine learning - Suresh Kumar, A; Vanmathi, S; Praveen Sanjay, B; Ramya Bharathi, S; Sakthi Meena, M (International Journal of Engineering and Technology, 2018)
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.