Scheduling Data Allocation in Packet Based Wireless Communication System Using Data Mining

The purpose of this thesis is scheduling data allocation in large scale networks and in packet based wireless communication system, specifically in the Evolved Packet Core network (EPC) and its various systems and sub-components in order to identify possible areas within the network where machine learning algorithms can be integrated in order to automate some of its tasks. The thesis involves the study of existing literature in order to identify viable machine learning methods that have been successfully integrated into telecommunication networks, and then evaluate whether they are applicable to be utilized within the EPC network. The thesis also involves the introduction of new features and improvements to the network which are also achieved through machine learning. The intention is to explore both supervised and unsupervised learning, depending on the type of data used and tasks that are performed by the network’s various components. This is done through extensive research of the network’s main and support nodes, followed by detailed proposals of implementation of new and existing tasks within the node using machine learning. This thesis aims to find out the data allocation (DA) in packet based wireless communication system using data mining strategies which achieves load balancing and better communication in wireless communication networks from the proportional fairness perspective. We will look for the DA scheduling strategy that benefits wireless communication networks the most. Wireless communication networks have an emerging wireless communication architecture where access nodes of different types are deployed throughout the geographical area to off-load traffic from macro cells to different data allocation for quick communication and this thesis will provide three major contributions to the advance research of communication. For the training, testing and validation of KDD (Knowledge Discovery and Data Mining) Cup 2019 dataset a well-known MATLAB software was used for this purpose. We used Clustering based Algorithm in Data Mining.