Machine Learning-Based Paging Enhancement in 5G Network

Abstract

Recently, technologies such as big data, artificial intelligence, and machine learning have been applied to intelligently and effectively operate fourth-generation (4G) and fifth-generation (5G) network systems. In particular, we are interested in using them in 4G mobility management entities and 5G access and mobility management functions (AMFs), where functional enhancement or performance improvement is required. This paper presents an enhanced paging approach based on supervised machine learning and a Markov process for the performance improvement of paging in 5G AMFs. User equipment (UE) profile information in 5G AMFs classifies subscribers into two types using a UE classifier model with k-nearest neighbors (KNN)-supervised learning. In this paper, UE movement data between next-generation NodeBs (gNodeBs) are analyzed, and the Markov process is applied to construct a transition probabilistic model. When a UE moves to an adjacent gNodeB in the 5G connection management-idle state, a method for predicting the gNodeB movement is required to perform paging effectively on the predicted gNodeBs. In the proposed paging method, the AMF applies the UE profile information to the KNN-supervised learning model and classifies the subscriber UE type. In addition, on the UE movement between gNodeBs statistics, it generates state-transition probabilities and then performs paging on the gNodeB list. Experimentally, the paging responses and signals of the proposed method are compared with the existing paging methods and presented with the result that the UE location is identified using the recently visited gNodeB list in the tracking area of the AMF.