# 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. k-nearest neighbor; Markov process; paging +
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entity (MME) owing to the paging process. The results of improvements such as load reduction and delay reduction, which were predicted in a related study, have not been proven through application to actual commercial 5G systems [4]. Hence, the improvement in 5G networks was not confirmed. Herein, the effect of applying machine learning to the AMF paging process in a 5G core network is discussed. Paging is the process of determining the location of the UE and is used to inform the UE of subscribers regarding the arrival of the voice or data to be delivered to the UE in the connection management (CM)-idle state. In the 5G AMF-initiated paging procedure, when the AMF receives the N1N2 message transfer signal from the session management function (SMF), it transmits the next-generation application protocol (NGAP) paging signal to the gNodeB in the tracking area (TA). Subsequently, when the AMF does not receive a response from the gNodeB to the completion of the connection of the radio resource with the UE within a certain duration, the AMF retransmits the NGAP paging signal to the gNodeBs in all the TAs. The maximum number of paging attempts was set in the paging profile configuration of the AMF, and the proportion of these paging signals constituted approximately 30% of the total signals processed in the AMF, which was an important target of performance improvement for the AMF and imposed a significant burden on the load as well as the linked gNodeB simultaneously. These were applied to the AMF based on the results of analyzing the signal statistics of the MME collected from several LTE commercial systems; the paging signal portion was in the range of 20% to 40%. In more detail, averages of other signals consisted of paging, 25%, UE-triggered service, 38%, MME-triggered service, 25%, and attach or tracking area update, 28%, respectively. Therefore, in a 5G wireless communication environment that requires hyper-connectivity with the UE, the paging process must be improved by reducing the paging signal load of the AMF and gNodeB. We believe that the proposed method should provide the enhanced paging performance and lead to successful 5G system commercialization and establishment.