# Machine Learning-Based Paging Enhancement in 5G Network
## 3 Proposed Machine Learning-Based Paging
### Proposed Paging
#### 3.3.2 Proposed Process  
The AMF selected the final gNodeBs from the probabilistic gNodeB list for paging based on a combination of three variables: the weight ratio of the UE group in the UE classifier model result, the transition probability of the gNodeB, and the confidence level in the paging profile configuration. During paging, the weight assigned to each UE group was applied to the UE when probabilistic gNodeBs were selected. In this paper, the AMF classified the UE into two UE groups, as shown in Table 3, and acquired the UE profile information of these UE groups. To predict the UE movement to the gNodeBs, the AMF periodically acquired a significant amount of UE movement data between gNodeBs and the UE profile information from the registration step processed by UE mobility events. For example, the periodic or  
Figure 6: Algorithm of proposed machine learning-based paging. Figure 7: Proposed AMF paging range profile. mobility registration update, service request, and handover procedures in 5G contained the latest UE movement data. After obtaining the UE movement data between the gNodeBs, the AMF periodically combined with the newly acquired UE movement data. The training data were obtained by multiplying the decay factor for past UE movement data at the current point and calculating the transition probability of moving from one gNodeB to all of its adjacent gNodeBs. The confidence level of the paging profile configuration, a set of configurable probability values, was a threshold terminating the process of selecting gNodeBs for the paging process. The confidence level included one or more probability values that provided backup confidence level values for retries, even if the first paging attempt failed. Therefore, the number of probability values of the confidence level was equal to the number of paging attempts configured for each paging step in the AMF paging profile configuration. The confidence level could be set in units of the entire AMF or the paging profile configuration. When both were set, the confidence level of the paging profile configuration was prioritized. The number of gNodeBs used in this paper was 1300; the number of possible states in the transition matrix. The transition matrix of all gNodeBs was as follows:  
\[\mathit{Pij}^{\prime}(1300\mathit{gNodeBs})=\left[ \begin{array}{cccccccc}&gNodeB 0&gNodeB1&\dots&\dots&\dots&gNodeB1299\\ gNodeB0&p0\_0&p1\_0&\dots&\dots&\dots&p0\_1299\\ gNodeB1&p1\_0&p1\_1&\dots&\dots&\dots&p1\_1299\\ \dots&\dots&\dots&\dots&\dots&\dots&\dots&\dots&\dots\\ \dots&\dots&\dots&\dots&\dots&\dots&\dots&\dots&\dots\\ gNodeB1299&p1299\_0&p1299\_1&\dots&\dots&\dots&p1299\_1299\end{array} \right]\]  
where \(\mathit{Pij}\) was the transition probability from state \(i\) to state \(j\). At the evaluation of the proposed AMF paging experiment, the transition probabilities of all gNodeBs were recalculated every 20 min, and its result was used in the Markov process. Figure 8 shows five gNodeBs, and UE movement data of them are given to easily explain the proposed process without the collected UE movement data of 1300 gNodeBs used in this paper. Figure 8: Example of proposed machine learning-based paging. Figure 8 shows an example of probabilistic gNodeB paging applied with machine learning, where the AMF selects gNodeB2 with the highest transition probability based on the last visited gNodeB0, then gNodeB1 with the second highest, followed by gNodeB4 with the next highest. When the sum of the accumulated transition probabilities of the selected gNodeBs satisfied or exceeded the probability value of the specified confidence level, no further gNodeBs were selected for the paging attempts. The paging range profile showed that the AMF always paged to the last visited gNodeB0, along with the selected gNodeBs, in every paging attempt. The example shown in Figure 8 illustrates the manner by which the AMF performs paging based on the probabilistic gNodeB list applied using machine learning.