# Machine Learning-Based Paging Enhancement in 5G Network
## 3 Proposed Machine Learning-Based Paging
### Proposed Paging
#### 3.3.2 Proposed Process
The AMF analyzed the movement data of UE in the last visited gNodeB0 and measured the transition probabilities of four adjacent gNodeBs (gNodeB1, gNodeB2, gNodeB3, and gNodeB4) from gNodeB0, as shown in Figure 8, and the transition probabilities were sorted in the descending order [20]. We defined there were five states of gNodeB0, gNodeB1, gNodeB2, gNodeB3, and gNodeB4. The AMF selected the gNodeB to perform paging in the order from the gNodeB with the highest to lowest transition probability. The Markov process was implemented as follows: The state transition probabilities for each gNodeB were based on the UE movement from gNodeB0. In this example, the total number of UE that moved from gNodeB0 to the neighboring gNodeB is 2000. If the number of UE that moved to gNodeB2 was 1400 with 70% transition probability, gNodeB1 was 300 with 15% transition probability, NodeB4 was 200 with 10% transition probability, and gNodeB3 was 100 with 5% transition probability [21; 22]. Therefore, we set the transition probability as \(p00=0\), \(p01=0.15\), \(p02=0.7\), \(p03=0.05\), and \(p04=0.1\). If No. 4 of the paging profile configuration in Table 4 was selected, the number of paging attempts was set to three, and the confidence level for the node criterion was set to three probability values: 0.7, 0.8, and 0.9. Subsequently, the AMF performed three paging attempts in the following second machine learning-based probabilistic paging. In step one of paging, the AMF first selected gNodeB2. In this regard, the transition probability of gNodeB2 was 0.7, which was equal to or greater than the first probability value (0.7) at the confidence level. Next, when the target UE was classified into UE group 2 by the UE classifier model, gNodeB was assigned the corresponding weight ratio (100%), as shown in Table 5. Therefore, the AMF attempted to page gNodeB2 and gNodeB0. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paging Profile**} & \multirow{2}{*}{**Last Visited**} & \multicolumn{2}{c}{**Latest Visited or**} & \multirow{2}{*}{**Last Visited TA**} & \multirow{2}{*}{**TA List**} & \multirow{2}{*}{**Confidence Level**} \\  & & & & & \\  & **gNodeB** & & & & \\ \hline
1 & 0 & 0 & 0 & 4 & \\
2 & 0 & 0 & 2 & 3 & \\
4 & 0 & 3 & 2 & 1 & 0.7, 0.8, 0.9 \\... &... &... &... &... &... \\
20 & 3 & 2 & 2 & 2 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of the proposed AMF paging profile configuration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**UE Group** & **Weight** & **Remark** \\ \hline
1 & 50\% & Resident, office worker \\
2 & 100\% & Traveler, non-office worker \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Example of weight table of UE group. In step two of paging, if step one of paging failed, the AMF selected gNodeB2 and gNodeB1, where the transition probabilities of the two gNodeBs were 0.85, which was equal to or greater than the second probability value (0.8) in the confidence level. Next, if the target UE was classified into UE group 1 by the UE classifier model, the gNodeB was assigned a corresponding weight ratio (50%). Therefore, the AMF finally paged gNodeB0 along with gNodeB2; however, by applying a weight ratio of 50% to the gNodeBs, gNodeB1 was excluded from the paging attempts. In step three of paging, if step two of paging failed, then the AMF selected gNodeB2, gNodeB1, and gNodeB4, where the sum of the transition probabilities of the four gNodeBs was 0.95, which was equal to or greater than the third probability value (0.9) at the confidence level. Next, if the target UE was classified as UE group 1 by the UE classifier model, the AMF selected gNodeBs with a weight ratio of 50%. However, the AMF attempted to page gNodeB2, gNodeB1, and gNodeB4 together with gNodeB0. Because the AMF had a history of paging failure to which a weight ratio was previously applied, the weight ratio (100%) was applied instead of the weight ratio (50%) of UE group 1 in the UE classifier model.