# Machine Learning-Based Paging Enhancement in 5G Network
## 3 Proposed Machine Learning-Based Paging
### Proposed Paging  
#### 3.3.1 Proposed Algorithm  
In this paper, a paging method using supervised machine learning and the Markov process was proposed for the 5G AMF. Firstly, a UE classifier was implemented using KNN-supervised machine learning to classify subscribers based on the characteristics of UE movement patterns. Meanwhile, UE movement was monitored at the AMF when the UE moved to adjacent gNodeB. Secondly, statistics were used to predict the current gNodeB of the moving UE and the recent gNodeB transition probabilities obtained from the UE movement. The dataset of the UE profile information used for supervised learning was as follows:  
* First registration time: The time at which the first registration in the AMF was completed
* Last registration time: The time at which the last registration/service was performed in the AMF
* Last update type: The last UE registration type
* Last service type: The last UE service type
* gNodeB/TAC: The UE's latest gNodeB/TAC information
* Latest TA list: The UE's latest TA list information  
For UE classification, we considered various classifiers such as the KNN, random forest, decision tree, and SVM, applied with the UE profile information, shown in Table 2, registered to the AMF. However, UE profile information for the 5G AMF was hardly obtained because 5G services were not fully commercialized. Therefore, the UE profile information obtained in the 4G MME was converted to satisfy the 5G technical specifications and used in our experiment. Subscribers were classified into two UE groups, as shown in Table 3. One UE group included local residents and office workers who did not move significantly daily, and the other UE group included travelers, non-office workers, etc. We expected the overall paging performance to have improved by classifying the UE with the supervised learning and applying the weight to the confidence level used for probabilistic gNodeB selection during the paging process. The grouping of UE was to reduce paging signals with a smaller gNodeB list per group classified according to the number of UE movements. For the experiment involving four classification models, i.e., KNN, random forest, decision tree, and SVM, 1000 UE profile data of every 20 min cycle were observed for \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Item** & **Example of Value** \\ \hline
1 & First Registration Time & MM-DD hh:mm:ss \\
2 & Last Registration Time & MM-DD hh:mm:ss \\
3 & Last Update Type & Periodic, Mobility \\
4 & Last Service Type & UE-Initiated, Network-Initiated \\
5 & gNodeB ID & 6217 \\
6 & TAC & 15410 \\  & & 450-09-15410 \\
7 & Latest TA list & 450-09-15391 \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UE profiled dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**UE Group** & **Movement Type** & **Remark** \\ \hline
1 & UE with low mobility & Resident, office worker \\
2 & UE with high mobility & Traveler, non-office worker \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UE group category. five days, and four days data were applied to supervised learning. Then, 1000 UE profile data for the last one day were used for the performance evaluation of the four models [18]. Performance evaluation of the classification models was conducted using scikit-learn, a machine learning library of the Python programming language [19]. Figure 5 shows the test results for the classification accuracy. In this classification evaluation, we set a K of 20 in the KNN model, and entropy gain and estimators of 100 in the random forest model. In addition, we used the entropy criterion in the decision tree model and the linear kernel model with regularization of 10, and gamma of 0.1 in the SVM model. We observed that both the KNN and SVM models, which were candidates for our UE classification model, provided satisfactory classification accuracy. In addition, the simplicity and convenience of implementing the supervised learning classifier model in the AMF were considered for the classification model selection.