# Machine Learning-Based Paging Enhancement in 5G Network
## 4 Experiment and Results
### Experiment Results and Evaluation  
The paging profile configuration of the AMF used in the experimental environment was as follows: The typical paging approach was implemented via only one attempt from the first paging range with the last visited gNodeB to the fourth paging range with TA list paging. Meanwhile, the proposed machine learning-based probabilistic paging did not attempt the first paging with the last visited gNodeB, and the second paging based on the probabilistic paging method applied with machine learning was attempted three times. The confidence level of the paging profile configuration was set to 0.7, 0.8, and 0.9, and the weight ratio of UE groups 1 and 2 of the KNN-supervised learning-based UE classifier model was set to 50% and 100%, respectively, after which the statistics regarding the paging signals were obtained from the AMF. Subsequently, the experimental results were compared with each other. For the performance evaluation, three cases were performed: typical paging, probabilistic paging applied with the Markov process, and probabilistic paging with the KNN-supervised learning-based UE classifier model and Markov process.  
Figure 12 shows the change in the overall paging signal. As shown in Figure 12, when probabilistic paging was conducted, the total number of paging signals in the AMF decreased by 60% as compared with the typical paging shown in Figure 3, because the remaining paging steps of the paging profile configuration were not performed after the success of the paging. When the proposed probabilistic paging with machine learning was applied, the total number of AMF paging signals reduced by 70% compared with that of the typical paging.  
In terms of the average number of signals per paging shown in Figure 13, the probabilistic paging method decreased it by 83%, whereas the probabilistic paging method with machine learning decreased it by 86%.  
Based on the results of two previous experiments, we observed that the UE classifier model classified the UE into groups based on the subscriber type and assigned a weight ratio to select the best gNodeB. This reduced the overall paging signal and contributed positively to the AMF performance. In this experiment, all paging attempts were performed up to the entire TA list of the last step; therefore, the final paging success rate was the same for both the existing gNodeB list paging method and the proposed machine learning-based probabilistic paging method.  
Figure 11: Experiment scenario for proposed paging system.  
Figure 14 shows the number of paging signals per second over a week. As shown, the number of paging signals per second reduced by an average of 40-80% per day. In addition, the probabilistic paging method applied with machine learning showed that the number of paging signals per second reduced to 25% compared to the typical paging approach, owing to the probabilistic paging method involving a Markov process.  
Figure 14: Paging signals observed daily for different conditions of paging system.  
Figure 12: Total paging signals observed for different conditions of paging system.  
Figure 13: Average paging signals observed for different conditions of paging system.