# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 5 Experimental Results
### Normalized Maximum Largest Weighted Delay First (MLWDF) Algorithm  
* Approximate RBFNN Mode-MLWDF Algorithm  
Figure 7 presents the results of comparing the normalized maximum largest weighted delay first (MLWDF) algorithm by applying the approximate radial basis function neural network (RBFNN) model for downlink scheduling in an LTE network. The performance of the two algorithms was evaluated in terms of their accuracy in allocating radio resources to different users. According to the results presented in Figure 7, the approximate RBFNN model was more accurate than the MLWDF scheduling algorithm, with an accuracy improvement of 0.021%. This means that the approximate RBFNN model was better at allocating radio resources to different users more accurately and efficiently compared to the MLWDF algorithm. However, it is important to note that the accuracy improvement  
Figure 6: Comparison of the performance of the PF algorithm and GRNN model.  
of the RBFNN model was lower than the raw data presented by the MLWDF algorithm. The results also showed that the performance of the algorithms was evaluated for different numbers of users, with the highest value of the users indicated as 6, 18, and 30 compared to the raw data. Overall, the results suggest that the RBFNN model is a more effective and accurate algorithm for downlink scheduling in an LTE network compared to the MLWDF algorithm.  
* Exact RBFNN Mode-MLWDF Algorithm  
Figure 8 presents the results of comparing the normalized maximum largest weighted delay first (MLWDF) algorithm with an exact radial basis function neural network (RBFNN) model that was applied to the MLWDF for downlink scheduling in an LTE network. The performance of the two algorithms was evaluated in terms of their accuracy in allocating radio resources to different users. According to the results presented in Figure 8, the exact RBFNN model was more accurate than the MLWDF scheduling algorithm, with an average accuracy improvement of 0.021%. This means that the exact RBFNN model was better at allocating radio resources to different users more accurately and efficiently than the MLWDF algorithm. Additionally, the results indicate that the exact RBFNN model showed a symmetrical result compared to the raw data for all numbers of users tested. The results also showed that the performance of the algorithms was evaluated for different numbers of users tested in the study. Overall, the results suggest that the exact RBFNN model may be a more effective and accurate algorithm for downlink scheduling in an LTE network than the MLWDF algorithm.  
* GRNN Model-MLWDF Algorithm  
Figure 9 presents the results of comparing the normalized maximum largest weighted delay first (MLWDF) algorithm with a hybrid algorithm that uses both a generalized regression neural network (GRNN) and an exact radial basis function neural network (RBFNN) model for downlink scheduling in an LTE network. The performance of the two algorithms was evaluated in terms of their accuracy in allocating radio resources to different users.  
Figure 7: Comparison of performing the MLWDF algorithm and approximate RBFNN Model.  
According to the results presented in Figure 9, the hybrid GRNN-RBFNN model was much more accurate than the MLWDF scheduling algorithm, with an average improvement in accuracy of 0.030%. This means that the hybrid GRNN-RBFNN model was better at allocating radio resources to different users more accurately and efficiently compared to the other models.  
Figure 8: Comparison of performing the MLWDF algorithm and exact RBFNN model.  
Figure 9: Comparison of performing the MLWDF algorithm and GRNN model.  
the MLWDF algorithm. Additionally, the results indicate that the highest number of users tested was 30, with a symmetrical result for the raw data for all users tested except for 9, 12, 15, 24, and 30. Figure 9 may be specific to the particular network scenario and conditions used in the study and can be directly applicable to other network environments.