# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 5 Experimental Results
### Normalized Exponential/Proportional Fairness (EXP-PF) Algorithm  
* Approximate RBFNN Model-EXP-PF Algorithm  
Figure 10 presents the results of comparing the normalized exponential/proportional fairness (EXP-PF) algorithm with an 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 10, the approximate RBFNN model was more accurate than the EXP-PF scheduling algorithm, with an improvement in accuracy of 0.021%. However, the approximate RBFNN model was less accurate than the raw data. The users tested included 9, 12, 21, 18, 27, and 30, with the highest value of users indicated as 3, 6, 15, 18, and 24 compared to the raw data. Figure 10 may be specific to the particular network scenario and conditions used in the study and can be directly applicable to other network environments.  
* Exact RBFNN Model-EXP-PF Algorithm  
Figure 11 presents the results of comparing the normalized exponential/proportional fairness (EXP-PF) algorithm with 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 10: Comparison of performing the EXP-PF model and approximate RBFNN.  
According to the results presented in Figure 11, the exact RBFNN model was more accurate than the EXP-PF scheduling algorithm, with an improvement in accuracy of 0.023%. All users tested, including 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30, presented symmetrical results compared to the raw data. It is important to note that the results presented in Figure 11 may be specific to the particular network scenario and conditions used in the study and can be directly applicable to other network environments.  
* GRNN Model-EXP-PF Algorithm  
Figure 12 presents the results of comparing the normalized exponential/proportional fairness (EXP-PF) algorithm with a hybrid model combining a generalized regression neural network (GRNN) and a radial basis function neural network (RBFNN) 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 12, applying the proposed hybrid GRNN-RBFNN model for EXP-PF scheduling was more accurate than the EXP-PF scheduling algorithm, with an improvement in accuracy of 0.030%. The number of users tested was 3, 6, 18, 21, and 27, and the highest value of the users was indicated as 9, 12, 15, 24, and 30 compared to the raw data. It is important to note that the results presented in Figure 12 may be specific to the particular network scenario and conditions used in the study and can be directly applicable to other network environments. Further evaluation and validation of the hybrid GRNN-RBFNN model in a variety of network scenarios and conditions may be necessary to fully assess its effectiveness as a downlink scheduling algorithm in LTE networks.  
Figure 11: Comparison of performing the EXP-PF model and exact RBFNN.