# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 5 Experimental Results
### Normalized Proportional Fair (PF) Algorithm  
* Approximate RBFNN Model-PF Algorithm  
Figure 4 presents the results of comparing the normalized proportional fair (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 precision in allocating radio resources to different users. The results presented in Figure 4 show that the RBFNN model was more precise than the PF scheduling algorithm, with an average precision of 0.086 (%). This means that the RBFNN model better allocated radio resources to different users more precisely and efficiently than the PF algorithm. The results also showed that the performance of the PF algorithm varied depending on the number and heights of the users in the network. Specifically, the raw data for the PF algorithm showed that the number of users ranged from 3 to 27, with heights of 6, 12, and 30. The results suggest that the RBFNN model may be a more effective and precise algorithm for downlink scheduling in an LTE network compared to the traditional PF algorithm. * Exact RBFNN Mode-PF Algorithm  
Figure 5 presents the results of comparing the normalized proportional fair (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. According to the results presented in Figure 5, the exact RBFNN model was more accurate than the PF scheduling algorithm, with an accuracy of 0.090%. This means that the exact RBFNN model was better at allocating radio resources to different users more accurately and efficiently compared to the PF algorithm. The results also showed that the performance of the exact RBFNN model was symmetrical for all users, which means that it performed consistently \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Parameters Values** & **Parameters** & **Parameters Values** \\ \hline Number of clusters & 1 & Bandwidth & 10 MHz \\ Number of cells & 7 & simulation duration & 46 s \\ Frame Structure & FDD & Video bit-rate & 256 kbps \\ UEs number & 30 & Flow duration & 40 s \\ Cell Radius & 1.5 Km & Maximum delay & 0.1 \\ Speed of UE & & 3 km/h & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters used for the models. well for all users in the network. Overall, the results suggest that the exact RBFNN model may be a better algorithm for downlink scheduling in an LTE network than the traditional PF algorithm. However, as with the results presented in Figure 4, it is important to note that the results presented in Figure 5 may be specific to the particular network scenario and conditions used in the study and can be directly applicable to other network environments. Figure 4: Comparison of performing the PF algorithm versus the approximate RBFNN model. Figure 5: Comparison of performing the PF algorithm and exact RBFNN model. * GRNN Model-PF Algorithm  
Figure 6 presents the results of comparing the normalized proportional fair (PF) algorithm with a hybrid algorithm that combines the general regression neural network (GRNN) and radial basis function neural network (RBFNN) models 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 6, the GRNN-RBFNN hybrid model was more accurate than the PF scheduling algorithm, with an accuracy improvement of 0.030%. This means that the GRNN-RBFNN hybrid model was better at allocating radio resources to different users more accurately and efficiently compared to the PF algorithm. The results also showed that the performance of the hybrid model was evaluated for different numbers of users, with the highest accuracy improvement observed for the numbers of users 9, 12, 15, 24, and 30.