# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 4 Proposed Model
### The Framework of the Proposed Model  
This model includes four input nodes: users, packet loss rate (PLR), delay, and spectral efficiency, along with one output node (i.e., fairness factor). Moreover, selecting samples for the training and testing sets is critical to ensuring the ANN model's accuracy and generalization capability. A common approach is to randomly split the data into a training set and a testing set, with a typical ratio of 80% for the training set and 20% for the testing set. However, other ratios can also be used based on the specific problem's requirements in techniques such as cross-validation to ensure that the ANN model's performance is consistent across different datasets. In order to ensure that the ANN model's results adequately reflect its ability to work with collected data, it is essential to use appropriate training and testing techniques. Overfitting occurs when the ANN model is too complex and fits the  
Figure 1: Framework for the ANN Topology Concept.  
training data too closely, leading to poor generalizations on new data. Techniques such as early stopping, regularization, or dropout can be used to avoid overfitting.  
Furthermore, the performance of the ANN model in this paper is evaluated on a separate validation dataset to ensure that it can generalize well to new data. The number of hidden nodes in the ANN model determines its complexity, which can significantly impact its performance. A model with too few hidden nodes may not capture the underlying patterns in the data, while a model with too many hidden nodes may overfit the training data. Therefore, it is essential to find the optimal number of hidden nodes that balances the model's complexity and its ability to capture the underlying patterns in the data, as shown in Figure 1.  
The mean square error (MSE) determines the ANN model's training performance. It helps to decrease the number of mathematical operations and the memory needed for any computer task. Furthermore, it decreases the MSE value of the training data. MSE states that the neurons in the hidden layer are similar to the number of rounds required during the training process while executing the ANN model [25], as the MSE described in Equation (5).  
\[MSE=\frac{1}{2}\sum_{i=1}^{R}(t(n)-a(n))^{2} \tag{5}\]  
where \((a(n))\) refers to the actual output generated by a network, \((t(n))\) indicates the expected output, and R refers to the number of rounds. This study presented a scheduling algorithm dataset with 50 data samples. The proposed ANN model is described above. Figure 2 indicates the framework steps for the proposed normalizing data based on the ANN model.  
The method used for normalizing input data for the ANN model is designed, which is how the data that goes into it is normalized. However, the methods for normalizing data include the following:  
Min-max scaling: This method scales the data to a fixed range, typically between 0 and 1. The formula used for this scaling is Equation (6).  
\[x\_scaled=(x-min(x))/(max(x)-min(x)) \tag{6}\]  
Standardization: This method scales the data to have zero mean and unit variance. The formula used for this scaling is Equation (7).  
\[x\_scaled=(x-mean(x))/std(x) \tag{7}\]  
Max normalization: This method scales the data such that the maximum value is 1. The formula used for this scaling is Equation (8).  
\[x\_scaled=x/max(x) \tag{8}\]  
The choice of normalization method depends on the data distribution and the specific problem. In some cases, it may be beneficial to use a combination of these methods, such as applying standardization after using min-max scaling. Normalizing the input data for ANN models is crucial to ensure that the features are on the same scale, which helps prevent the model from giving undue importance to features with higher values. Additionally, normalization can speed up the training process by reducing the oscillations in the loss function during training.