# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 2 Literature Review
A comparison of the performance of the different techniques indicated that all ANN models could predict the transmitted LTE radio signals with numerous errors. A recent study by [PERSON] [11] aimed to resolve these issues related to the existing models (experimental and deterministic) by implementing ML-based algorithms to predict path loss in LTE networks. They developed the RBFNN and multilayer perception neural network (MLPNN) models with the measured data as an input variable and compared it to the measured path loss. They noted that the RBFNN was more accurate, as it showed lower root mean squared errors (RMSEs) than the MLPNN. Also, an ANN-aided scheduling scheme for the UEs with mobility in LTE dynamic HetNets was proposed in another study [12]. They determined a faster eICIC reconfiguration technique in the LTE HetNets. This technique helped to achieve a marginal gap compared to the centralized solution. Using historical data, this proposed technique could train the RBFNN to determine the relationship between the surrounding environment (channel and UE deployment), an optimal cell range extension, and a nearly blank subframe pattern. The researchers investigated the performance of their proposed algorithm concerning its throughput and utility during simulations. They noted that an optimal resource assignment helped rapidly vary the HetNets with low-performance degradation. In [13], the researchers studied the probabilistic GRNN model for modelling and estimating the data corresponding to the spatial signal power loss. Commercial data was collected from the LTE network interface's outdoor location. This examined GRNN model was trained with a power loss measurement of a spatial signal. The data was collected from three different outdoor signal propagation locations. It is noted that the proposed model showed better results in comparison to the conventional least square regression modelling process. Some researchers proposed an ANN-based classification system with higher accuracy and performance [14] for fingerprint images using the NN models. In [15], the researchers used this technique for classifying bacteria. The results indicated that the ANN was effective and feasible. In [16], the researchers classified aerial photographs using the ANN. They noted that the ANN was suitable for classifying the remotely sensed data, exceeded the maximal probability classification for the classification accuracy, and showed a positive effect. In [17], the researchers used the ANN for classifying spoken letters. They noted a 100% (training) and 93% (testing) classification accuracy. All these studies stated that the ANN could be used for classification owing to its better performance. To improve machine-type communication (MTC) security, some researchers [18] studied the issues related to the lack of authentication requirements and attack detection for LTE-based MTC devices. As a result, they introduced a better NN model for detecting attacks. The results indicated the efficiency of this model in detecting attacks and compared the system's security. ANN models could be used for optimizing the resource allocation algorithms for communication networks. Furthermore, ANN technology could overcome the resource allocation problems noted in the LTE network. Some common ANN activation functions that were applied included the sigmoidal, binary, and hyperbolic sigmoidal functions based on the RBFNN, MLPNN, recurrent neural network, and perceptron models. All these functions were used for developing network communication. The researchers also considered the backpropagation and the gradient descent algorithms as training algorithms for the ANN. ANNs are used for different tasks, like approximation and prediction of functions, pattern classification, prediction, and clustering. However, the performance of the algorithm was significantly affected by data preparation and the setup used for the NN structure. ANN and mathematical models are used to evaluate and validate experimental data [19; 20].