# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 2 Literature Review  
Many researchers have used ANN models to enhance the performance of the downlink communication system in LTE networks. Different models were used, where some studies considered channel estimation, while others investigated user device condition or mobile location. Predictive analyses of various ANN models for predicting and classifying the data over the LTE network were conducted in some other studies. Several studies have reported significant improvements in various performance metrics, such as throughput, fairness, and quality of service, when using ANNs for downlink scheduling. For example, a study by [5] showed that an ANN-based downlink scheduling algorithm achieved up to 30% higher throughput than a conventional rule-based algorithm. [PERSON] [6] proposed an accurate channel environment estimation technique using the ANN and support vector machine regression (SVR) models for the standardized signal structure of the LTE downlink system [6]. This technique was used under the impulsive, non-linear noise that interfered with reference codes after considering the high mobility conditions. He studied the SVR and ANN performances using the simulation results, which performed better than the decision feedback (DF), least squares (LS), and ANN algorithms. Another study [7] presented a method for relaying the reference symbol information. This information was used for estimating the total frequency response of the channel. This technique was summarized in two steps: firstly, channel differences were adapted after applying the ANN-based learning methods trained using the genetic algorithm (ANN-GA). Secondly, the channel matrix was estimated to improve the performance of LTE networks. They validated the proposed algorithms using various ANN-based estimator algorithms, such as the feed-forward neural network, the layered recurrent neural network, the least squares (LS) algorithm, and the cascade-forward neural network for closed-loop spatial multiplexing (CLSM) single-user multi-input. The results of this comparison indicated that the proposed ANN-GA algorithm showed better accuracy than others. Furthermore, the significant increase in network subscribers led to resource allocation issues. To resolve this problem, the researcher proposed a downlink algorithm that ensured an effective and faster resource allocation solution for real-time video applications [8]. This solution used an ANN algorithm that allowed resource allocation after considering the UE conditions. They noted that the AI techniques used for resource allocation on the LTE network generated accurate results, but the ANN-based training process could take a long time. Hence, dynamic resource allocation can be done by realizing the daily ANN training processes whenever the eNodeB is intense. In terms of predicting and classifying data over the LTE network via applying ANN models, many researchers have carried this out. In an earlier study [9], the researchers investigated the performance of two ANN models for prediction and training algorithms (i.e., Levenberg-Marquardt and Bayesian regularization). They primarily focused on integrating an ANN into the LTE network during the mobile handover start-up phase. They compared the received signal strength (RSS) and the hysteresis fringe parameters for the adaptive neural hysteresis fringe reduction algorithm. The study aimed to resolve the channel estimation problem noted in LTE networks. In [10], the researchers determined the adaptive learning and predictive ability of three ANN models, i.e., RBFNN, GRNN, and MLPNN, using the spatial radio signal dataset derived from the commercial LTE cellular networks. Thus, they could verify the efficiency and accuracy of the adaptive prediction system using attenuation and oscillation landscapes to determine the radio signal strength propagated in the LTE urban micro-cell topography. Their results indicated that the ANN prediction techniques could adapt to the measurement errors regarding the attenuation of the LTE radio signals.