# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 1 Introduction  
The 3 rd Generation Partnership Project (3 GPP) implemented the (LTE) strategy for fulfilling the increasing demand for wireless networks. The radio resource management scheduling algorithms were also used in this LTE setup. They assigned radio services to the final users based on different criteria for quality of service (QoS). However, the development of the downlink scheduling algorithms was a major problem noted during resource allocation in the LTE system. Different scheduling techniques were proposed for addressing this issue, and an investigation of the downlink algorithms garnered a lot of research interest during the LTE implementation, as several researchers started shifting to packet scheduling over LTE since it was regarded as a rapidly growing technology that can significantly affect the future of wireless networks. In the LTE downlink algorithm used for QoS class identifiers, the radio resource allocation steps use the QoS specifications and channel condition reports to determine the users' transmission orders. However, inefficient resource allocation in the LTE networks can be noted due to poor network performance that deteriorates the data throughput and network fairness index and increases the average delay.  
A better communication system could be developed by integrating the different artificial intelligence (AI) concepts and machine learning (ML) techniques for scheduling the end-user devices and designing wireless structures. The use of artificial intelligencetechnologies in the wireless communication system was based on its ability to encourage the massive advancement of wireless traffic and the emergence of new uses for wireless services. The services that were not tested in the past varied between general multimedia and video-based services. The major issue in the evolution and development of wireless networks in the past decade is a greater need for wireless networks that provide better communication services with higher reliability, lower latency, and a higher end rate [1].  
This study focuses on resource allocation scheduling at the receiver end for optimizing the downlink across the LTE network. It ensures the fulfilment of the QoS about their fairness. For this purpose, the study investigated three artificial neural network (ANN) algorithms, i.e., the approximate radial basis function neural network (RBFNN), the exact RBFNN, and the generalized regression neural network (GRNN). The researchers approximated the exact Gaussian fields using the appropriate radial function (RF). ANNs perform many tasks, like approximation or function prediction, pattern classification, and clustering. The performance of the ANN during tasks is significantly affected by the data preparation setup of some of the ANN structures [2; 3]. In previous studies, the researchers investigated the performance of the ANN concerning wireless communication [4]. They noted that ANNs could be used for studying network prediction and user behavior and offering solutions to users for different problems associated with wireless networks. These problems included resource computation, spectrum management, replacing cached contents, cellular connectivity, and resource assignment.  
The remaining paper has been organized in the following manner: Section 2 presents an overview of related studies that used different ANN models for the LTE network. Section 3 presents a review of the LTE downlink scheduling techniques. Section 4 describes the architecture of the proposed ANN model that processes the LTE downlink scheduling algorithms. It then follows with a description of the simulation environment and results. The final section presents the conclusions.