# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 1 Introduction  
Modern mobile broadband services are based on the development of the Long-Term Evolution (LTE) and enhanced cellular system. The merits provided by the LTE are low cost per bit, high system capacity, high data rate, and high spectrum efficiency [1, 2]. Heterogeneous wireless networks (LTE) are used to satisfy the needs of cellular networks [3, 4]. The requirement of broadband mobile traffic is met using the small and macro cells of the heterogeneous networks [5, 6]. Device-to-device (D2D) communication is also termed as LTE direct transmission [7]. These D2D communication systems have the capacity to improve the usage of spectral resources and minimize energy consumption. D2D supports peer-to-peer and location-based services and applications [8].  
In 3 rd generation partnership project LTE, one node is equipped between User Equipment (UE) and enhanced Node B (eNB). The radio network controller in the eNB has the ability to perform traffic balancing, mobility and radio resource management (RRM) [9]. Moreover, the eNB and UE are used to tune the system parameters for achieving an adequate performance, which is defined as self-optimization in LTE [10]. The lesser capability in the cell edge is assumed as the constraint in LTE-Advanced (LTE-A) systems. The interference occurring from the users affects the capacity of the LTE, which causes aggressive frequency. A LTE's random access network is affected because of the complete isolation between different parallel services and signaling overhead [11; 12]. The developing characteristics and trends of the 5G LTE-A are cloud radio access network, improved indoor coverage, Human-to-Human and machine-to-machine communications, less delay, less energy consumption, and massive multiple input and multiple output [13].  
The development of 5 th generation mobile communication networks (5G) satisfies the expected requirements of modern communication, such as huge amounts of connected devices with varying service requirements, high traffic volume, and improved quality of user experience [14]. Therefore, intelligent learning approaches are being developed to improve the performance of the 5G network. Deep learning (DL) is used as a model-free and data-driven approach for minimizing the difficulty with available training inputs and outputs. The issues related to the resource allocation is avoided based on the offline training of simulated data and delivering the results using the well-trained networks during online processes [15]. The interdependency of OSI stack layers is considered to be a key issue in the RRM. An appropriate cross-layer optimization approach is required to obtain an optimal performance for the stack layer. Moreover, access methods with numerous interfaces requires the organization of numerous RRM elements in the same equipment [16]. Examples of the conventional RRM methods are specified as follows: hybrid approach [17], Carrier aggregation-based RRM [18], and pathloss-threshold-based component carrier and cluster configuration algorithm [19]. The main contributions of this paper are as follows:  
* An optimal bandwidth and power is assigned to the UEs using the LSTM in the 5G network, whereas the GSO is used to discover adequate hyperparameters.
* An allocation of resources to the unwanted UE is avoided by analyzing the request queue of all the UEs. Additionally, the LSTM-based RRM also reduces the traffic in the network.
* Additionally, a guard level insertion in the data is used to reduce the ISI in the network. The ISI existing in the data creates a high amount of errors during data transmission.  
The complete arrangement of the paper is given as follows: Section 2 corresponds to a literature survey covering the existing papers on RRM. Section 3 describes the system model and LSTM-based RRM processed in the 5G environment. The experimental and comparative analysis of the LSTM-based RRM in the 5G environment is given in Section 4. Section 5 provides the conclusions.