# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 2 Literature Survey  
[PERSON] et al. [20] developed the Distributed RRM (DRRM) for 5G multi-RAT multiconnectivity networks. The wide range of 5G is supported by enabling the tight interworking among the LTE and 5G. The minimum UE throughput is increased by using the optimization problem. This work only achieved less computation effort and less signaling overhead. A higher transmission rate was not achieved when the BS was only considered as the reference signal received power. [PERSON], and [PERSON] [21] presented the LTE network over the QoS along with the RRM. The QoS with optimal confederation-aware technology, namely QOC-RRM, was used to allocate the optimum resources for the users in the LTE network. Next, the operators in the LTE network were prioritized by using the recurrent deep neural network. Further, the queuing criterion information was shared by the chaotic weed optimization algorithm to accomplish the routing process that was used for performing data transmission. Then, the priority of the users for the available resources was scheduled based on the priority value. However, this RRM was analyzed only on fourth generation systems. [PERSON] and [PERSON] [22] developed the RRM to enhance the downlink performance of soft-frequency reuse-based LTE. The spectral efficiency of the system is improved by dynamically allocating the RRM in the manner of being distributed and centralized at the network. After performing the allocation, the confederation concept is developed in the network. The confederation style network is combined with a routing algorithm, and this confederation concept is used to minimize the overhead. [PERSON] et al. [23] developed the predictive RRM (PRRM) scheme for next-generation wireless networks (NGWNs). Additionally, the issues related to mobility management and resource control are addressed while satisfying Quality of Service (QoS) requirements. Therefore, the IEEE 802.21 Media Independent Handover (MIH) protocol is used to optimize the operation among the heterogeneous networks. This MIH protocol delivers the coordination among the two previous network characteristics. Moreover, the PRRM is combined with the handover process, which comprises three phases: resource allocation estimation, radio resource allocation decision, and allocation notification. [PERSON] et al. [24] presented the Deep Reinforcement Learning (DRL) to perform the RRM in the Integrated Access and Backhaul (IAB) networks. The developed DRL-based RRM is used to mitigate the congestion at the access side of the IAB network. The transmission buffer is initialized to observe the IAB node's congestion rate, which helps to provide the enough resources to the users. Moreover, the dynamic power management and Markov decision process are used to transform the constrained issue due to the problem of power consumption. However, the overall complexity of this developed DRL-based RRM is similar to the existing approach. [PERSON] [25] developed the semicentralized resource allocation approach for IAB networks. The developed semicentralized resource allocation is flexible and it has less complexity. Therefore, this flexible semicentralized resource allocation is used to adopt this approach for various needs, cases, and traffic classes of 5G networks. Here, an effective resource allocation is achieved by using generic optimization. However, the developed semicentralized resource allocation is only dependent on the data transmitted and reported in the 3 GPP deployment. [PERSON] et al. [26] implemented the Reinforcement Learning (RL) for optimizing the dynamic power and performing channel allocation. Here, the Channel State Information (CSI) is combined with the RL for adaptively minimizing the interferences. The interference during the communication is increased according to the number of users. [PERSON] et al. [27] developed the power control approach using the Deep Q-Network (DQN) to enhance the system-level Energy Efficiency (EE) of 5G networks [28].