## 2 Related Works and Research Problem  
Network slicing in the 5G core radio access network is the subject of a large body of literature. 5G C-RAN slicing faces numerous issues due to the limited quantity of radio resources in C-RAN and the ever-increasing traffic from a wide range of vertical businesses. Slicing a network has a number of issues, not the least of which is determining how best to allocate resources so that individual slices can maximize resource usage and ensure they receive the quality of service they demand. Many academics have recently focused on the topic of resource distribution for network slices in 5G C-RAN. The 3 GPP in particular has been the focus of a variety of studies in this area. Resources can be allocated in two distinct ways: statically or dynamically. Without taking into account fluctuations in slice traffic, static allocation assigns the same amount of resources to each slice at all times. With dynamic allocation, resources are distributed among slices on the fly, based on the volume of traffic in each slice [13]. However, it is more difficult to make real-time changes to the allocation when using this method. In [14], the authors explored how eMBB and URLLC network slices' resource distribution in 5G C-RAN is adjusted to meet the needs of the slices. To meet the stringent requirements of the URLLC slice, they devised an algorithm employing a mixed-integer nonlinear program to allocate the resources and isolate the slice traffic. In compared to previous allocation methods that are solely concerned with power transmission, simulation results revealed that this one might deliver a high throughput percentage. However, the algorithm in that study did not take slice admission control into account, and it focused solely on two network slices without addressing the mMTC network slice. By taking into account both admission control and resource allocation in the RAN, the authors of [15] studied resource management for numerous network slices. The purpose of this research was to implement regulations for controlling who can access the slices. As an added bonus, their proposed approach would improve bandwidth consumption while also resolving the bandwidth distribution for the slices, assuring the necessary QoS. The numerical findings for throughput and bandwidth utilization [16] demonstrated that the suggested admission control strategies are superior to other approaches. However, [14; 15] relied on static resource allocation algorithms that are inflexible in the face of fluctuating data traffic. Reduced resource utilization and inability to meet QoS requirements for network slices are both consequences of static resource allocation. To address this problem, the team behind [17] developed a dynamic resource allocation strategy based on deep reinforcement learning to distribute the RAN's shared resources across its several slices. To assign radio resources to a slice without knowing the slice's traffic demands beforehand, they propose a new technique called generative adversarial network-powered deep distributional Q network (GAN-DDQN). As can be seen from the simulation results, the algorithm outperformed the traditional approaches. Under the URRLC scenario, however, which mandates low-latency communication, the proposed technique does poorly. To increase transmission speed while still meeting the QoS criteria of the network slices, the authors of [18] looked into the various latency limitations imposed by the slices. A dynamic resource allocation mechanism was implemented in the RAN with the help of the constrained Markov decision process (CMDP). Based on the simulation results, it is clear that the suggested algorithm satisfies the delay requirement for the eMBB, mMTC, and URLLC use cases. In order to better allocate radio resources for network slices in a software-defined network-based radio access network (SDN-RAN), a dynamic user count aware (DUCA) system was developed in a recent study [19].