## 2 Related Works and Research Problem
To dynamically revise the resource portion for each slice, the system calculates the total number of active users and the number of new user requests. Results from a simulation shown that DUCA helpsserve more people across the eMBB, URLLC, and mMTC network slices while keeping them separate. The authors of [20] implemented a dynamic resource allocation approach in the RAN to satisfy the joint QoS requirements of URLLC and eMBB slices. Lyapunov optimization is used in the proposed method to schedule the service of the two network slices and allocate the RAN resources. The numerical results for URLLC and eMBB showed that the suggested method successfully achieved reduced latency and high data quality. In [21], the authors proposed using game theory to create a strategy that dynamically allocates radio resources to each slice in the RAN. Critical Internet of Things (cloT), Enhanced Mobile Broadband Premium (eMBB Pr. ), and Enhanced Mobile Broadband were modeled using a 3 GPP system-level simulator. The researchers then compared the results to those of a fixed allocation of resources. As demonstrated by the results obtained, the proposed method met the Quality of Service (QoS) standards despite the varied nature of the data traffic encountered throughout all three test cases. The research team behind [22] looked at the use of several 5G gNobeB base stations to serve multiple network slices, with the goal of discovering the optimal resource sharing algorithm for RAN slicing. To maximize efficiency, the system allotted radio bandwidth to network slices from each gNodeB base station. In addition, [23] proposed a dynamic resource allocation mechanism for RAN slicing, which was the subject of research into slice isolation. If you do not want one slice's data traffic to affect the other slices, slice isolation is a must. The results of the study demonstrated that optimal slice isolation leads to greater resource utilization and a larger number of consumers being serviced. Admittance control in C-RAN slicing is a topic that has been investigated in a number of recent papers. C-RAN has limited resources and hence cannot serve all users at once. In order to ensure that each slice meets its minimum QoS criteria, an admission process must be used to accept just the necessary users. To reduce the BP of eMBB and URLLC slices in the 5G C-RAN, the authors of [24] propose an admission control strategy. In the event of a C-RAN resource shortage, BP indicates the proportion of users who will be affected. Traditionally, efforts to admission control have placed greater emphasis on lowering the BP of the URLLC slice than the BP of the eMBB slice. According to the findings of that investigation, the proposed method decreases BP in eMBB while keeping BP in URLLC at a lower level [24]. In addition, [25] laid out a plan for how to develop an effective admission control mechanism for the 5G C-RAN, which boosts revenue for network operators. The authors employed mixed-integer nonlinear programming and eMBB and URLLC use case simulations to prioritize accepting customers who would bring in the most money for network owners. It was demonstrated that using the proposed framework boosted operator profit and reduced power usage. However, in improving resource usage, the techniques reported in [24; 25] only considered admission control on two slices of the RAN slicing. Last but not least, in [26], the authors explore the burgeoning demand for the few RAN resources and suggest a system for admission control and resource allocation for network slicing in the RAN. This system determines which users are eligible based on a minimal Quality of Service (QoS) that can be met in each slice, and then provides those users with the necessary resources. Both the number of allowed users and the efficiency with which resources were used increased throughout the course of the experiments.