## 2 Related Work
### End-to-End Network Slicing
This paper [27] presents a management and orchestration framework for the slicing of next-generation RAN. It defines the important interfaces and information models for the provisioning of RAN slices dynamically. Additionally, the authors have discussed the feasibility and requirements for the automation and orchestration of RAN slices. Moreover, a self-planning mechanism is illustrated which can handle all issues related to the management of the RAN network slicing domain [1]. This paper [28] investigated the issues and problems in the RAN slicing domain. The main challenge is how to allocate and share the spectrum for different kinds of slices to meet diverse user requirements. The other key issue is how to provide openness for third parties to use the same spectrum such as support for multi-tenant. The multi-tenant support enables third parties to use and share the infrastructure of different network operators, instead of deploying new infrastructure. In this system [29], the author has proposed an application-specific RAN slicing framework in which the radio spectrum can be assigned concerning application-specific radio resource blocks (RRBs). Additionally, an application-specific scheduling policy for the distribution of the RAN spectrum was introduced. Hence, the comparison between the conventional spectrum allocation mechanism and application-specific RRB distribution and scheduling was carried out, and the results show the efficiency and feasibility of the proposed system. In the end, an intelligent network architecture based on application-specific RAN slicing and machine learning was introduced. In this paper [28], a RAN slicing system is presented with two services supports, such as eMBB and V2X. Additionally, this paper explains the issue and challenges that occur during the implementation of RAN slicing. A less complex heuristic-based algorithm has been developed with the RL (reinforcement learning) approach, which can allocate the optimal resources for each slice. It ensures the availability of RAN resources for each slice to meet the traffic requirements and maximizes the utilization of RAN resources. A simulation setup has been deployed to test the performance of the system. These results show that the proposed system can improve the performance of the network concerning latency, data rate, and resource utilization. In this paper [30], the author proposed a novel RAN slicing framework with haptic communications for 5G networks named Hap-SliceR. Firstly, an RL-based RAN resource slicing strategy has been implemented which can optimally allocate the radio resources to different slices according to the specified requirements. Secondly, this sliceR is capable of customizing the RAN resources for haptic transmission and communications. For the allocation of RAN resources, a heuristic-based algorithm has been designed and developed. The performance of the Hap-SliceR can be evaluated by comparing it with a well-known 5G air-interface design. STORNS is an analytical framework for RAN slicing which works based on stochastic geometric theory [31]. They have developed a mathematical model to prove the benefits of slices RAN comparedto non-sliced RAN. This system can decide according to the provided service level agreement (SLA) and ensure average throughput per slice. Whenever a slice request arrives, it acts as an admission control block and allocates the resources for the admitted slice. Moreover, it will optimally allocate the bandwidth and transmit power for each tenant by introducing a new radio resource allocation technique. The performance of the STORNS can be validated with the help of numerical results by comparing the slicing and non-slicing systems. In this work [32], the authors have presented an optimization approach for resource allocation and mode selection in fog RAN. It also provides a RAN slicing mechanism for F-RAN where fog UEs and legacy UEs are served by the implemented slices.