## 1 Introduction  
In recent years, the fifth generation of communication networks, or simply 5G, has been continuously reshaping the ICT landscape. Along with the advent of 5G and beyond technologies, many services have also emerged, including augmented and virtual reality, vehicle-to-everything communications, e-health, and smart homes. Due to this diversity of existing services, the International Telecommunication Union (ITU) has identified three major usage scenarios for 5G services, namely: Ultra-Reliable Low Latency Communications (uRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communications (mMTC) [1]. Each of these usage scenarios has specific requirements that distinguish the types of resources allocated for each service request. To be specific, adaptive and on-demand resource provisioning methods based on varying service request types are needed to meet user needs [2]. Moreover, it is essential to meet the various requirements of these services to provide end-users with the best quality of service (QoS) possible. One of the critical technologies on 5G and beyond systems is network slicing (NS) [3]. NS refers to creating multiple virtual networks within a 5G physical infrastructure to constitute a physical network. This technology is made possible by Software-Defined Networking (SDN) [4] and Network Function Virtualization (NFV) [5]. Moreover, each slice in the network has certain network functions tailored to the different services required by users. A classification of these slices includes service, resource, and deployment-driven NS solutions [6]. Moreover, creating slices for specific services within the physical network also ensures that network resources are efficiently utilized or allocated throughout the system. Likewise, NScan be broken down into three major processes: slice creation, slice isolation, and slice management [7]. This categorization of NS processes is further expanded into slice monitoring, slice mapping, and slice provisioning, as discussed in [8]. Recently, NS has become the subject of most research related to 5G networks, as it can improve service delivery and QoS. For example, the work in [9] studied the management and allocation of radio access networks (RAN) resources focusing on its impact on uRLLC and eMBB slices. The authors proposed an intelligent decision-making technique to manage network traffic and allocate the required resources. The work in [10] enumerates several factors that affect the implementation of network slices. These include resource allocation, slice isolation, security, RAN virtualization, feature granularity, and end-to-end (E2E) slice orchestration. [PERSON] et al. presented the applications of machine learning (ML) and artificial intelligence (AI) for NS solutions in [11]. Their paper listed various ML and AI algorithms, and applications for different NS use-cases such as mobility prediction and resource management. Reference [12] studied the challenges in slice admission and management. The work investigated network revenue, QoS, inter-slice congestion, and slice fairness and possible solutions through various slice admission strategies and optimization techniques. [PERSON] et al. [13] investigated how the NS process can improve E2E QoS in 5G networks by proposing an NS framework through effective resource allocation. Two scenarios, including (1) radio resource slicing for heterogeneous networks (HetNets) and (2) bi-resource slicing for core networks, were investigated to evaluate the efficiency of the proposed framework. Recent studies have also applied the growing popularity of Deep Learning techniques to NS scenarios. For example, in [14], a framework for NS called DeepSlice was developed to classify incoming network service requests as either uRLLC, eMBB, or mMTC requests using Deep Learning. Ideal network slices for slice requests are then provided based on the classification results, with additional efficient resource allocation and network traffic regulation.