# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 1 Introduction
NPN can be deployed in two ways [7]: (i) Standalone NPN (S-NPN), where enterprises use a dedicated band and deploy a full 5G network independently. Since it is completely isolated from the PLMN, it can effectively protect the enterprise's data security. The disadvantage is that it is very costly and not suitable for most enterprises. (ii) Public network integrated NPN (PNI-NPN), where the NPN shares some infrastructure with the PLMN to accomplish the network's deployment. NPNs are usually implemented by creating dedicated network slices [8]. Hence, the main issue considered in this paper is the resource allocation in the scenario of the integration of PLMN and NPN, which are both achieved via NS. The resource allocation problem of the NS mainly includes two parts: the resource allocation of RAN and the VNF mapping of CN. For the resource allocation of RAN, ref. [9] focuses on efficient sharing of radio resources among different users while guaranteeing the service requirements. Although this approach usually results in higher multiplexing gains, it does not guarantee performance isolation. A two-level RAN RB resource allocation method is proposed in [10]. It is assumed that when the pre-allocated RB resources of a base station are insufficient, it is allowed to borrow additional RB directly from other base stations. It has the advantage of reducing the signaling overhead and providing an immediate service response to end users. But, the isolation between slices is also not considered. Ref. [11] proposed a resource scheduling scheme that consists of deep learning (DL) in conjunction with reinforcement learning (RL). The main goal is to satisfy the users' QoS requirements with as few resources as possible while satisfying the isolation requirement. The advantage is that DL and RL are integrated together and the isolation of slices is taken into account. The disadvantage is that it only considers the allocation of resources to slices, but it does not continue to allocate them to end users. In [12], a two-layer resource allocation framework is proposed, where the upper and lower layers correspond to the Centralized Unit or Distributed Unit (CU/DU) side and the Remote Radio Unit (RRU) side, respectively. After that, a Q-learning-based NS resource allocation scheme is proposedto maximize the total utility of the whole network. Ref. [13] mainly addresses the problem of bandwidth allocation for deep reinforcement learning-based NS. It enables the weighted sum of spectral efficiency and quality of experience (QoE) to be maximized. For the VNF mapping of CN, ref. [14] proposed a scheme based on the feature decomposition method to implement VNF mapping and traffic control. However, only simple graph mapping relationships are considered, which do not involve the processing of dynamic services and cannot meet the real-time requirements of services. Ref. [15] presents the VNF placement problem in NS, where the optimization problem is to minimize the reliability degradation and cost while maintaining strict delay constraints. A Tabu search-based algorithm is used to find a solution. In [16], a VNF deployment scheme that maximizes operational benefits is proposed to ensure the reliability of the network. Ref. [17] proposed a graph neural network-based algorithm to predict the resource demand by using the topological information of the VNF forwarding graph, thus reducing the failure rate of slices. However, the research above on the resource allocation for NS does not address NPN slices. In other words, only the QoS requirements of different slices are considered, and the differences between public and non-public network slices are not taken into account. Therefore, in this paper, we focus on the resource allocation problem in the scenario where public network slices (including eMBB slices and URLLC slices) and non-public network slices (NPN slices) coexist.