# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 1 Introduction
Compared with public network slices, the characteristics of NPN slices are reflected in two aspects: (i) NPN slices require higher service priority and reliability; and (ii) NPN slices require higher security and privacy. In addition, we summarize the related works and compare them with our work in Table 1.  
In this paper, we first formulate the E2E resource allocation problem with both public and non-public slices, which is a nonlinear integer programming problem. Next, due to the nature of the problem, we split the joint optimization problem into two sub-problems: the RB allocation problem and the feasibility problem of VNF mapping. Then, a combination of deep reinforcement learning (DRL) and machine learning (ML) algorithms are used to solve the problem. More specifically, DRL is employed to provide the RB allocation scheme that makes the objective function, which is the network operator's profit, as large as possible. ML assists in determining whether the allocation scheme provided by DRL satisfies the constraints, thus helping the DRL agents to receive fast reward feedback. For deep reinforcement learning, we use multi-agent reinforcement learning to reduce the action space, and the specific algorithm employed is independent proximal policy optimization (IPPO). For machine learning, we speed up the solution feasibility determination by training a binary classifier, and the specific algorithm used is random forest (RF). Therefore, we name the proposed algorithm _the IPPO-RF algorithm_. The simulatio \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Objective Function** & **Slicing Domain** & **Optimization Algorithm** & **Non-Public Network** \\ \hline
[9] & Throughput maximization & RAN & Nash Q-learning \& MCTS & \(\times\) \\ \hline
[10] & Sum-rate maximization & RAN & Deep Q-Learning & \(\times\) \\ \hline
[11] & Utilization maximization & RAN & LSTM \& A3C & \(\times\) \\ \hline
[12] & Utility maximization & C-RAN & Multi-agent Q-learning & \(\times\) \\ \hline
[13] & Spectral efficiency maximization & E2E & Deep Q-Learning & \(\times\) \\ \hline
[14] & Revenue maximization & CN & Feature decomposition \& Heuristic & \(\times\) \\ \hline
[15] & Cost minimization & CN & Tabu search & \(\times\) \\ \hline
[16] & Expenditures minimization & CN & Heuristic algorithm & \(\times\) \\ \hline
[17] & Failure rate minimization & CN & Graph neural network & \(\times\) \\ \hline Our work & Profit maximization & E2E & Deep reinforcement learning \& \(\checkmark\) \\  & & Machine learning & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works. IPPO-RF algorithm has good performance, i.e., not only are all the constraints satisfied, but the service priority and security of the NPN slices are ensured. The main contributions of this paper can be summarized as follows. * We solved the resource allocation problem associated with E2E network slicing, which involves both the RB allocation problem in the RAN and the VNF mapping problem in the CN. * 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. Compared with public network slices, the characteristics of NPN slices require higher reliability and higher privacy. * We innovatively utilize a collaborative approach, combining DRL and ML, to address the E2E resource allocation problem of network slicing. The significance of the algorithm above lies in its contribution of a valuable approach for solving similar problems. The rest of the paper is organized as follows. The system model is presented in Section 2. In Section 3, we first describe how to split the joint optimization problem of this paper into two sub-problems. Next, a combination of DRL and ML algorithms are introduced to solve the problem. In Section 4, we present the simulation results as well as discussions, and finally, we conclude the paper in Section 5.