# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 4 Simulation Results  
In this paper, the simulation experiments are performed on a PC with a 12 th Gen Intel(R) Core(TM) i9-12900K processor and 8 GB of RAM. To simulate the proposed problem, we use the programming language Python 3.10.4 and PyCharm Community Edition 2022.2.1. In the simulation, we consider one eMBB slice, one URLLC slice, and one NPN slice, i.e., \(|\mathcal{S}_{e}|=|\mathcal{S}_{u}|=|\mathcal{S}_{up}|=1\). The users of the eMBB slice and the URLLC slice are uniformly distributed in a \(4\times 4\) km\({}^{2}\) area. There are three gNodeBs in this area. The users of the NPN slice are generated randomly around a particular gNodeB only. We assume that the number of users in the eMBB, URLLC, and NPN slices are 10, 10, and 5, respectively. The PRU are generated via a uniform distribution within a given interval. Specifically, the personalized rate requirement interval for eMBB users is \([2,4]\) (Mbps), the personalized delay requirement interval for the URLLC users is \([2,2,2.8]\) (ms), and the personalized rate and delay requirement interval for the NPN users is \([2.5,5]\) (Mbps) and \([4.5,6]\) (ms), respectively. We set the user satisfaction threshold of the eMBB, URLLC, and NPN slice to 0.9, 0.9, and 1, respectively, i.e., \((^{ae}_{min},^{eu}_{eun},^{eu}_{min})=(0.9,0.9,1)\). We assume that the SFC of the eMBB slice is VNF1 \(\rightarrow\) VNF3 \(\rightarrow\) VNF4 \(\rightarrow\) VNF5, the SFC of the URLLC slice is VNF2 \(\rightarrow\) VNF4 \(\rightarrow\) VNF5 \(\rightarrow\) VNF6, and the SFC of the NPN slice is VNF1 \(\rightarrow\) VNF7 \(\rightarrow\) VNF3 \(\rightarrow\) VNF8. The core link transmission delay is uniformly distributed from 0.5 ms to 2 ms and the core link bandwidth is uniformly distributed from 10 Mbps to 50 Mbps. The remaining simulation parameters are summarized in Table 2.  
We create and train the neural networks of the IPPO using the Pytorch 1.10.2 framework. More specifically, for each agent, the Actor Network and the Critic Network both consists of two fully connected hidden layers, each with 64 neurons, and the Adam optimizer is used. With the exception that the activation function of the output layer is softmax, the activation function of the remaining layers is ReLU. The other hyperparameters of IPPO are given in Table 3.  
The RF algorithm is used to determine whether the RB allocation scheme of RAN can be successfully implemented by the VNF mapping in CN. The dataset of the RF algorithm  
Figure 4: Multi-agent interaction with the MDP environment. is the randomly generated sets of \(\mathcal{D}:=\{r^{s},d^{s}_{CN},s\in\mathcal{S}\}\) and the label is 0 or 1, which is found using the DFS+pruning algorithm. We picked 50,000 sets of data with the label 0 and 50,000 sets of data with the label 1 to train the RF. The most important hyperparameter of the RF is the number of decision trees \(n\_estimator\). To find the optimal hyperparameter, the dataset is first divided into a training set, validation set, and test set in the ratio of 3:1:1. Then, the RF is constructed with a different number of decision trees \(n\_estimator\), and the four-fold cross-validation is performed with the training set and the validation set. The mean accuracy of the cross-validation is plotted in Figure 5 for different \(n\_estimator\) values. The red circle marks the optimal point, which corresponds to \(n\_estimator=21\), which is the value we used to build the RF.