# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Total number of users, \(U\) & 25 \\ \hline Number of gNodeBs, \(B\) & 3 \\ \hline Number of slices, \(S\) & 3 \\ \hline Total number of RBs, \(K\) & 80 \\ \hline Bandwidth of an RB, \(W\) & 180 KHz \\ \hline Downlink transmission power, \(P_{d}\) & 30 dBm \\ \hline Power of AWGN, \(\sigma^{2}\) & \(-114\) dBm \\ \hline Pathloss from gNodeB to user & \(128.1+37.6\ log_{10}(D[\text{km}])\) dB \\ \hline Channel & Rayleigh fading [19] \\ \hline Packet arriving rate per user in the corresponding slice, \(\lambda_{e},\lambda_{ie},\lambda_{np}\) & 100, 10, 50 packets/s \\ \hline Packet length per user in the corresponding slice, \(\omega_{e},\omega_{u},\omega_{np}\) & 5, 2, 9 kbits \\ \hline Number of server nodes, \(V\) & 8 \\ \hline Number of VNF types, \(F\) & 8 \\ \hline Server computing resources, \(\mathcal{O}\) & \(\{10,9,5,2,5,3,12,5\}\times 10^{2}\) \\ \hline Number of server VMs, \(\mathcal{M}\) & \(\{3,2,2,1,1,1,2,2\}\) \\ \hline Data rate expansion ratio, \(\beta\) & \(\{0.9,0.7,1.2,1.5,0.8,1.0,8.1,1.1\}\) \\ \hline Correlation coefficient, \(\alpha\) & \(10^{-5}\) \\ \hline Unit price of service rate, \(\varrho\) & 20 \\ \hline Unit price of RB and computing resources, \(\sigma_{1},\sigma_{2}\) & 13, 0.1 \\ \hline Minimum rate of slices, \(r^{e}_{min^{\prime}}r^{u}_{min^{\prime}}r^{pp}_{min}\) & 2, 1, 2.5 Mbps \\ \hline Maximum delay of slices, \(d^{e}_{max},d^{u}_{max},d^{up}_{max}\) & 10, 3, 6 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Value** \\ \hline Learning rate of actor & 0.0002 \\ \hline Learning rate of critic & 0.0001 \\ \hline Number of episodes & 5000 \\ \hline Discount factor & 0.99 \\ \hline The scope of the clip, \(\varepsilon\) & 0.2 \\ \hline Loss function & Mean square error \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyper parameters for IPPO. We train an RF model with the total training set, which includes the training set and the validation set. The accuracies achieved on the total training set and the test set is 0.99994 and 0.99910, respectively. A better way to test the training effectiveness of the model is to use the confusion matrix. As shown in Figure 6, where the rows correspond to the predicted labels and the columns correspond to the true labels, each element represents the number of data belonging to the corresponding case. It can be seen that for both the training set and the test set, the overall predictions are reasonably accurate, despite a few instances of incorrect predictions. It can be concluded that the well-trained RF model has a high accuracy in predicting the feasibility of VNF mapping solutions in CN. The IPPO model used to find the optimal RB allocation scheme is generated and trained according to the simulation parameters in Table 2. The cumulative reward for each episode is shown in Figure 7, where Figure 7 shows the actual episode reward curve during the training process and Figure 7 shows the reward curve after moving average processing. It can be seen that as the number of training episodes increases, the reward keeps increasing and the performance of the agent improves significantly at around 1000 episodes, eventually converging gradually. The result shows that the training of IPPO is effective and the multiple agents learn how to choose the action so that the reward (i.e., the objective function of the model) is as large as possible. In order to illustrate the benefits of using the RF algorithm, we compare two cases. In both cases, the outer loop of finding the optimal RB allocation scheme uses the proposed IPPO method. In one case, the inner loop uses RF to determine the feasibility of the VNF mapping, denoted as IPPO-RF, and in the other case, DFS is used in the inner loop, denoted as IPPO-DFS. Figure 5: Cross-validation accuracy graph for RF. Figure 6: Confusion matrix.