# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 4 Simulation Results
(1) Allocation in Proportion to the Personalized Requirement of Users (PPRU): First, the personalized requirement of each user \(q_{s,u}^{d}\) or \(q_{s,u}^{v}\) is multiplied with the minimum satisfaction threshold \(\xi_{min}^{s}\) of the slice \(s\) and noted as \(q_{s,u}^{s}\). After that, the minimum RB resources required to satisfy \(q_{s,u}^{\prime}\) are calculated and used as the allocation scheme for user \(u\) in slice \(s\). Figure 9: User satisfaction of each slice. (2) Allocation by Maximizing User Rates (MUR): First, resource pre-allocation is performed to ensure that the allocated resources can meet the QoS demand of each user. After that, the remaining RBs are continuously allocated to the user with the largest rate increment and the rate increment of this user is updated. The allocation is stopped until the user satisfaction of the slice meets the requirements. Take the personalized rate requirement \(q_{s,u}^{s}\) as an example, where the rate increment for user \(u\) is calculated as \(\min(r_{s,u}^{RB},q_{s,u}^{req}-r_{s,u}^{now})\), where \(r_{s,u}^{RB}\) denotes the rate that an RB can provide to user \(u\) in slice \(s\), and \(r_{s,u}^{route}\) indicates the rate that the current user \(u\) has acquired. For personalized delay requirements \(q_{s,u}^{d}\), first convert to the rate requirements and then process them as outlined above. (3) Allocation by Maximizing User Satisfaction (MUS): First, the same resource pre-allocation process is performed as for the algorithm MUR. After that, the remaining RBs are continuously allocated to the user with the largest satisfaction increment and the satisfaction increment of this user is updated. The allocation is stopped until the user satisfaction of the slice meets the requirements. The satisfaction increment is calculated in a manner similar to the rate increment in the MUR algorithm. To avoid the chance of single-moment results, we change the PRU at multiple moments. First, we compare the probability of successfully completing the resource allocation for the four algorithms, randomly changing the PRU, and counting the success rate of the four algorithms in 1000 experiments. The results are shown in Table 4. It is evident that the proposed IPPO-RF algorithm consistently provides a resource allocation scheme that meets all the requirements in every experiment. In contrast, the success rates of the remaining three algorithms are 0.42, 0.54, and 0.61, respectively, i.e., it is not guaranteed that the given resource allocation scheme satisfies all the constraints. Then, compare the profits of the above four algorithms for five different instances when all algorithms successfully perform the resource allocation. The results are shown in Figure 11. It can be observed that, compared to the other three algorithms, the proposed IPPO-RF algorithm can provide resource allocation schemes with higher profit. The three benchmark methods are ranked in descending order of performance as MUR, MUS, and PPRU. The proposed IPPO-RF algorithm outperforms the three benchmark algorithms PPRU, MUR, and MUS by 84%, 46%, and 57%, respectively. It can be concluded that the IPPO-RF algorithm proposed in this paper has better performance and can provide a better allocation scheme. Table 2.0 Security of each slice.