# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 4 Simulation Results and Discussion  
In this section, we evaluate the proposed scheme using Monte Carlo simulations. Matlab is chosen as the simulation tool due to its efficient matrix operation functions. We simulate the interference between UAV clusters by generating a random UAV cluster scenario. Based on this, we employ the aforementioned scheduling scheme to determine the transmission time slots of the UAVs and assess the interference of the entire system. The sequential scheduling approach is used as the benchmark, in which time slots are allocated to UAVs in a sequential manner, with any time slots that violate the RIS constraint being skipped. Figure 4 illustrates an example of time slot selection and the resulting UAV collisions. The figure depicts two UAV clusters, each consisting of four UAVs. The x-axis represents the indices of the times slots, while the y-axis represents the indices of the UAVs within each cluster. The scheduling cycles of the UAVs are given by the vectors \(\left(T_{1}^{N_{c}},\ldots,T_{M}^{N_{c}}\right)=(4,8,12,16)\) and \(\left(T_{2}^{N_{c}},\ldots,T_{M}^{2}\right)=(8,8,14,16)\). The LCM of the UAV periods is denoted by \(N=40\). The dashed vertical line in the figure represents the time slot at which a UAV collision occurs. It can be observed that with the sequential scheme, UAV scheduling collisions occur in a total of eight time slots, whereas with the LIM scheme, only one time slot experiences a collision. Notably, no UAV scheduling collisions occur with the RIM scheme. Figure 3: The illustration of the rollout algorithm based on local interference minimization (LIM). We evaluate the average interference level per time slot across various schemes in our simulation. To generate the elements of the interference matrix, we employ random variables following a Bernoulli distribution. The interference probability, which indicates the likelihood of significant interference between two UAVs, is defined as the expectation of the Bernoulli distribution. For every interference probability, we conduct 100 random interference scenarios. By employing the aforementioned scheduling algorithm, we determine the scheduling time slots for the UAVs and statistically analyze the average interference level for each time slot. The simulation results are given in Tables 2 and 3 for two and three UAV clusters, respectively. A comparison of Table 2a-c reveals that an increase in the number of UAVs per cluster leads to a corresponding increase in average interference level. In the scenario of two clusters with four UAVs per cluster, the RIM scheme can achieve complete interference-free communication. However, in the scenario of six UAVs per cluster, the RIM scheme is unable to completely avoid interference in high-probability scenarios. When comparing Tables 2b and 3a, it is evident that despite having the same total number of UAVs, the average interference level is higher in the three-cluster scenario. This is due to the presence of more sources of interference in the three-cluster scenario. Figure 4: The UAV scheduling results of different scheduling schemes and UAV collisions. \(N_{c}=2\), \(M=4\), \(L=2\). We evaluate the degree of interference reduction after implementing LIM and RIM algorithms in diverse scenarios with varying numbers of clusters and UAVs. For the case of two UAV clusters, it is shown that the LIM scheme can reduce the interference level by 63-75% when compared to the sequential scheduling scheme. Furthermore, the RIM scheme can reduce the interference level by 96-100%. When there are two UAV clusters and each cluster has four UAVs, the RIM scheme completely eliminates interference between UAV clusters. In the scenario with three UAV clusters, the LIM scheme achieves 57-88% reduction in interference compared to the baseline scheme. The RIM scheme, building upon the LIM scheme, provides an additional 64-93% reduction in interference.