# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 4 Simulation Results and Analysis  
To further analyze the performance of the proposed data link network collaborative channel perception in this paper, the simulation verification of the above theoretical analysis was conducted. The computer we used for the simulation had a 32 GB cache, a Core i9 CPU, and its operating system was Windows 11. The code was implemented using Matlab 2019. The simulation parameters are listed in Table 1.  
We simulated and analyzed how the number of one-hop neighbor nodes affects the value of channel occupancy statistics. Additionally, we conducted a comparative analysis between the local channel perception [5; 6; 7; 8] and the collaborative channel perception [15], as shown in Figure 2.  
Through analyzing the simulation results in Figure 2, we can observe that, with the change in the number of neighbor nodes within the one-hop range, the channel occupancy statistics calculated by the local channel perception [5] exhibits the greatest fluctuation. The collaborative channel perception, on the other hand, shows moderate fluctuation. The method proposed in this paper, however, displays the smallest fluctuation and gradually stabilizes. This is mainly due to the fact that the local channel perception [5] calculates channel occupancy statistics by counting the number of traffic pulses in the channel based on the local node. This heavily relies on the channel perception capability of the local node device. When the characteristic parameters of the local node device are randomly selected within the normalized range from 0.70 to 0.95, they significantly affect the node's traffic pulse statistical capabilities, leading to large fluctuations in the value of channel occupancy statistics. The collaborative channel perception [15] overcomes this issue by utilizing data from neighboring nodes for fusion to calculate channel occupancy statistics. However, during data fusion, the weight allocation does not match the channel perception capability of node devices, failing to eliminate the influence of fluctuations in characteristic parameters on the channel occupancy statistics. In contrast, the method proposed in this paper addresses this issue by allocating data fusion weights based on the channel perception capability of node devices. This approach can suppress the influence of fluctuations in characteristic parameters on the channel occupancy statistics, thereby improving the accuracy of channel state perception. The collaborative channel perception calculates the channel occupancy statistics through rational weight allocation and multi-node data fusion, which represents the congestion level of the current channel state. By combining the channel occupancy statistics with a data link message priority threshold, it performs data link message channel access control, thereby significantly impacting network throughput and transmission delay. Based on the simulation parameters shown in Table 1, this paper simulated and analyzed the impact of channel occupancy statistics on network throughput and transmission delay. The results are shown in Figures 3 and 4, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline network protocol & SPMA & packet inter-arrival time & Poisson distribution \\ channel environment & multipath fading channel & statistical time widow size & 100 ms \\ network scale & number of nodes within one & packet size & 50 bit \\  & hop: 0–40 & packet priority & 0–7 \\ transmission rate & 0.1 Mbit/s & packet priority & binary exponential backoff \\ the range of characteristic & 0.70 to 0.95 & backoff algorithm & algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 2: The impact of the number of one-hop neighbor nodes on channel occupancy statistics. the collaborative channel perception [15]. the local channel perception [5].