# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 4 Simulation Results and Analysis
Since channel perception [5; 6; 7; 8] belongs to the local perception method, and the channel perception [5] is the most typical one, this paper, therefore, took the channel perception [5] as the comparison object for the local channel perception in the analysis of simulation results. Through analyzing the simulation results in Figure 3, under identical channel occupancy statistics, the network throughput of the method proposed in this paper is the most optimal. In contrast, the local channel perception [5] performs the worst, while the collaborative channel perception [15] falls in between. This is primarily due to the fact that the local channel perception heavily relies on the traffic pulse statistical capability of the local node device to perceive the channel state, without considering the neighboring nodes' perception capability. Consequently, when the node device is impacted by factors like electromagnetic interference or sensor instability, the accuracy of traffic pulse statistics decreases, leading to a significant deviation of the channel occupancy statistics from the actual channel state. This results in errors in decision making for transmitting data link messages, reducing the success rate of data link message transmission and ultimately decreasing the network throughput. The collaborative channel perception [15] overcomes the dependency on the traffic pulse statistical capability of a single node device and employs the distance-weighted fusion of multiple node statistics data. However, the distance-weighting factor does not  
Figure 4: The impact of channel occupancy statistics on transmission delay. the collaborative channel perception [15]. the local channel perception [5]. channel perception [7]. channel perception [6]. channel perception [8]. Figure 3: The impact of channel occupancy statistics on network throughput. the collaborative channel perception [15]. the local channel perception [5]. channel perception [7]. channel perception [6]. channel perception [8]. accurately reflect the traffic pulse statistical capability of nodes. Factors that affect the statistical capability of node devices, such as antenna VSWR, SNR, and component aging, are not reflected in the node weight allocation strategy. This results in errors in the channel occupancy statistics. Nevertheless, the deviation of these errors is less significant than that of the local channel perception. In contrast, the method proposed in this paper scientifically evaluates the traffic pulse statistical capability through evidence fusion theory and assigns data fusion weights based on the credibility and falsity of characteristic parameters of node devices. This enables the calculated channel occupancy statistics to accurately reflect the actual state of the current data link network channel. This, in turn, improves the success rate of data link message transmission and subsequently enhances the network throughput. Through analyzing the simulation results in Figure 4, it can be observed that, under the same channel occupancy statistics, the transmission delay of the method proposed in this paper is optimal. In contrast, the local channel perception [5] performs the worst, while the collaborative channel perception [15] falls in the middle. Specifically, when the value of channel occupancy statistics is at 50%, the transmission delay of the method proposed in this paper is reduced by 21% compared to the local channel perception and by 19% compared to the collaborative channel perception [15]. This is primarily due to the fact that the proposed method can scientifically evaluate the traffic pulse statistical capability of nodes within a one-hop range and allocate data fusion weights based on the credibility and falsity of their characteristic parameters. The channel occupancy statistics can accurately reflect the actual state of the current channel, thereby improving the accuracy of data link message transmission decisions and reducing the probability of data link message exchange conflicts within the network.