# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Node Weight Allocation Strategy  
Through the above analysis, it is evident that, in the process of channel state perception within the data link network, acquiring the traffic pulse counting values recorded by each node is necessary to calculate current channel occupancy statistics. However, given the variations in the electromagnetic environment, transmission channels, and device reliability among different nodes, the credibility of the traffic pulse counting values recorded by each node varies obviously. This makes adopting an equal gain combining method for data fusion inappropriate. The statistical capability of the traffic pulse is closely related to its transmission and reception performance. Therefore, accurately measuring the transceiver capabilities of each node device is crucial for enhancing the reliability of channel state perception. In this paper, five characteristic parameters for node devices were established, including receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF. An evidence fusion algorithm was employed to assess the differences in transmission and reception performance among node devices, serving as a foundation for scientifically evaluating the statistical capability of the traffic pulses. In this paper, based on the evidence fusion theory, the corrected credibility and falsity were used to measure the difference and conflict of characteristic parameters of node devices. The higher the credibility and the lower the falsity of a characteristic parameter, the stronger the traffic pulse statistical capability of the node device. Therefore, when performing channel state perception, a higher weight should be assigned to such node devices. Conversely, the lower the credibility and the higher the falsity of a characteristic parameter, the weaker the traffic pulse statistical capability of the node device, and a lower weight should be assigned to it. Thus, the credibility and falsity of characteristic parameters can serve as a basis to assign channel state perception weights to each node within a one-hop range. Since both the credibility and falsity of each node device share the same positive or negative attributes, the geometric mean method can be employed to calculate the channel state perception weight of each node device. The weight of the \(j\)-th node device is expressed as:  
\[\omega_{j}=\sqrt[n]{\prod_{j=1}^{n}Crd^{\prime}\left(m_{j}\right)}+\sqrt[n]{ \prod_{j=1}^{n}\left[1-F^{\prime}\left(m_{j}\right)\right]} \tag{20}\]  
where \(n\) represents the number of characteristic parameters for the node device. In this paper, \(n=5\). \(j\) represents the number of nodes within the one-hop range of the current node (including the current node itself), with \(j=1\), \(2\), \(\ldots\), \(N+1\). \(Crd^{\prime}\left(m_{j}\right)\) represents the corrected credibility of the \(j\)-th characteristic parameter for the \(j\)-th node, while \(F^{\prime}\left(m_{j}\right)\) represents the corrected falsity. The normalized weight assigned to the node device is expressed as:  
\[\alpha_{j}=\frac{\omega_{j}}{\sum_{k=1}^{N+1}\omega_{k}} \tag{21}\]  
Through this formula, the node weights are converted into normalized values ranging from 0 to 1. Compared to the existing collaborative channel perception, this method increases the number of node characteristic parameters from one to five. Data fusion technology is utilized to analyze the credibility and falsity of these five node characteristic parameters. Based on this analysis, allocation weights are determined, and ultimately, the channel perception results are derived. From a computational complexity perspective, the complexity of this method involves two main aspects. Firstly, there is the data fusion calculation process for five node characteristic parameters. Secondly, there is the process of calculating channel perception results based on weights. The complexity of the data fusion calculation process is primarily related to the number of node characteristic parameters and the number of nodes within the one-hop range.