# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
###### Abstract  
The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception. collaborative perception; data link; data fusion; weight allocation +
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utilizes carrier sense technology, which is a typical energy detection method. This technology judges whether a node is currently transmitting data on the channel based on the signal energy detected on the channel, thus classifying the channel as either busy or idle. However, it can only make qualitative analyses of channel states. The local channel perception [5; 6; 7; 8] quantitatively analyzes the channel state of data link networks by counting traffic pulses in the channel and indicates the congestion level of the current channel state using channel occupancy statistics. This method, coupled with priority thresholds of data link messages, enhances network throughput and overcomes the limitations of qualitative analysis in carrier sensing technology. However, the local channel perception heavily relies on the channel perception capability of local node devices and is unable to resolve the hidden terminal problem. This results in low reliability and significant application limitations. The collaborative channel perception involves transmitting perceived data from multiple nodes to a data fusion center. At this center, the channel occupancy statistics is calculated using either equal gain combining or weighted combining [9; 10; 11; 12; 13; 14]. In comparison to local channel perception, this approach significantly improves the reliability of channel state perception and has become the dominant method in the field of channel state perception. The collaborative channel perception [15] fuses the perceived data from neighboring nodes within a single-hop range, using a distance-weighted strategy to calculate channel occupancy statistics. This provides a decision-making basis for the transmission of data link messages. This method addresses the limitations of local channel perception by mitigating the dependence on the perception capability of individual node devices. However, it only takes into account the influence of distance factors on channel state perception. In reality, the channel perception capability of node devices is closely related to their transmission and reception performance. Many factors significantly impact the node device channel perception capability, such as receiving sensitivity, the antenna voltage standing wave ratio (VSWR), signal-to-noise ratio (SNR), component aging, and mean time between failures (MTBF). These factors can cause significant fluctuations in the channel perception capability of nodes, resulting in significant deviations from the actual channel state. The unreasonable weight allocation is a drawback of the existing collaborative channel perception.