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Local Cooperative Sensing With Temporal–Spatial Decoupling
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AI
DOI：10.1109/LCOMM.2025.3645792.png)
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Abstract
En 中文
Spectrum sensing in 3D environments is critical for reliable autonomous aerial vehicle (AAV) communications. However, in realistic spectrum availability-heterogeneous environments, the complex spatiotemporal coupling characteristic challenges extracting both temporal and spatial features simultaneously. Therefore, this letter proposes a temporal-spatial decoupled local cooperative framework, decomposing the complex sensing task into two relatively simpler subtasks. Specifically, a composite feature representation integrating auto-correlation and cross-correlation matrices is introduced to enrich sample information. Furthermore, a multi-residual convolutional neural network (CNN) with a channel attention mechanism is designed as a universal classifier, maintaining superior nonlinear fitting capability while controlling the network scale. Experiments demonstrate that the proposed strategy achieves superior sensing performance compared to existing methods.
Keywords:
Spectrum sensing
AAV
spectrum availability-heterogeneity
CNN
Journal
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