Multiview Graph Neural Networks for Spectrum Sensing in Cognitive Radio

Cognitive radio (CR) facilitates efficient spectrum management and optimization in wireless networks with massive wireless access and data transmission demands, where spectrum sensing (SS) is a prerequisite for dynamic spectrum management. Current neural-network-based methods tend to construct discriminative statistical measures as input of networks or implement more complex architectures to improve SS performance, failing to consider the complementarity between different statistical measures. Furthermore, the existing methods represent received signals as fixed-size matrices within Euclidean space, which cannot be reused across various access devices equipped with differing numbers of receiving antennas. To alleviate the above issues, this work proposes a multiplex graph convolutional network-based SS (MGCN-SS) framework. First, multiplex networks integrating multiview and graph structure are constructed as the input. The multiplex network employs a hierarchical structure composed of multiple independent homogeneous graphs to construct the multiview structure. Each network layer corresponds to a view, which is represented in the form of a homogeneous graph. The dependencies among different views are encoded by the coupling adjacency matrix in the multiplex network. Then, the multiplex graph convolution, composed of intralayer graph convolution and interlayer graph convolution, is used to extract high-level cross-view fusion features. Specifically, the intralayer graph convolution utilizes graph spectral convolution to aggregate node representations and smooth the noise interference within the graph. The interlayer graph convolution introduces the attention mechanism to assign different weights to each view, preventing any specific view from dominating the learning process. Extensive experimental results illustrate that the MGCN-SS surpasses the deep-learning-based and conventional methods regarding detection performance and demonstrates strong generalization capabilities.