A Cooperative Spectrum Sensing Method Based on Soft Low-Rank Subspace Clustering

When processing sensing signals under low signal-to-noise ratio environment, the sensing performance cannot be guaranteed in existing algorithms. To ensure sensing performance, we propose a novel spectrum sensing algorithm based on soft low-rank subspace clustering (SLRSC) in this brief. Firstly, the lowest rank coefficient matrix of signal vectors is calculated by low-rank representation, and the adjacency matrix is built to make coefficient matrix balance. Secondly, the eigenvalue of the adjacency covariance matrix is extracted as a feature. Finally, variable weighting k-means method is used to cluster, which avoids complicated threshold derivation and improves cluster accuracy. Simulation results prove that the proposed SLRSC algorithm has excellent sensing performance under high noise case.