Blind Spectrum Sensing with Low Rank and Sparse Model

Spectrum Sensing is a cornerstone in cognitive radio which can detect the spectrum holes in order to raise spectrum utilization ratio. Traditional spectrum sensing detectors depend on some prior information or restricted by low signal-noise ratio and computation complexity in practical application. A GoDec based spectrum sensing detector is proposed for combining covariance based methods with low rank and sparse model theory. The proposed detector divides the received signal into two segments of equal length, and then decomposes the covariance matrix respectively by low rank and sparse matrix decomposition. The primary user exists if the difference between the low rank matrices is lower than a predefined threshold. Simulation results show that the proposed detector has robustness and high detection probability.