Spectrum Sensing in the Extremely Low SNR Regime by Exploiting Dictionary Learning

In the cognitive radio scenario, the case of extremely low SNR is often encountered, which incurs a deceasing of detection performance. To circumvent this difficulty, we propose a promising spectrum sensing algorithm by exploiting dictionary learning. In the proposed algorithm, the energy of maximum sparse component acquired by dictionary learning is utilized as a test statistic to improve detection performance as much as possible. More importantly, we extract the maximum sparse component of the received signal via a learned dictionary and the forced sparsity 1 to maximize the SNR of maximum sparse component. Some simulations are carried out to verify theoretical results. Simulation results show that the proposed algorithm overwhelmingly outperforms the sparse-representation-denosing-based algorithm and the conventional energy-based algorithm in the extremely low SNR case.