LAUSR

In this paper, we consider spectrum sensing (SS) problems with various general noise models such as Middleton class A (MCA), isometric complex symmetric $\alpha $ -stable ( $\text{S}\alpha \text{S}$ ), and isometric complex generalized Gaussian distribution (CGGD). This approach enables us to examine the effect of practical phenomena such as impulsive noise on SS problems. In this general framework, we propose a detector based on convolutional neural networks (CNNs) with favorable performance under various noise models. The proposed model-free and data-driven CNN offers robustness in diverse noise scenarios. Thus, it can be utilized in environments with different physical behaviors. We demonstrate this method outperforms the highly regarded likelihood ratio test (LRT) in most cases. For all impulsive cases, the proposed CNN is the superior detector, providing a near-optimum performance for the conventional Gaussian noise. We indicate the proposed data-driven CNN offers an appropriate alternative solution to LRT. However, it requires more computational operations, a rich training dataset, and a training process, instead. Furthermore, the main rationale for proposing this CNN is that it enables the network to generalize its effective performance to various noise models and cases. To this end, quantitative simulations confirm superiority of the proposed CNN compared to other recent deep-learning methods.