# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results
### Joint CSS Method
**(b)**\(P_{\mathbf{f}}\) versus SNR (dB) of BEISTA-Net+BSWSS-Net, ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN when \(P_{\mathbf{d}}=0.9\). reaches 0.5, the detection probability of the BEISTA-Net+BSWSS-Net joint algorithm remains around 1, while the false alarm probability increases to approximately 0.45. Compared to the corresponding WSS algorithms, this represents a noticeable decline in detection performance, primarily due to the significantly reduced information obtained from compressive sampling, which degrades the quality of the reconstructed signal. Overall, the proposed joint CSS algorithm demonstrates robust performance across varying sparsity levels. Figure 16 illustrates the ROC at \(\text{SNR}=0\) dB. From the figure, it is clear that BEISTA-Net+BSWSS-Net significantly outperforms ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN. At \(P_{\text{f}}=0.1\), \(P_{\text{d}}\) for BEISTA-Net+BSWSS-Net is 88.23%, while ISTA-Net+DeepSense achieves 83.95%, CSS-Net achieves 82.02%, and R-ADMM+ParallelCNN achieves 78.82%. Compared to the current state-of-the-art algorithms, the \(P_{\text{d}}\) is improved by 4 percentage points when \(P_{\text{f}}=0.1\) and \(\text{SNR}=0\) dB. Then, we evaluate the performance in the NR dataset, with \(\mu_{NR}\in(0,0.5]\), \(P=8\), and \(L=16\). We also study the effect of SNR and signal sparsity level on the CSS algorithm, as well as ROC. From Figures 17-19, it is clear that the BEISTA-Net+BSWSS-Net joint algorithm outperforms the other three algorithms. Compared to the results in the TVWS dataset, the four algorithms show better performance in the NR dataset. This is because the label dimension in the NR dataset is smaller, leading to better classification results. In the NR dataset, BSWSS-Net outperforms the other three algorithms to a greater extent than in the TVWS dataset. Compared to the current state-of-the-art algorithms, the detection probability has been improved by nearly 4 percentage points when \(P_{\text{f}}=0.1\) and \(\text{SNR}=0\) dB. Figure 15: TVWS dataset with \(\text{SNR}\in[-10,10]\) dB, \(P=8\), \(L=16\). (**a**) \(P_{\text{d}}\) versus SNR (dB) of BEISTA-Net+BSWSS-Net, ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN when \(P_{\text{f}}=0.1\). (**b**) \(P_{\text{f}}\) versus SNR (dB) of BEISTA-Net+BSWSS-Net, ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN when \(P_{\text{d}}=0.9\). From the experimental results of the aforementioned two datasets, it is evident that the proposed BEISTA-Net+BSWSS-Net joint algorithm consistently achieves the highest detection probability and the lowest false alarm probability under varying noise conditions and sparsity levels. The algorithm is capable of adapting to scenarios with significant variations in channel conditions, achieving robust detection performance. Figure 16: ROC curves when \(\text{SNR}=0\) dB in TVWS dataset, \(\mu_{NR}\in(0,0.5]\), \(P=8\), and \(L=16\). Figure 17: NR dataset with \(\mu_{NR}\in(0,0.5]\), \(P=8\), \(L=16\). (**a**) \(P_{\text{d}}\) versus SNR (dB) of BSWSS-Net, CNNWSS-Net, DeepSense, and ParallelCNN when \(P_{\text{f}}=0.1\). (**b**) \(P_{\text{f}}\) versus SNR (dB) of BSWSS-Net, CNNWSS-Net, DeepSense, and ParallelCNN when \(P_{\text{d}}=0.9\).