# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results
### BSWSS-Net  
#### 5.2.1 Sense Accuracy  
As shown in Figure 8a, we compare \(P_{\mathbf{d}}\) of BSWSS-Net, CNNWSS-Net [18], DeepSense [21], and ParallelCNN [22] under various SNR conditions at \(P_{\mathbf{f}}=0.1\) in the TVWS dataset. As shown in Figure 8b, we compare \(P_{\mathbf{f}}\) of BSWSS-Net, CNNWSS-Net, DeepSense, and ParallelCNN under various SNR conditions at \(P_{\mathbf{d}}=0.9\) in the TVWS dataset. As SNR increases, \(P_{\mathbf{d}}\) increases, and the \(P_{\mathbf{f}}\) decreases, indicating an improvement in algorithm performance. From these figures, it can be observed that the performance of BSWSS-Net and CNNWSS-Net is similar and much better than DeepSense and ParallelCNN. When \(\text{SNR}>-3\) dB, BSWSS-Net can meet the requirement of \(P_{\mathbf{d}}>0.9\), while \(P_{\mathbf{f}}<0.1\). These results indicate that, under varying levels of noise, BSWSS-Net can more effectively identify spectrum holes with a higher probability, thereby improving the efficiency and reliability of spectrum sensing.  
Next, the impact of signal sparsity level on detection performance is evaluated in the TVWS dataset. Figure 9a illustrates the \(P_{\mathbf{d}}\) versus sparsity level at \(P_{\mathbf{f}}=0.1\), Figure 9b illustrates the \(P_{\mathbf{f}}\) versus sparsity level at \(P_{\mathbf{d}}=0.9\). It is distinctly observable that with the augmentation of signal sparsity, the detection probabilities of the four spectrum-sensing algorithms diminish, while the false alarm probabilities escalate. This phenomenon can be attributed to the fact that higher signal sparsity implies a greater number of users within the same bandwidth, leading to increased signal comp  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **\# of FLOPs** & \begin{tabular}{c} **\# of Learnable** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{c} **Execution** \\ **Time (ms)** \\ \end{tabular} &
\begin{tabular}{c} **Video Memory** \\ **Usage (MB)** \\ \end{tabular} \\ \hline ISTADR-Net & \(2.6\times 10^{8}\) & 64 & 4.2 & 18.1 \\ R-ADMM & \(1.7\times 10^{8}\) & \(6.6\times 10^{4}\) & 11.4 & 14.8 \\ ISTA-Net & \(7.2\times 10^{8}\) & \(2.4\times 10^{6}\) & 2.1 & 49.4 \\ BEISTA-Net & \(7.3\times 10^{8}\) & \(2.5\times 10^{6}\) & 3.9 & 56.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity analysis of different reconstruction algorithms.  
degrades detection performance. At a sparsity level of 0.05, the detection probabilities of the several algorithms approximate 1, and the false alarm probabilities are nearly 0. When the sparsity reaches 0.5, the BSWSS-Net achieves a detection probability of approximately 0.86 and a false alarm probability of about 0.15. Under identical conditions, CNNWSS-Net, DeepSense, and ParallelCNN exhibit detection probabilities significantly lower than that of BSWSS-Net, and their false alarm probabilities are notably higher. In summary, the proposed BSWSS-Net attains excellent detection performance across varying sparsity levels.  
Figure 10 illustrates the receiver operating characteristics (ROC) at \(\text{SNR}=-4\) dB. It is evident that as the false alarm probability \(P_{f}\) increases, the detection probability of all four algorithms also increases. Under the same conditions, BSWSS-Net consistently achieves the highest detection probability. Specifically, when \(P_{f}=0.1\), the detection probability of BSWSS-Net nearly reaches 0.9, essentially meeting the requirements of cognitive radio. This observation clearly demonstrates that the detection performance of the BSWSS-Net algorithm surpasses that of the other three algorithms.  
Figure 10: ROC curves when \(\text{SNR}=-4\) dB in TVWS dataset and \(\mu_{TVWS}\in(0,0.5]\).  
Figure 9: TVWS dataset with \(\text{SNR}\in[-10,10]\) dB. (**a**) \(P_{\text{d}}\) versus sparsity level of BSWSS-Net, CNNWSS, DeepSense, and ParallelCNN when \(P_{\text{f}}=0.1\). (**b**) \(P_{\text{f}}\) versus sparsity level of BSWSS-Net, CNNWSS-Net, DeepSense, and ParallelCNN when \(P_{\text{d}}=0.9\).