# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion
### Performance Evaluation
The AUC values corroborated the findings from the detection probabilities, indicating that the models employing an end-to-end \(1\)DCNN RNN structure at the SU level demonstrated superior performance in terms of sensitivity at low \(P_{fa}\) rates and overall detection capability compared to the CNN_LSTM and cooperative LSTM detectors. Additionally, we examined the detection performance of the proposed method across various SNR levels by plotting the detection probability against the SNR curves. Figure 8 displays these curves for the proposed detectors, as well as other conventional methods, with a fixed \(P_{fa}\) of \(0.1\). Our focus is on showcasing the simulation results using signals with lower SNRs ranging from \(-20\) dB to \(-10\) dB, as models that excel at lower SNRs tend to perform better at higher SNRs as well. Despite observing some performance degradation at low SNR values, the proposed DNN methods still exhibit promising outcomes. Notably, the CNN-LSTM detector does not perform as well as the other proposed detectors that utilize an end-to-end \(1\)DCNN RNN structure at the SU level. This disparity can be attributed to the structure of the CNN-LSTM detector. The challenge lies in  
Figure 7: ROC curves under \(-14\) dB SNR condition. information by combining the outputs of the 1 DCNN from all SUs before processing with a single LSTM. There is a potential loss of individual SU-specific information in this approach. On the contrary, employing separate LSTMs at each SU allows for the individual modeling and capture of SU-specific information. This enables the model to learn hierarchical representations directly from the raw signal data, which proves advantageous when the raw signal contains critical temporal patterns or spatial features crucial for SS. Furthermore, it is evident that the proposed DNN model based on bidirectional LSTM outperforms the one based on unidirectional LSTM. This superiority stems from the bidirectional LSTM's capability to effectively capture temporal features from the signals, resulting in improved detection performance. Moreover, it appears evident that the MED algorithm outperforms the proposed ones under low-noise conditions (actually, starting from \(-12\) dB). This occurs due to its simplicity and the direct nature of its comparison algorithm. This feature makes the distinction between statistical test results and the threshold more obvious under low-noise conditions. Although MED seems to perform best under high SNR conditions, it should be noted that the DNN maintains near-perfect performance too, which is acceptable. The DNN is trained under a wide range of low SNR conditions, enabling it to efficiently extract complex patterns and features from signals, even in challenging and noisy environments typical of low SNR scenarios. The wide range of low SNR conditions in our training data, while beneficial for robust performance in challenging environments, may marginally affect the DNN's performance at higher SNR values. Consequently, the observed superiority of our approach in low SNR environments aligns perfectly with our design objectives and meets the practical requirements of real-world applications. Importantly, the DNN also addresses the limitations of traditional approaches, such as MED, at low SNR conditions. Ensuring high accuracy in SS is crucial in granting SUs access to the spectrum while avoiding interference with the primary user. However, this increased accuracy also leads to a longer sensing time, thereby reducing the permitted data transmission time for SUs. Therefore, it is essential to determine an optimal sensing time that maximizes the throughput for SUs while adequately protecting the PU. To evaluate the proposed methods, we compare their response times, specifically the time required to classify an example (which consists of a sensing period of 64 samples). Figure 8: Pd-SNR curves with a fixed \(P_{fa}\) of 0.1. The computation time results for various methods are summarized in Table 3, where the online detection time is measured in milliseconds.