# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion
### Performance Evaluation
According to the table, it is evident that all DNN methods exhibit higher online detection times. This can be attributed to the parallelization of convolution operations using a GPU, whereas LSTM operations cannot be parallelized. Notably, the conventional SS method demonstrates the shortest online detection time since the maximum eigenvalue detector involves simpler computations compared to a DNN, which typically entails more complex operations involving forward propagation through multiple layers. However, this trade-off is offset by the superior accuracy offered by the DNN-based methods. Although the cooperative LSTM approach utilizes a \(15\times 15\) input shape, which is still lower than our vectorized input, it exhibits the highest online detection time due to the two-dimensional CNN architecture and computational complexity discussed earlier. In comparison to other proposed methods, the bidirectional LSTM-based method incurs the longest online detection time among the proposed approaches, but it only requires an additional millisecond to process a single piece of input data. Nevertheless, it delivers the best performance across all SNR conditions. Consequently, we can conclude that the methods based on bidirectional LSTM present the most favorable compromise. Furthermore, we examine the influence of different sample lengths on the detection performance. We specifically set the sample length, denoted as N, to three different values: 32, 64 and 128. Figure 9 illustrates the receiver operating characteristic curves of the proposed bidirectional LSTM-based method for these various sample lengths. The graph clearly demonstrates that a larger sample length leads to a higher probability of detection. This is attributed to the fact that longer samples contain a greater amount of correlation and pattern information, allowing for the more accurate extraction of temporal features from the received signal data. However, it is important to note that longer sample lengths introduce increased computational complexity and latency, which can be problematic for real-time applications that require swift response times. Therefore, when contemplating the utilization of larger sample lengths, it is essential to carefully consider the trade-off between the potential benefits of enhanced accuracy and these associated drawbacks. In a CSS systems, SU devices often operate at different SNR levels. This variation in SNR can occur due to several factors, including differences in the distance from the PU transmitter, varying channel conditions, varying interference levels and different transmission power levels. Considering this inherent variability in the SNR among SU devices is important for the realistic simulation and evaluation of CSS systems. It enables a more accurate assessment of the system's performance and its ability to adapt to and operate in the diverse SNR conditions commonly encountered in real-world scenarios. To simulate this scenario, a specific approach was employed. To each SU device, we assigned a mean SNR value of \(-14\) dB; the actual SNR was obtained by adding random values chosen from a uniform distribution with different ranges. Three separate simulations were conducted using the bidirectional LSTM models. In each simulation, the added values were uniformly selected from the ranges \([-1,1]\) dB, \([-2,2]\) dB and \([-3,3]\) dB, respectively. The results obtained from the aforementioned scenario were then compared to the previous case, as described earlier, where all SU devices were assumed to share the same SNR value. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Online Detection (ms)** \\ \hline CNN\_LSTM & 22 \\ CNN\_LSTM\_MLP & 22 \\ CNN\_BLSTM\_MLP & 23 \\ Cooperative LSTM & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computation time comparison of different methods (sensing period: 64 samples). Figure 10 illustrates the results according to the specific scenario described above.