# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 2 Related Works  
The conventional approaches for SS can be classified into three main groups, which are energy detection [18], feature-based detection [19] and matched filter detection (MFD) [20]. The latter utilizes a predefined filter to conduct correlation operations between known and unknown signals, aiming to detect specified frequency and modulation signals effectively. One notable advantage of this approach is its ability to achieve a low probability of false detection in a short time. However, it does require perfect knowledge of the primary user's signaling features, which is not obligatory for primary users, and it involves substantial power consumption. As a result, MFD is often deemed suitable for scenarios with known master user signal information and high-phase synchronization. Generally, SS approaches require prior knowledge about the PU's signal or noise power, but this prior knowledge may not be available in many cases, especially for non-cooperative communication [21]. Therefore, the literature primarily focuses on blind detection techniques, which are suitable for a diverse range of SS scenarios in dynamic environments, without requiring this information. Energy detection (ED) is a commonly used technique due to its ease of implementation [22]. It operates by measuring the energy levels of the received signal in a particular frequency band and comparing them to a predefined threshold value to determine whether the band is occupied by a primary user or not. However, energy detection suffers from noise uncertainty [23]. It is suitable for spectroscopic detection tasks in low-SNR environments but often requires combination with other signal identification and analysis techniques due to its susceptibility to false alarms and reduced sensitivity in high-noise environments. To address this issue, maximum eigenvalue detection (MED) has been proposed [24; 25], which involves calculating the eigenvalues and dynamically adapting the detection threshold based on the expected minimum energy level of the primary user signal. MED stands out for its heightened sensitivity to signal changes by focusing on detecting the largest eigenvalue. This characteristic makes it particularly effective in capturing variations in signal characteristics, enhancing its utility in scenarios where subtle changes are significant. Despite its advantages, the practical application of maximum eigenvalue detection encounters challenges in certain scenarios, such as environments characterized by high dynamics or non-linear signal behavior, where its performance may degrade. Nevertheless, this method finds utility when dealing with signals in which changes in the largest eigenvalue signify meaningful variations, providing a reliable means to discern significant signal characteristics across a range of applications. In [24], the authors compare the performance of MED and ED in different scenarios and demonstrate that MED outperforms ED in scenarios with a low SNR and high noise levels. Another widely used method is cyclostationary detection, which leverages the statistical characteristics of modulated signals to demonstrate the periodicity of detection. This method showcases more stable performance in the presence of background noise uncertainty and can operate effectively under low SNR conditions. Nevertheless, it is important to acknowledge that the performance of cyclostationary feature detection may be compromised if the received signal exhibits cyclostationary features due to background noise. Additionally, the computation time and complexity associated with Fourier transforming the cyclical autocorrelation function need to be considered when implementing this method. Nevertheless, traditional sensing methods have limitations in their ability to fully exploit the time, frequency or spatial dependencies that exist in the detected signals. As a result, their performance may be limited in certain scenarios. Researchers have delved into inventive approaches harnessing the power of machine learning.