###### Abstract
While wideband sensing methods offer the potential to sense a significantly broader spectrum in comparison with narrowband sensing methods, the latter, characterized by their relatively lower sampling rates, prove more compatible with conventional, non-bulky, cost-effective equipment, including mobile phones, software-defined radios, and military radio devices. Despite the generally high performance exhibited by coherent narrowband sensing methods, such as matched filtering and waveform detection, their notable drawback lies in the necessity of possessing either partial or complete prior knowledge of the primary signal characteristics, which is frequently unavailable. Alternative narrowband sensing techniques, such as cyclostationary feature detection, covariance-based detection, etc., exhibit commendable performance even in challenging channel conditions. Nonetheless, these methods are encumbered by the drawback of high computational complexity. Energy detection is a non-coherent and straightforward narrowband sensing technique that is widely adopted in the literature. It involves subjecting a test statistic to binary hypothesis testing against a threshold value. Its popularity stems from its low computational complexity and the absence of the need for prior knowledge about primary user signals. However, it exhibits limitations in terms of reliability at low signal-to-noise ratio (SNR) values and susceptibility to noise uncertainty. Finally, it is worth mentioning that artificial intelligence (AI)-based approaches have been leveraged either as standalone methodologies or in combination with the aforementioned techniques to yield enhanced and more efficient approaches [18]. In [19], a system is introduced that incorporates convolutional neural network (CNN) and long short-term memory (LSTM) deep learning (DL) architectures. CNN extracts energy-correlation features from sensing data covariance matrices, while LSTM utilizes these features across multiple sensing periods to learn the primary user activity patterns and enhance the detection probability. Notably, this approach operates without making assumptions about signal-noise models. In [20], researchers utilize short-time Fourier transform to create time-frequency matrices from the received samples, which serve as inputs for the CNN model used in spectrum occupancy classification. Effectively, this approach transforms the problem into an image classification task. The proposed method places no constraints on primary user signals and remains robust in the presence of SNR variations. Ref. [21] presents a system using spectral correlation function outputs as the input for a CNN. This approach not only determines channel occupancy, but also identifies the types of signals occupying the channel, demonstrating robustness even in challenging channel conditions. In [22], the authors employ power spectrum as the CNN input, normalizing received signal power to mitigate noise power uncertainty. They use the Residual Neural Network model, training it with an extensive dataset of signal and noise data. Transfer learning is applied, where the network is initially trained with simulated data and then fine-tuned with real-world signals. This method performs well under colored noise and exhibits the ability to generalize when detecting unknown signals. In contrast, Ref. [23] directly employs complex-valued samples as CNN inputs, eliminating the need for feature extraction. Transfer learning techniques are integrated to address performance degradation in diverse scenarios beyond the training dataset. Likewise, in [24], raw signals act as inputs for a neural network model, which includes one-dimensional CNN, bidirectional LSTM (BiLSTM), and self-attention (SA) components. This fusion utilizes CNN for local pattern extraction, BiLSTM for capturing long- and short-term dependencies, and SA for highlighting specific features.