As 6G technology research gradually matures and the IoT rapidly gains momentum, the demand for wireless spectrum has significantly increased. Spectrum sensing plays a crucial role in this context. In this section, we will focus on introducing the technology of deep spectrum sensing. Deep spectrum sensing refers to the application of deep learning techniques, and it is a critical function in CR and DSA systems, where devices need to detect and utilize available radio frequency (RF) spectrum bands opportunistically and efficiently. Traditional spectrum sensing methods often rely on signal processing techniques and statistical analysis to detect the presence of primary users or other wireless devices in a specific frequency band. Deep spectrum sensing leverages DNNs to improve the ACC and robustness of this detection process. The implementation of deep spectrum sensing requires the assistance of relevant algorithms, and different algorithms have different characteristics.