# A Review of Research on Spectrum Sensing Based on Deep Learning
###### Abstract
Over the past few years, researchers in the field of communications have employed several prominent network models for spectrum sensing, including convolutional neural networks and long short-term memory networks [12; 13]. Several published papers have demonstrated the superiority of deep-learning-based spectrum-sensing algorithms compared to traditional approaches. Figure 1: The working mode of cognitive radio system. Consequently, numerous researchers have investigated the combination of deep learning neural networks with spectrum-sensing research. For instance, in [14], scholars [PERSON] et al. considered the geodetic distances between signals as statistical features and employed deep neural networks (DNNs) for classifying the data based on these distances, achieving spectrum sensing. Additionally, [PERSON] et al. proposed a spectrum-sensing method based on residual networks (ResNets) in [15]. This method employed two-branch convolution to improve the feature extraction capabilities, resulting in superior performance compared to traditional spectrum-sensing methods. The enhanced feature extraction capabilities led to higher detection probabilities and lower bit error rates (BERs) than the traditional methods, especially at low SNR ratios. Currently, several institutions, including the University of Electronic Science and Technology, Harbin Institute of Technology, Beijing University of Posts and Telecommunications, Florida State University, and the University of Surrey, have been actively researching deep learning in the field of spectrum sensing for cognitive radio, and have made significant progress. This paper aims to provide a summary of the current research status, application scenarios, and future development directions of deep-learning-based spectrum-sensing technology. However, it is crucial to note that although the spectrum-sensing approach based on classical deep learning (DL) networks has shown promising performance, it also has certain limitations. Most of the current research focuses on the combination of deep learning and non-cooperative spectrum sensing. There are drawbacks to this approach, however. First, in non-cooperative spectrum sensing, the availability of large-scale labeled data can be limited as each device typically only has its own sensing results, leading to a lack of data sharing and collaboration. Second, in non-cooperative spectrum sensing, individual devices sense and process independently, which may not meet the computational requirements of deep learning models due to the computational power and energy constraints of the devices. There has been a growing interest among scholars to explore the application of deep learning to cooperative sensing. Cooperative sensing allows the aggregation of data from multiple devices, resulting in a larger and more diverse dataset that can be used to train deep learning models. This approach offers several advantages. First, cooperative sensing provides real-time spectrum information and environmental change data, enabling deep learning models to adapt and update rapidly to dynamic conditions. Second, it enhances the availability of observational data and multi-source information, which improves the ability of deep learning models to accurately sense and identify interfering sources. Researchers have started to investigate deep-learning-based cooperative sensing algorithms. For instance, [PERSON] et al. [16] constructed a new 2D dataset of received signals and trained three cooperative spectrum sensing (CSS) schemes using classical convolutional neural networks (CNNs) such as LeNet, AlexNet, and VGG-16. They compared the performance of CSS schemes based on AND, OR, and majority judgment, with the former achieving favorable results. On the other hand, [PERSON] et al. [17] proposed a DNN-based CSS algorithm and introduced the federated learning framework (FLF) into CSS. Their results demonstrated a detection probability of 98.78% and a false detection probability of 1% at an SNR of \(-\)15 dB.