# A Review of Research on Spectrum Sensing Based on Deep Learning
###### Abstract  
In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions. cognitive radio; spectrum sensing; wireless communication; cooperative spectrum sensing 20231  
future demand, and now it can only meet about half of the demand [3]. To summarize, the shortage in electromagnetic spectrum resources, as a key problem of wireless mobile communication nowadays, will restrict the healthy development of wireless communication and become a bottleneck for developing wireless communication technology in the future if not solved effectively. At present, spectrum resources are managed and allocated by governments worldwide, such as in the United States, where spectrum management and allocation are under the Federal Communications Commission, and in China, where the National Radio Administration manages the spectrum. Moreover, the allocation method is generally \"static allocation\". The available spectrum is divided into multiple non-overlapping parts allocated to different users, the users of different frequency bands are called authorized users, and the frequency band is called the authorized frequency band. Although countries have made a rational allocation of the spectrum, after decades of development, it can be pointed out that the utilization rate of each frequency band at different times and in different geographical locations is disappointing. A study by the Federal Communications Commission of the United States found that, among the licensed frequency bands available and in use, spectrum utilization, especially in the hundreds of megabits to 3 GHz band, where the demand for frequencies is very tight, is very low, with the utilization rate of the licensed frequency bands fluctuating between 15 and 85 percent. Only a few frequency bands have high utilization rates, and the vast majority of frequency bands only have low utilization rates. The utilization rate in some regions is only 5%, so the utilization of the spectrum is extremely unbalanced. At present, the global spectrum resources below 1 GHz have been distributed to very few, and the remaining available frequency bands for wireless communication are also quite limited and cannot satisfy the expansion of new services. How to realize the dynamic management of the spectrum and spectrum sharing mechanisms to improve the efficiency of spectrum utilization in each period and each region has become an urgent problem to be solved [4]. Cognitive radio (CR) is a technology that can improve spectrum utilization, and efficient and accurate spectrum detection is the key to its implementation. The concept of cognitive radio was proposed by Dr. [PERSON], the \"father of software radio\", in his doctoral dissertation in 1999 [5].