# A Review of Research on Spectrum Sensing Based on Deep Learning
###### Abstract
He pointed out that the key to cognitive radio lies in the system's ability to comprehensively recognize, analyze, learn, and judge all kinds of information in the external radio environment. The system can communicate intelligently with other cognitive devices to realize a dynamic spectrum allocation policy to improve the efficiency of the frequency spectrum and achieve reliable communication. The cognitive radio system consists of four main steps, i.e., spectrum sensing, spectrum analysis, spectrum judgment, and spectrum reconstruction. The working mode of the cognitive radio system is shown in Figure 1 below. Among the four steps, spectrum sensing (SS) is one of the essential components of cognitive radio, which is the core technology and prerequisite for realizing CR applications and constructing cognitive radio networks [6] and is also the focus of this paper. Spectrum-sensing technology enhances spectrum use efficiency by continuously monitoring and analyzing the radio spectrum. It promptly identifies and utilizes unoccupied frequency bands when pre-allocated or statically assigned frequencies are unavailable, meeting the demand for spectrum resources. Radio spectrum sensing has a wide range of promising applications and has been the subject of significant research efforts. In the future, it is foreseeable that this technology will be widely used in communications, radar, unmanned aerial vehicles, intelligent transportation, and other fields. In the field of communication, spectrum sensing can be used for spectrum management and resource allocation in communication systems. SS can monitor the utilization of the radio spectrum to detect which primary user bands are free, for better spectrum resource allocation and dynamic spectrum sharing. In the field of radar, spectrum-sensing techniques can monitor and analyze the use of the radar spectrum to achieve optimal utilization and management of the spectrum for radar defense against jamming [7]. In the field of unmanned aerial vehicles (UAVs),spectrum sensing can provide spectrum information for UAV communication and navigation. By sensing the surrounding spectral environment and information in real time, UAVs can select the optimal communication bands and avoid interfering sources. In addition, spectrum sensing can also help UAVs perform environment sensing and obstacle avoidance, improving safety and intelligence [8]. In the field of smart transportation, this technology can monitor the spectrum used by various wireless devices and communication systems in transportation networks and perform dynamic spectrum management to ensure communication quality and reliability for various applications [9]. As can be seen, the development of spectrum-sensing technology is of great strategic importance. After decades of development, cognitive radio technology and its spectrum-sensing methods can be broadly categorized into two types: single-node spectrum sensing and cooperative spectrum sensing [10]. Single-node spectrum sensing involves independent judgment by a single user, which does not require a complex system structure or data fusion. It provides a relatively simple sensing process but also faces challenges in breaking through the limitations imposed by physical constraints. On the other hand, cooperative spectrum sensing treats multiple sub-users as a group of cooperative entities that share sensing information. This approach aims to achieve more accurate frequency usage detection for primary users (PUs). However, it still encounters several practical challenges, including coordination, energy consumption, latency, security, and participation issues. In recent years, artificial intelligence has emerged as a popular technology worldwide, and the development of deep learning has introduced new ideas and methods for spectrum-sensing research. Deep learning excels in nonlinear modeling and adaptive learning, significantly boosting spectrum sensing's detection performance and speed. This, in turn, facilitates more efficient utilization of spectrum resources [11].