# A Review of Research on Spectrum Sensing Based on Deep Learning
###### Abstract
We have summarized the review papers on spectrum sensing in recent years in Table 1, which shows the main contributions of each paper and the research focus. The table shows that the earlier literature [18] provides an overview of traditional spectrum-sensing algorithms and presents future challenges. Ref. [19] further validates the effectiveness and shortcomings of some well-established SS-CRN techniques through simulations. Refs. [20,21] focus on the application of machine learning in CSS and performance comparisons of different machine learning algorithms without further summarizing the development of deep learning in this direction. Whereas Refs. [22,23] focus on the application of deep reinforcement learning (DRL) in SS and deep neural networks in SS, the former does not further summarize the development of deep neural networks in the field. At the same time, the latter lacks an overview of the application scenarios and the main techniquesof deep learning in CSS. In conclusion, given the absence of a comprehensive review of deep learning for cooperative spectrum sensing, it is imperative to undertake this endeavor to consolidate existing knowledge, bridge the gap in the literature, and drive additional advances in this promising research area. This review fills a gap in the analysis of deep-learning-based applications in cooperative spectrum sensing (Figure 2). The contributions of the present paper are as follows:  
* Traditional approaches to radio spectrum sensing are studied, and a detailed summary of the strengths and weaknesses of each method is provided. * The applications of CNNs, long and short-term memory networks (LSTMs), combinatorial neural networks, and other types of neural networks in radio spectrum sensing \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Year** & **Main Contribution** \\ \hline A Survey of Spectrum Sensing & & Evaluating spectrum usage in multiple dimensions. It discussed the external sensing algorithms and other alternative sensing methods. \\ Applications [18] & & Several primary conventional spectrum-sensing techniques in CR are analyzed, and the effectiveness and shortcomings of some mature SS-CRN techniques are verified through simulation. \\ \hline An Overview of Cooperative Spectrum & & Comparison of the performance of three machine learning algorithms with conventional spectrum-sensing algorithms under two channels, AWGN and Rayleigh. \\ \hline An Overview of Deep Reinforcement & & It presented a comprehensive overview of state-of-the-art research in the field of DRL for SS in CR. Proposing future challenges for deep reinforcement learning in spectrum sensing. \\ \hline Machine Learning for Cooperative & & It summarized various ML-based algorithms in the CSS and DSS domains. It justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. \\ \hline Deep Neural Networks for Spectrum & 2023 & The paper describes the application of several DL algorithms to SS. The paper outlines the application of traditional ML algorithms with simple ANNs in SS The importance of RF datasets with migration learning is further discussed. \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & Outlines and compares the conventional SS approach with the CSS approach. A comprehensive overview of the application and development of CNNs, LSTMs, and other deep neural networks in SS. Further overview of the application and development of deep neural networks and deep reinforcement learning in CSS. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main contributions of recent review papers on spectrum sensing. are described. A comprehensive review of deep-learning-based spectrum-sensing algorithms for the period 2021 to 2023 is presented. * The paper concludes with a summary of the applications, current state of research, and challenges faced by deep learning in cooperative spectrum sensing.