# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Cooperative Spectrum Sensing  
Since spectrum sensing receives the influence of channel multipath fading, shadow effects, and receiver instability in practical application scenarios, some scholars have proposed the theory of cooperative spectrum sensing. The core principle of cooperative spectrum sensing is to share the sensing information of all users, which enables more accurate detection of the spectrum state of PUs with monitoring data. When spectrum sensing is performed, all cognitive nodes will not be in a deep fading state or outside the transmitter coverage of the authorized user at the same time. Therefore, multiple cognitive nodes cooperatively sensing an authorized user's operating frequency band in a specific space at a specific time can achieve spatial diversity, thus alleviating the problems encountered in single-node spectrum sensing and improving the detection performance of spectrum sensing [37].  
Cooperative spectrum sensing has two key steps: The first step is to collect and fuse the user's local sensing data. The second step is to adjudicate the channel state, which maximizes the impact of poor-quality data on sensing, thus eliminating noisy signals and interference from malicious users.  
The performance of cooperative spectrum sensing not only depends on the sensing algorithm of a single node but also has higher requirements for the optimization of the sensing data fusion algorithm for secondary users. The data fusion algorithms for cooperative sensing are mainly classified into two types: the hard data fusion criterion (hard fusion, HF), and the soft data fusion criterion (soft fusion, SF). Comparing the two data fusion schemes, hard fusion has a better detection performance due to multiple judgments, while  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method of Spectrum Sensing** & **Advantages** & **Disadvantages** \\ \hline Energy Detection & The lowest complexity. Does not require pre-determined knowledge. & Selection of the threshold for detecting primary users. Inability to differentiate interference from primary users and noise. \\ \hline Matched Filter Detection & The probability of false detection is achieved in a short time. & It requires perfect knowledge of the PU signaling features. Large power consumption. \\ \hline Cyclostationary Feature Detection & Just need to know the channel status information. & Large amount of calculations and high complexity. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of advantages and disadvantages of three single-node spectrum-sensing methods.  
soft fusion requires a larger bandwidth as a control channel due to the global judgments made by the fusion center, which results in a larger network load [38].  
The different ways of sensing information through the interaction of each sensing node under the cooperative sensing framework are mainly categorized into centralized cooperative spectrum sensing, distributed cooperative spectrum sensing, and relay-assisted cooperative spectrum sensing, and several spectrum-sensing modes are described in detail and reviewed in the following sections.