# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Long Short-Term Memory Networks to Spectrum Sensing
#### 3.2.2 LSTM-Based Spectrum-Sensing Methods  
(1) Explorations based on different inputs  
At the 30 th Annual PIMRC Conference, the authors [PERSON] et al. proposed exploiting temporal correlations between spectrum data via LSTM networks, that is, by relating previous sensing events to current sensing information and feeding it into the LSTM. Moreover, in [77], a spectrum-sensing scheme based on an LSTM network is proposed to address the problem of considering the temporal dependence of signals in cognitive radio. The results show that the proposed scheme achieves better detection performance and classification accuracy at low SNRs compared to alternative methods. However, the training time of the proposed scheme is longer, and the execution time is slightly increased. In summary, this study provides an innovative framework for LSTM-based spectrum sensing and suggests that reducing the time consumption of RNN-LSTM networks is a direction for future research. In Ref. [78], [PERSON] et al. perform additional research based on the work presented in [77]. The innovation is to improve the performance of CR by statistically calculating the activity of PUs as an input, and spectrum sensing based on PU activity statistics (PAS-SS) is proposed to improve the sensing performance. Experiments were  
Figure 13: Relationships between internal variables of LSTM cells. conducted to compare the classification accuracy with the ANN-based hybrid spectrum-sensing algorithm and the random forest algorithm under the same SNR conditions, and the proposed method achieved a 10% accuracy improvement at low SNRs, which demonstrates the excellent performance of the proposed scheme in the low-SNR regime. The author [PERSON], in [79], accomplishes the realization of spectrum sensing through signal recognition and signal detection. The core idea of the paper is to learn both the cyclostationary features of time-discrete signals through LSTM and the original signal data, the former by classifying different signals by exhibiting different cyclostationary features. This stage is obtained using the spectral correlation function, and then the cyclic profiles of the signals are input as sequences into the LSTM. The latter learns the raw data directly. The performance of the LSTM based on the raw signal data and the method of the paper at SNR = 30 dB is compared in an experiment, showing 93.5% classification accuracy for the former and 99.8% for the latter. In summary, we can observe that LSTM exhibits greater efficiency in learning the original data. Similar to CNN, LSTM can also learn correlated features through the covariance matrix of the signal. [PERSON] et al. [80] extracted spatially relevant features through the covariance matrix after dimensional changes, then learned temporally relevant features through LSTM and combined the two for joint learning, proposing a CM-LSTM method for spectrum perception, which is able to learn the spatial features of multiple signals received by antenna arrays and the temporal features of individual signals to achieve spectrum perception. The authors conducted experiments to compare the performance of the SVM, GBM, RF, and ED algorithms at the same SNR. The results show that CM-LSTM is more accurate in the SNR range from \(-\)15 dB to \(-\)7.5 dB. This result indicates that the joint learning of spatially and temporally correlated features by LSTM improves the performance of spectrum sensing considerably. (2) Explore the combination of algorithms  
Spectrum prediction is also a vital part of spectrum sensing, and in [81], [PERSON] et al. proposed a different deep-learning-based method for spectrum prediction. This approach incorporates the introduction of an LSTM network as a key highlight, as it can handle time-series problems and memorize historical data, which is highly useful in spectrum prediction. By using the Taguchi method [82], the authors provide an efficient approach for the design of neural networks that can reduce time consumption and computational resources.