# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Other Neural Networks in Spectrum Sensing
In the range of \(-20\) dB to \(-14\) dB SNR, the detection probability increased by \(0.1\), which reflects the advantages of the wavelet transform after signal preprocessing, but the detection performance at low SNR ratios still needs to be improved. In 2020, [PERSON] et al. applied an ANN to hybrid spectrum sensing (hybrid SS, HSS). In Ref. [87], in order to overcome the problem that the traditional HSS method requires the detector to extract a priori knowledge of the statistical features, an ANN-based hybrid spectrum-sensing method is proposed. The core idea of the paper is to extract the signal features through six detectors (ED, ACD, EVM, EVMM, CPSD, and GoF) to compose the test statistic input to the network for training. The experimental results show that the detection probability of the model increases with an increase in the number of detectors; the model with six detectors to extract the information has the best performance; and the detection probability of the model in the range of the SNR of \(-14\) dB to \(-10\) dB has the highest detection probability. This result shows that the more signal features that are extracted by the detector, the better the performance of the system. Deep neural networks (DNNs) are multilayer unsupervised neural networks with the overall use of a layer-by-layer pre-training mechanism that are used for their excellent scalability and multi-tasking abilities and have achieved great results in the field of spectrum sensing. In Ref. [88], [PERSON] et al. propose that in order to solve the problem of noise uncertainty and carrier frequency mismatch in the field of spectrum sensing, two spectrum-sensing schemes are proposed, using the phase-difference (PD) feature method combined with DNN. The first scheme is a DNN-PD distribution detection method based on known carrier frequencies and phase differences, while the second scheme performs the same operation based on unknown carrier frequencies. The robust properties of the PD distribution are used as inputs in the training phase of the DNN, and it is found through simulation experiments that the proposed PDD is unaffected by noise power variations in the first scheme with known CF information, which reflects the great robustness of the method. Moreover, under the second scheme, with unknown prior information, the detection probability of the proposed BPDD improves by at least \(0.1\) in the SNR range from \(-20\) dB to \(-5\) dB compared to the existing blind sensing methods of BCSD, PDVD, and PRD. This result shows that analyzing the phase-difference distribution between a noisy scrambled signal and a Gaussian noise signal is effective for blind sensing. Unlike the former, in 2023, the author [PERSON], inspired by the fact that robust alternating direction multiplier (RADMM) [89] can improve the robustness of the algorithm, proposed a robust recovery method for broadband spectral signals by combining the RADMM algorithm with DNN in [90]. To stabilize the training phase of the network, they employed numerical differential gradient computation. Training and testing were conducted on real-world signal datasets, and the experimental results showed that the proposed method had a good convergence rate at low sampling and low SNRs and had reconstruction capability for sparse broadband spectrum signals. In order to solve the target spectral domain adaptation (DA) problem in real spectrum sensing [91], [PERSON] et al. [92] introduced consistency constraints into a dual GAN to realize the TFL task, followed by the use of transfer learning (TL) [93] in the training phase to improve the training efficiency, and finally, could realize the domain alignment generation function. Their experimental results showed that the proposed CCD-GAN outperformedthe accuracy of SAGA with the same recall, which indicated that the proposed method could effectively improve the DA problem. Realizing spectrum sensing of broadband signals with a low SNR has been a major problem in this field. In order to overcome this problem, in 2023, [PERSON] et al.