You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Deep Learning Techniques in Wireless Signal Recognition 
Chapter Title:  Fourier Transform. 
1. Abstract of Cited Paper (BIB001):  The existing decision-theory based classifiers for M-ary frequency shift keying (MFSK) signals have assumed that there is some prior knowledge of the transmitted MFSK signal parameters; while the feature-based classifiers have some limitations such as that their thresholds are signal-to-noise-ratio-dependent (SNR-dependent). In this paper, we investigate some useful properties of the amplitude spectrum of MFSK signals. Using these properties as classification criteria, a fast Fourier transform based classifier (FFTC) of MFSK signals has been developed. The FFTC algorithm is practical since it only requires some reasonable knowledge of a received signal. It is found that the FFTC algorithm works well in classifying 2-FSK, 4-FSK, 8-FSK, 16-FSK, and 32-FSK signals when SNR>0dB. The FFTC algorithm also gives good estimation of the frequency deviation of the received MFSK signal. 
2. Abstract of Cited Paper (BIB002):  The future wireless communication will come up with a strict requirement on high spectral efficiency, developing novel algorithms for spectrum sensing with deep sensing capability will be more challenging. However, traditional expert feature-based spectrum sensing algorithms are lack of sufficient capability of self-learning and adaptability to unknown environments and complex cognitive tasks. To address this problem, we propose to build up a deep learning network to learn short time-frequency transformation (STFT), a basic entity of traditional spectrum sensing algorithms. Spectrum sensing based on the learning to STFT network is supposed to automatically extract features for communication signals and makes decisions for complex cognitive tasks meanwhile. The feasibility and performances of the designed learning network are verified by classifying signal modulation types in deep spectrum sensing applications. 
3. Abstract of Cited Paper (BIB003):  This paper presents a method for hybrid radar and communication signal recognition and classification based on Deep Convolutional Neural Network (DCNN) for feature extraction and classification. The main idea is to transform modulation mode into time-frequency map for recognition. To overcome the single input single output (SISO) characteristic of DCNN to output multi tags for hybrid signals, we propose a repeated selective strategy that segments the time-frequency map and classifies the selective regions by utilizing DCNN repeatedly. The experiments compare traditional methods (ANN and SVM, which can only satisfy the classification of single signals) with our method and show that the DCNN with short-time Fourier transform (STFT) performs better and more stable in single signals and achieve a classification accuracy over 92% at 0 dB and over 98% at 5 dB in hybrid signals.

Literature Review:
Fourier transform is a significant technique in signal processing and analysis. For most signals, frequency domain analysis is more convenient and intuitive than time domain, and the Fourier transform provides convenience in decomposing a function of time (a signal) into the frequencies. In BIB001 , in order to analyze the signal modulation method, discrete Fourier transform (DFT) and the calculated amplitude and phase values were employed to classify MFSK, MPSK signals. In BIB002 , a classifier adopted fast Fourier transform (FFT) has been proposed to classify the received MFSK signal. Joint time-frequency transforms play an important role in characterizing time-varying frequency information, which could compensate for the insufficiency of the onedimensional solution and process the time and frequency characteristics simultaneously. The short time-frequency transform (STFT) is known as a classical time-frequency representation and has been widely adopted in signal processing. In BIB003 , a novel learning scheme with STFT features based on DL is proposed, which could automatically extract features and do decision-making in complex missions. The performance of the proposed method is verified in the spectrum application of classifying signal modulation type. In BIB004 , the authors propose a DCNN method that transforms modulation mode to a new time-frequency map and successfully recognize the hybrid communication signals under low SNR condition.