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:  Instantaneous Time Features. 
1. Abstract of Cited Paper (BIB001):  This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB. 
2. Abstract of Cited Paper (BIB002):  This work presents a novel method for automatic modulation classification based on discriminative learning. The features are the ordered magnitude and phase of the received symbols at the output of the matched filter. The results using the proposed front end and support vector machines are compared to other techniques. Frequency offset is also considered and the results show that in this condition the new method significantly outperforms two cumulant-based classifiers. 
3. Abstract of Cited Paper (BIB003):  A technique for joint modulation format and bit-rate classification is proposed.The proposed technique can simultaneously enable non-data-aided SNR estimation.The technique uses asynchronous delay-tap plots with artificial neural networks.The signal classification accuracy is 99.12% and mean SNR estimation error is 0.88?dB.Due to its simplicity, it is attractive for future cognitive wireless networks. We propose a novel technique for automatic classification of modulation formats/bit-rates of digitally modulated signals as well as non-data-aided (NDA) estimation of signal-to-noise ratio (SNR) in wireless networks. The proposed technique exploits modulation format, bit-rate, and SNR sensitive features of asynchronous delay-tap plots (ADTPs) for the joint estimation of these parameters. Simulation results validate successful classification of three commonly-used modulation formats at two different bit-rates with an overall accuracy of 99.12%. Similarly, in-service estimation of SNR in the range of 0-30 dB is demonstrated with mean estimation error of 0.88 dB. The proposed technique requires low-speed asynchronous sampling of signal envelope and hence, it can enable simple and cost-effective joint modulation format/bit-rate classification and NDA SNR estimation in future wireless networks. Display Omitted 
4. Abstract of Cited Paper (BIB004):  A novel algorithm for simultaneous modulation format/bit-rate classification and non-data-aided (NDA) signal-to-noise ratio (SNR) estimation in multipath fading channels by applying deep machine learning-based pattern recognition on signals’ asynchronous delay-tap plots (ADTPs) is proposed. The results for three widely-used modulation formats at two different bit-rates demonstrate classification accuracy of 99.8%. In addition, NDA SNR estimation over a wide range of 0−30 dB is shown with mean error of 1 dB. The proposed method requires low-speed, asynchronous sampling of signal and is thus ideal for low-cost multiparameter estimation under real-world channel conditions.

Literature Review:
Generally, instantaneous time features consist of a series of parameters, namely, carrier amplitude, phase, and frequency. These parameters and the variation of them are developed to modulation classification. Nine key features are summarized in the Table 2 . In BIB001 , the authors use eight key features in the recognition procedure and compare with some manually selected suitable thresholds to classify both analogue and digital modulation signals. Unlike the previous approaches with manual threshold, machine learning has been developed to improve the performance and reduce tedious threshold operations. To choose the threshold automatically and adaptively, artificial neural networks (ANN) is employed in some literature. In BIB002 , a universal ANN was proposed for analogue as well as digital modulation types classification with nine key features employed. Although ANN has achieved success in modulation recognition, its overdependence on training sample data and easily getting into a local optimum restrict Kurtosis of the normalized instantaneous frequency the performance and application, while the support vector machines (SVM) can effectively alleviate these problems. In BIB003 , the straightforward ordered magnitude and phase were adopted by SVM classifier. The proposed method is close to the theoretical upper bound and has an advantage of easy implementation. Recently, deep learning as an extension of ANN is showing its great potential in modulation recognition  . Complex features are learned by multiple hidden layers, so the processing of input data could be simpler in deep learning schemes. A new model Long Short Term Memory (LSTM) has been applied to modulation recognition, which is suitable for processing the time series data with relatively long intervals or delays  . The time domain amplitude and phase data were employed in the model without complex feature extraction. Simulations show that the method could achieve high classification accuracy at varying SNR conditions. The flexibility of the proposed model was also validated in scenarios with variable symbol rates. Unlike the existing methods which only focus on modulation types classification, in BIB004 BIB005 , they propose a novel scheme for classifying modulation format and estimating SNR simultaneously. The asynchronous delay-tap plots are extracted as the training data and two multilayer perceptron (MLP) architectures are adopted for real-world signal recognition tasks.