A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation Recognition <s> Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, with both military and civilian applications. MC is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate classification of linear digital modulations in slowly varying flat fading channels. With unknown channel amplitude, phase and noise power at the receive-side, we derive hybrid likelihood ratio test (HLRT) and quasi-HLRT (QHLRT) -based classifiers, and discuss their performance versus computational complexity. It is shown that the QHLRT algorithm provides a low computational complexity solution, yet yielding performance close to the HLRT algorithm. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation Recognition <s> In this paper, we propose an algorithm for the classification of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a Gaussian mixture distribution, a well-known model of man-made and natural noise that appears in most radio channels. The classifier utilizes a variant of the expectation-maximization algorithm to estimate the channel and noise parameters without the aid of training symbols. With these estimates, the signal is classified using a hybrid likelihood ratio test. Results are presented which show that the proposed classifier's performance approaches that of the ideal classifier with perfect knowledge of the channel state and noise distribution. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Modulation Recognition <s> Deep neural network (DNN) has recently received much attention due to its superior performance in classifying data with complex structure. In this paper, we investigate application of DNN technique to automatic classification of modulation classes for digitally modulated signals. First, we select twenty one statistical features which exhibit good separation in empirical distributions for all modulation formats considered (i.e., BPSK, QPSK, 8PSK, 16QAM, and 64QAM). These features are extracted from the received signal samples and used as the input to the fully connected DNN with three hidden layer. The training data containing 25,000 feature vectors is generated by the computer simulation under both additive Gaussian white noise (AWGN) and Rician fading channels. Our test results show that the proposed method brings dramatic performance improvement over the existing classifier especially for high Doppler fading channels. <s> BIB003
Modulation recognition has attracted much attention in last decades; quite a few scholars have presented a variety of excellent approaches, which can be approximately separated into two categories: LB BIB001 BIB002 and FB approaches, as shown in Figure 4 . Theoretically, the LB approaches are capable of obtain optimal performance by considering all the unknown quantities for the probability density function (PDF) and are usually employed as the theoretical upper bound for the performance comparison of modulation recognition. However, such approaches are burdened with high computational complexity and are prone to mismatching when applying the theoretical system model to the actual scene. To consider the effects of frequency offset and time drift, a large number of computational operations bring about extremely high complexity in maximum likelihood-based detector BIB003 . To tackle the problem of high complexity in practice, a large number of suboptimal approaches come into being. The FB approaches usually extract certain features from the received signal; then reasonable classifiers are used to classify different modulation signals. Since the training samples are employed to train a good classifier, the robustness of FB approaches is significantly improved for various system models and channel conditions. In addition, effective features and enough training data are also significant for improving classification accuracy. To reduce the difficulty in expert-feature extraction and enhance the flexibility for modulation classification applied in different system and channel fading environment, DL is applied for self-feature learning based on the I/Q raw data or sampling data. In the following, we introduce the feature-based approaches and feature learning approaches in modulation recognition.