A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Feature Learning. <s> We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Feature Learning. <s> We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Feature Learning. <s> Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. <s> BIB003 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Feature Learning. <s> Modulation recognition is a challenging task while performing spectrum sensing in cognitive radio. Recently, deep learning techniques, such as convolutional neural networks (CNNs) have been shown to achieve state-of-the-art accuracy for modulation recognition. However, CNNs are not explicitly designed to undo distortions caused by wireless channels. To improve the accuracy of CNN-based modulation recognition schemes, we propose a signal distortion correction module (CM). The proposed CM is also based on a neural network that can be thought of as an estimator of carrier frequency and phase offset introduced by the channel. The CM output is used to shift the signal frequency and phase before modulation recognition and is differentiable with respect to its weights. This allows the CM to be co-trained end-to-end in tandem with the CNN used for modulation recognition. For supervision, only the modulation scheme label is used and the knowledge of true frequency or phase offset is not required for co-training the combined network (CM+CNN). <s> BIB004
Feature learning is based on the raw data or sampling data instead of crafting of expert features. A recently proposed AMC scheme based on DL model takes advantage of CNN classifier BIB001 . The time domain IQ samples are directly fed to CNN model and suitable matched filters can be learned automatically in different SNRs. In BIB002 , the classification accuracy of CNN architectures with different sizes and depths was analyzed. The authors also provided a hybrid learning scheme, which combines CNN model and long short term memory (LSTM) network to achieve better classification performance. Reference  constructs a CNN model with 5 layers for the recognition of very high frequency signals. Simulation and actual signals are verified that the frequency offset and noise have a great impact on accuracy. Reference BIB004 proposed a novel model to obtain the estimation of the frequency offset as well as phase noise for improving accuracy rate. To further improve the performance of CNN-based recognition schemes in BIB001 , the authors present a signal distortion correction module (CM) and results show that this CM+CNN scheme achieves better accuracy than the existing schemes. In BIB003  , a heterogeneous deep model fusion (HDMF) approach is proposed, and the two different combinations between CNN and LSTM network without prior information are discussed. The timedomain data are delivered to fusion model without additional operations. The results show that the HDMF approach is an excellent alternative for a much better performance in heterogeneous networks.