Multidimensional CNN-LSTM Network for Automatic Modulation Classification

Wang, Na; Liu, Yunxia; Ma, Liang; Yang, Yang; Wang, Hongjun

doi:10.3390/electronics10141649

Cited by 16 publications

(12 citation statements)

References 40 publications

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“…On the other hand, [115] shows that if only applying raw IQ, CNN and LSTM will have similar performance, and the application of HOC can help with the accuracy by around 8%. Inspired by the former's research, [116] also utilizes IQ sequence and HOC as input of DNN classifier and archives a higher accuracy but finds there is still a limitation on M-QAM's classification. For the combination of sequences, discrete orthonormal Stockwell transform [102] and the IQ series are integrated as two channels and fed into a CNN, instead of using two separate NN modules ahead of a fusion module; this can save more parameters, and make the advantages of the hybrid features more intuitively.…”

Section: Multi-dimension Fusion Featuresmentioning

confidence: 99%

Wireless Signal Representation Techniques for Automatic Modulation Classification

Liu

Jin

et al. 2022

IEEE Access

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In this paper, we present a comprehensive survey and detailed comparison of techniques that have been applied to the problem of identifying the type of modulation contained within received wireless signals. Known as automatic modulation classification (AMC), the problem has been studied for many decades. AMC plays a significant role in both military and civilian scenarios and is the main step in smart receivers. Especially with the development of software-defined radios and automatic communication systems, IoT technology and the spread of 5G technology bring the number of spectrum-using equipment explosion, making the problem of scarce spectrum resources more prominent. Although AMC techniques can be optimized from the classifier's point of view, signal pre-processing also plays a critical role. Relevant data representation approaches include time-frequency analysis, cyclostationary transforms, and hybrid techniques. We provide a taxonomy of common approaches based on order and dimensionality along with an overall analysis of signal pre-processing algorithms for AMC. Furthermore, we reproduce the major existing schemes under uniform conditions, allowing an objective comparison among different methodologies. Finally, we create an open-source Python library to simulate these techniques so the results in this paper are reproducible for future research.

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Section: Multi-dimension Fusion Featuresmentioning

confidence: 99%

Wireless Signal Representation Techniques for Automatic Modulation Classification

Liu

Jin

et al. 2022

IEEE Access

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“…Wang et al [15] propose a Hierarchical Multi-feature Fusion (HMF) method which is based on a multidimensional Long Short-Term Memory (LSTM) network. The input features of the LSTM network are prepared by a multidimensional CNN module which compensates the features between those extracted by the one-dimensional and twodimensional convolutional ilters.…”

Section: Related Workmentioning

confidence: 99%

RFNet: Fast and efficient neural network for modulation classification of radio frequency signals

Chegini¹,

Shiri²,

Baniasadi³

2022

ITU J-FET

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Automatic Modulation Classification (AMC) is a well-known problem in the Radio Frequency (RF) domain. Solving this problem requires determining the modulation of an RF signal. Once the modulation is determined, the signal could be demodulated making it possible to analyse the signal for various purposes. Deep Neural Networks (DNNs) have recently proven to be successful in solving this problem efficiently. However, since deep networks consist of several layers resulting in a high number of trainable parameters, the hardware implementations of these solutions are resource-demanding. In order to address this challenge, we propose an efficient deep neural network referred to as RFNet to tackle the AMC problem efficiently. This network introduces the novel Multiscale Convolutional (MSC) layer to extract robust features in different resolutions. In addition, the network takes advantage of several Separable Convolution Blocks (SCB). These blocks employ pointwise and depth-wise convolutions to reduce network complexity. We further introduce RFNet+ and RFNet++ as extensions of RFNet with fewer number of parameters. These variants include fewer floating-point operations and hence a lower hardware implementation cost. Experimental results using the challenging RadioML 2018 dataset show that RFNet-32++ achieves an average classification accuracy of 56.09% over all Signal-to-Noise Ratios (SNRs) and an accuracy of 92.21% in+20dB SNR using only 3.1K parameters. The small number of parameters makes the RFNet family a promising solution for future AMC systems.

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“…Their method finally achieves good robust performance on signals' parameters [12]. Some other methods are committed to improving the structure of networks for improving the recognition accuracy [13][14][15]. Lin et al propose a deep residual shrinkage attention network for signal recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, better performance than either network alone can be achieved by combining the long-and short-term memory network (LSTM) and CNN. The combined network fuses timedomain features with time-frequency domain features, providing some improvement in the performance for signal recognition at low SNRs [14,15].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

Tao

Xiao

Gong

et al. 2022

Wireless Communications and Mobile Computing

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Feature extraction and recognition of signals are the bases of a cognitive radio. Traditional manual extraction for signals’ features becomes difficult in the complex electromagnetic environment. Although convolutional neural networks (CNNs) can extract signal features automatically, they have low accuracy in recognizing electromagnetic signals at low signal-to-noise ratios (SNRs) due to the agility of signals. Considering the great potential of spiking neural networks (SNNs) in classification, a spiking convolution neural network (SCNN) for the recognition of electromagnetic signals is proposed in this paper. The SCNN effectively integrates the extraction ability of spatial features in CNNs and temporal features in SNNs. Since the SCNN is difficult to train, the strategy of surrogate gradient is proposed to train it. By taking the 2-dimensional time-frequency distribution of 6 signals as input, the SCNN can effectively identify different signals at low SNRs. The method proposed in this paper contributes to promote the research and application of SNNs in the recognition of electromagnetic signals.

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Multidimensional CNN-LSTM Network for Automatic Modulation Classification

Cited by 16 publications

References 40 publications

Wireless Signal Representation Techniques for Automatic Modulation Classification

Wireless Signal Representation Techniques for Automatic Modulation Classification

RFNet: Fast and efficient neural network for modulation classification of radio frequency signals

Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

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