2020
DOI: 10.36227/techrxiv.11814696.v1
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Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

Abstract: Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed t… Show more

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Cited by 8 publications
(11 citation statements)
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“…Huang et al used compressive CNN for modulation classification [5]. Furthermore, CNN models were also used to classify the modulation types in an orthogonal frequencydivision multiplexing system, wherein the modulation classification accuracy was limited [26].…”
Section: B Deep Neural Network Methodsmentioning
confidence: 99%
“…Huang et al used compressive CNN for modulation classification [5]. Furthermore, CNN models were also used to classify the modulation types in an orthogonal frequencydivision multiplexing system, wherein the modulation classification accuracy was limited [26].…”
Section: B Deep Neural Network Methodsmentioning
confidence: 99%
“…Even in Rayleigh channel, the minimum classification accuracy still approaches to 84%, whereas the maximum value is near to 96%. [20] proposes a robust CNN-based approach which can precisely classify four types of modulation including BPSK, QPSK, 8PSK, and 16QAM in an orthogonal frequency division multiplexing (OFDM) system under presence of Phase offset (PO). In [21], CNN and LSTM have been used to solve AMC problem.…”
Section: Related Workmentioning
confidence: 99%
“…In recent years, deep learning theory has been widely used due to its effective feature extraction capability in image processing; thus, many researchers have begun to apply deep learning to modulation recognition [14] . For example, the Long Short-Term Memory network (LSTM) is directly used to identify signals [15] , or a Convolutional Neural Network (CNN) is used to identify the orthogonal and in-phase components of the signal [16] for blind recognition; however, the recognition accuracy of this method is poor at low Signal-to-Noise Ratio (SNR). Later, some researchers used deep learning theory to introduce a priori knowledge to a certain extent, conducted feature transformation on the target signal, extracted the signal constellation [17] and timefrequency energy graph [18] , and used a CNN to identify classification.…”
Section: Introductionmentioning
confidence: 99%