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Research Article
Jialiang Xu
Northeastern University
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6453-0527
License:
This work is licensed under a CC BY 4.0 License. Read Full License
This paper presents a novel modulation recognition algorithm based D-GF convolutional neural networks, named as D-GF-CNN algorithm. Firstly, a asynchronous delay sampling (ADS) technique is introduced. Via the defined ADS, the received signal is converted into asynchronous delay histogram (ADH). The ADH of different modulation signal has distinct characteristics, which provides great convenience for the neural network to identify the modulation mode. Then, the pixel point matrix of histogram is convolved with the dilated convolution kernel of the convolutional neural network, and the automatic extraction of signal features is completed so that the manual feature extraction processing can be effectively avoid. According to the optimization theory, a novel GF regularization function is given, which can improve the constraint ability of the loss function on the weight and effectively weaken the influence of network over-fitting on the modulation recognition accuracy. Theoretical analysis and simulation experiments show that the proposed algorithm can offer several advantages, such as automatically extract features, effectively prevent network over-fitting and improve recognition accuracy, etc.
Keywords
Modulation recognition, Asynchronous delay sampling, Convolutional neural networks, Dilated convolution, Regularization
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This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jialiang Xu
Northeastern University
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6453-0527
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Mar, 2021
No community comments so far
Reviewers invited
Invitations sent on 08 Mar, 2021
Review #? received
Received 08 Mar, 2021
Editor assigned
On 04 Mar, 2021
First submitted
On 04 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
This paper presents a novel modulation recognition algorithm based D-GF convolutional neural networks, named as D-GF-CNN algorithm. Firstly, a asynchronous delay sampling (ADS) technique is introduced. Via the defined ADS, the received signal is converted into asynchronous delay histogram (ADH). The ADH of different modulation signal has distinct characteristics, which provides great convenience for the neural network to identify the modulation mode. Then, the pixel point matrix of histogram is convolved with the dilated convolution kernel of the convolutional neural network, and the automatic extraction of signal features is completed so that the manual feature extraction processing can be effectively avoid. According to the optimization theory, a novel GF regularization function is given, which can improve the constraint ability of the loss function on the weight and effectively weaken the influence of network over-fitting on the modulation recognition accuracy. Theoretical analysis and simulation experiments show that the proposed algorithm can offer several advantages, such as automatically extract features, effectively prevent network over-fitting and improve recognition accuracy, etc.
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Figure 2
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Figure 11
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Figure 14
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