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Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks.

Mingxuan Li¹, Ou Li², Guangyi Liu³

¹National Digital Switching System Engineering and Technology R&D Center, Zhengzhou 450001, China. seu_lmx@foxmail.com.

Sensors (Basel, Switzerland)

|November 16, 2018

Summary

This study introduces a novel semi-supervised learning method for automatic modulation recognition using fewer labeled samples. The signal classifier generative adversarial network improves accuracy in cognitive radio domains.

Keywords:

automatic modulation recognition deep learning generative adversarial network semi-supervised

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Area of Science:

Electrical Engineering
Computer Science
Artificial Intelligence

Background:

Deep learning has accelerated automatic modulation recognition (AMR).
Current methods often require extensive labeled datasets.
Cognitive radio applications necessitate efficient AMR with limited data.

Purpose of the Study:

To develop a semi-supervised learning approach for AMR in cognitive radio.
To address the challenge of limited labeled samples in modulation recognition.
To enhance the performance of generative adversarial networks (GANs) for radio signal processing.

Main Methods:

Proposed a novel semi-supervised learning method: signal classifier generative adversarial network (SCGAN).
Enhanced the GAN architecture by incorporating an encoder network and a signal spatial transform module.
Adapted GANs for radio signal processing, overcoming limitations in computer vision applications.

Main Results:

Achieved improved classification accuracy (0.1% to 12%) on a synthetic radio frequency dataset compared to existing deep learning methods.
Demonstrated significant accuracy increases in semi-supervised scenarios compared to traditional methods.
Effectively mitigated nonconvergence and mode collapse issues inherent in complex radio signals.

Conclusions:

The proposed SCGAN offers an effective solution for AMR with limited labeled data.
The enhanced GAN architecture is well-suited for complex radio signal processing tasks.
This method advances the field of cognitive radio by enabling more efficient and accurate modulation recognition.