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A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing.

Shengliang Peng, Shujun Sun, Yu-Dong Yao

IEEE Transactions on Neural Networks and Learning Systems

|June 14, 2021

Summary

This summary is machine-generated.

This study surveys deep learning (DL) methods for automatic modulation classification in communications systems. It reviews signal representation techniques crucial for effective DL model training and performance.

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Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Modulation classification is vital for communications systems monitoring, management, and control.
Deep learning (DL) offers superior feature extraction and accuracy for modulation classification.
Effective signal representation and preprocessing are critical challenges in DL-based modulation classification.

Purpose of the Study:

To provide a comprehensive survey of state-of-the-art DL-based modulation classification algorithms.
To specifically review signal representation and data preprocessing techniques used in these algorithms.
To categorize and discuss existing DL algorithms based on their signal representation methods.

Main Methods:

Categorization of DL-based modulation classification algorithms into four groups based on signal representation (features, images, sequences, or combinations).
Review and analysis of signal representation and data preprocessing techniques within each category.
Summary and discussion of the advantages and disadvantages of each signal representation method.

Main Results:

Identified four primary categories of DL-based modulation classification based on signal representation.
Detailed review of various signal representation techniques and their impact on classification performance.
Comparative analysis of the strengths and weaknesses of different signal representation approaches.

Conclusions:

Signal representation is a key factor influencing the performance of DL-based modulation classification.
Understanding the trade-offs between different representation methods is crucial for algorithm selection and design.
This survey provides a valuable resource for researchers and practitioners in the field of wireless communications.