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Wireless signal modulation identification method based on RF I/Q data distribution.

Lihui Wang1, Zhenjia Chen2, Yonghui Zhang1

  • 1School of Information and Communication Engineering, Hainan University, Haikou, 570228, China.

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|November 2, 2021
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Summary
This summary is machine-generated.

This study introduces a novel method for wireless signal identification using I/Q data and gradient matrices, achieving an 82.75% recognition rate for modulated signals. Model weights are adjusted to improve accuracy, even with random noise interference.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Electromagnetic spectrum detection is crucial for next-generation wireless communication.
  • Wireless signal identification is key for spectrum management.
  • Existing methods require robust feature extraction for diverse modulated signals.

Purpose of the Study:

  • To develop an effective method for identifying different modulated wireless signals.
  • To utilize I/Q data and gradient matrices for signal classification.
  • To enhance signal recognition accuracy through model weight adjustment.

Main Methods:

  • Extracting distribution features from signal I/Q data.
  • Employing a two-dimensional gradient matrix for signal characteristics.
  • Using minimum gradient cumulative distance (GCD) for classification.
  • Adjusting model weights based on confusion matrix results.

Main Results:

  • Achieved a recognition rate of 82.75% for modulated signals.
  • Recognition rate dropped to approximately 79% with random noise.
  • Model weight adjustment algorithm effectively improved recognition rates.

Conclusions:

  • The proposed method using I/Q data and gradient matrices is effective for modulated signal identification.
  • Model weight adjustment is crucial for maintaining accuracy in noisy environments.
  • This approach contributes to advanced electromagnetic spectrum detection and management.