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Related Experiment Video

Updated: Jul 15, 2025

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Spectrum Sensing Method Based on Residual Dense Network and Attention.

Anyi Wang1, Qifeng Meng1, Mingbo Wang1

  • 1School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study introduces an improved collaborative spectrum sensing method using Residual Dense Networks and attention mechanisms. The novel approach enhances deep feature extraction, outperforming traditional methods, especially in low signal-to-noise ratio environments.

Keywords:
attention mechanismcooperative spectrum sensingresidual dense network

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Traditional Convolutional Neural Network (CNN) spectrum sensing faces challenges like gradient vanishing and limited feature extraction in deep networks.
  • Network degradation issues arise in deep network structures, hindering performance.

Purpose of the Study:

  • To propose a novel collaborative spectrum sensing method addressing gradient vanishing and feature extraction limitations.
  • To enhance spectrum sensing performance, particularly under low signal-to-noise ratio (SNR) conditions.

Main Methods:

  • Developed a collaborative spectrum sensing method integrating Residual Dense Network (RDN) and attention mechanisms.
  • Processed time-domain signal information by stacking, normalizing, constructing a 2D matrix, and mapping to grayscale images.
  • Trained the neural network using a training set of grayscale images for deep feature extraction.

Main Results:

  • The proposed method demonstrated superior spectral sensing performance compared to traditional collaborative spectrum sensing techniques.
  • Significant improvements were observed under low signal-to-noise ratio (SNR) conditions.

Conclusions:

  • The Residual Dense Network and attention-based collaborative spectrum sensing method effectively overcomes limitations of traditional CNNs.
  • The approach offers enhanced deep feature extraction and improved performance in challenging low SNR environments.