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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest

Myke D M Valadão1, Diego Amoedo2, André Costa3

  • 1Center for R&D in Electronic and Information Technology (CETELI), Department of Electronics and Computing (DTEC), Federal University of Amazonas (UFAM), Manaus 69067005, Brazil.

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Summary

Cognitive radio uses cooperative spectrum sensing to dynamically allocate users. A novel system combining residual neural networks and random forest classifiers achieves 98% accuracy in detecting licensed users, even in high noise environments.

Keywords:
cognitive radiocooperative spectrum sensingresidual neural network

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Increasing demand and user allocation policies have led to spectrum overload and underutilization.
  • Cognitive radio (CR) offers dynamic spectrum allocation for unlicensed users.
  • Cooperative spectrum sensing (CSS) shows promise for improving spectrum utilization efficiency.

Purpose of the Study:

  • To propose a cooperative spectrum sensing approach using a residual neural network (ResNet) architecture.
  • To achieve high accuracy in detecting licensed users, even under high noise power density.
  • To reduce the number of cooperating unlicensed users required for effective spectrum sensing.

Main Methods:

  • Feature extraction from sensing information of individual unlicensed users.
  • Random forest classification to identify licensed user presence at each unlicensed user.
  • Fusion of information from multiple unlicensed users at a central fusion center.
  • Decision making using a ResNet model trained on fused sensing data.

Main Results:

  • Achieved high accuracy in detecting licensed users, reaching 98% correct identification.
  • Demonstrated effectiveness even with high noise power density (-134 dBm/Hz).
  • Successful detection was achieved with the cooperation of only 10 unlicensed users.

Conclusions:

  • The proposed cooperative spectrum sensing system effectively identifies licensed users in challenging noisy conditions.
  • The combination of feature extraction, random forest, and ResNet offers a robust solution for dynamic spectrum access.
  • This approach enhances spectrum efficiency by enabling reliable detection with fewer cooperating users.