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Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks.

Yanqueleth Molina-Tenorio1, Alfonso Prieto-Guerrero2, Rafael Aguilar-Gonzalez3,4

  • 1Information Science and Technology Ph.D., Metropolitan Autonomous University, Mexico City 09360, Mexico.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Cognitive radio networks (CRNs) accurately detect primary users (PUs) and spectrum holes using sample entropy and neural networks. Neural networks offer superior accuracy in identifying PU carrier frequency and bandwidth for efficient secondary user (SU) spectrum access.

Keywords:
cognitive radioscooperative sensor networksmultiband spectrum sensingneural networksradio environment mapsreal-time implementation

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Area of Science:

  • Wireless Communication Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Cognitive radio networks (CRNs) are essential for efficient spectrum utilization.
  • Accurate detection of primary users (PUs) and spectrum holes is critical for secondary users (SUs).
  • Existing methods require robust real-time spectrum monitoring capabilities.

Purpose of the Study:

  • To propose and implement a centralized CRN for real-time multiband spectrum monitoring.
  • To develop radioelectric environment maps (REMs) for spectrum gap identification.
  • To compare digital signal processing (DSP) and neural network (NN) approaches for PU detection.

Main Methods:

  • Utilized software-defined radios (SDRs) in a real wireless environment.
  • Employed sample entropy for local spectrum occupancy monitoring by SUs.
  • Centralized data processing using DSP and NN techniques for PU feature extraction (power, bandwidth, central frequency).

Main Results:

  • Both DSP and NN-based CRNs successfully located PUs and identified spectral gaps.
  • The NN-based CRN demonstrated superior accuracy in detecting PU carrier frequency and bandwidth.
  • The system effectively avoided the hidden terminal problem by providing accurate spectrum availability information.

Conclusions:

  • Neural networks provide a highly accurate method for PU detection in CRNs.
  • Centralized spectrum monitoring with NNs enhances CRN performance and spectrum efficiency.
  • The proposed system effectively maps the radioelectric environment for dynamic spectrum access.