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Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks.

Sensors (Basel, Switzerland)·2022
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Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio.

Ernesto Cadena Muñoz1, Luis Fernando Pedraza Martínez2, Cesar Augusto Hernandez3

  • 1Systems and Industrial Department, Universidad Nacional de Colombia, Bogotá 111321, Colombia.

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Summary

A new Rényi-entropy method improves mobile cognitive radio network (MCRN) performance by accurately detecting primary users (PUs) using software-defined radio. This advanced technique offers higher detection probability than traditional energy sensing for mobile communications.

Keywords:
Rényi entropymobile cognitive radio networkssoftware defined radiospectrum sensing

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Cognitive Radio Networks

Background:

  • Mobile Cognitive Radio Networks (MCRN) require reliable detection of Primary Users (PUs) to avoid interference.
  • Traditional time-domain energy detection struggles with signal variations in mobile environments.
  • Existing methods lack efficiency in distinguishing signals without prior knowledge of PU features.

Purpose of the Study:

  • To develop and evaluate a novel frequency-domain detection method for MCRNs.
  • To enhance the accuracy and efficiency of detecting Primary User signals.
  • To implement and test a practical MCRN system using Software Defined Radio (SDR).

Main Methods:

  • Utilized Rényi-entropy for frequency-domain signal analysis.
  • Implemented a Mobile Cognitive Radio Network using Software Defined Radio (SDR), GNURadio, and OpenBTS.
  • Tested the system with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) signals.
  • Compared Rényi-entropy detection against conventional energy detection.

Main Results:

  • The Rényi-entropy method successfully distinguished noise from PU signals in the frequency domain.
  • Experimental results from the SDR implementation validated theoretical and simulation findings.
  • Achieved over 96% detection probability at a 10dB Signal to Noise Ratio (SNR).
  • The detector performed effectively in Additive White Gaussian Noise (AWGN) and Rayleigh channels.

Conclusions:

  • Rényi-entropy offers superior performance over energy detection in MCRNs.
  • The proposed SDR-based system provides a practical platform for MCRN research and development.
  • The method enhances Secondary User (SU) signal detection capabilities, improving spectrum sharing efficiency.