Frequency Spectrum Sensing by Multi-Stage Adaptive Optimization Algorithm with the Efficient Non-Cooperative Technique in Cognitive radios with hardware implementation

Document Type : Original Article

Authors

1 Imam hossein

2 PHD student of Imam Hossein University, communication and electronic collage

Abstract

Cognitive sensors, as the main part of cognitive radio systems, are the instruments which determine the spectral cavity, and thus provide optimal use of the bandwidth and prevent interference between permissible users. For reasons such as environmental noise effects, low levels of the signal, fading and multi-path phenomena, and receiver  sensitivity, the functionality of these sensors encounters many problems. In this paper, by first applying the  multi-antenna method in the receiver to obtain environmental signals and then applying the energy detector method, the detection threshold is adaptively determined with the CFAR method and the initial measurements of the environmental spectrum are achieved. The range of the spectrum where the signal is not detected is entered into the final step for decision making. In this stage, the final measurement of the spectrum is performed blindly and              non-cooperatively by finding specific values of the signal covariance matrix by the MME method, to increase the reliability in decision making and also to increase the likelihood of correct detection of the spectral cavity, in addition to preventing interference between authorized users. Simulation results show the probability of detection in the -25dB environmental SNR to be 75 %, which has improved by 15 dB compared to the benchmarks. After hardware  implementation, the simulation results are compared with the results obtained by experimental tests in the real environment.
 

Keywords

References

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Electronic and Cyber Defense
Volume 8, Issue 3 - Serial Number 31
November 2020
Pages 39-51

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History

  • Receive Date: 07 August 2019
  • Revise Date: 26 October 2019
  • Accept Date: 01 February 2020
  • Publish Date: 22 October 2020

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