Moment-Based Spectrum Sensing under Generalized Noise Channels

Nikolaos I. Miridakis, Theodoros A. Tsiftsis, Guanghua Yang

Research output: Contribution to journalArticlepeer-review


A new spectrum sensing detector is proposed and analytically studied, when it operates under generalized noise channels. Particularly, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise modeling. The introduced detector adopts a moment-based approach, whereas it is not required to know the transmit signal and channel fading statistics (i.e., blind detection). Important performance metrics are presented in closed forms, such as the false-alarm probability, detection probability and decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach. Finally, it is demonstrated that the proposed approach outperforms the conventional energy detector in the practical case of noise uncertainty, yet introducing a comparable computational complexity.

Original languageEnglish
Article number9195447
Pages (from-to)89-93
Number of pages5
JournalIEEE Communications Letters
Issue number1
Publication statusPublished - Jan 2021


  • Blind estimation
  • cognitive radio
  • impulsive non-Gaussian noise
  • spectrum sensing

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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