Robust Spectrum Sensing Via Probability Measure Transform

Yair Sorek, Koby Todros

Research output: Contribution to journalArticlepeer-review


In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. The proposed sensing technique, called measure-transformed covariance test (MTCT), operates by applying a transform to the probability measure of the data. The considered probability measure transform is structured by a non-negative function, called MT-function, that weights the data points. We show that proper selection of the MT-function, under the class of zero-centered spherically contoured Gaussian functions, can lead to significant mitigation of heavy-tailed noise effects on the sensing performance. Simulation studies illustrate the advantages of the proposed MTCT comparing to other robust MIMO and SIMO spectrum sensing techniques.

Original languageEnglish
Article number9449955
Pages (from-to)4023-4038
Number of pages16
JournalIEEE Transactions on Signal Processing
StatePublished - 1 Jan 2021


  • Cognitive radio
  • detection theory
  • probability measure transform
  • robust statistics
  • spectrum sensing

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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