Measure-transformed covariance test for robust spectrum sensing

Yair Sorek, Koby Todros

Research output: Contribution to journalConference articlepeer-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 spherical Gaussian functions, can lead to significant mitigation of heavy-tailed noise effects. Simulation studies illustrate the advantages of the proposed MTCT comparing to state-of-the-art spectrum sensing techniques.

Original languageEnglish
Pages (from-to)4970-4974
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 1 Jan 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021


  • Cognitive radio
  • Detection theory
  • Probability measure transform
  • Robust statistics
  • Spectrum sensing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Measure-transformed covariance test for robust spectrum sensing'. Together they form a unique fingerprint.

Cite this