A statistical knowledge autocorrelation based algorithm for spectrum sensing of OFDM signals in channels with frequency offset

Pat Chambers, Mathini Sellathurai

Research output: Contribution to journalArticle

1 Citation (Scopus)
15 Downloads (Pure)

Abstract

This work presents a novel autocorrelation based algorithm that uses statistical knowledge to detect orthogonal frequency division multiplexing (OFDM) signals in channels where frequency offset is present. The algorithm may be viewed as a significant improvement over other types of autocorrelation algorithm that appear in literature that lead to false alarm due to the hardware impairment of frequency offset. The algorithm works by making an unbiased estimate of the square of an autocorrelation coefficient and from that deduces an appropriate probability density function for the phase angle of the complex test statistic and thereby palliating the effect of phase distortion introduced by the frequency offset. It is shown that the algorithm presented in this work can be implemented on a testbed, as well as overcome simulations that have been specifically designed to have worst case frequency offset phase distortion conditions.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
Early online date6 Nov 2018
DOIs
Publication statusE-pub ahead of print - 6 Nov 2018

Keywords

  • autocorrelation
  • AWGN
  • cognitive radio
  • complex Gaussian distribution
  • Correlation
  • cyclical prefix
  • OFDM
  • Phase distortion
  • Probability density function
  • Sensors
  • Signal to noise ratio
  • spectrum sensing
  • test statistic
  • threshold
  • Wireless testbed

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering

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