Autocorrelation-based decentralized sequential detection of OFDM signals in cognitive radios

Sachin Chaudhari, Visa Koivunen, H. Vincent Poor

Research output: Contribution to journalArticle

219 Scopus citations

Abstract

This paper introduces a simple and computationally efficient spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) based primary user signal using its autocorrelation coefficient. Further, it is shown that the log likelihood ratio test (LLRT) statistic is the maximum likelihood estimate of the autocorrelation coefficient in the low signal-to-noise ratio (SNR) regime. Performance of the local detector is studied for the additive white Gaussian noise (AWGN) and multipath channels using theoretical analysis. Obtained results are verified in simulation. The performance of the local detector in the face of shadowing is studied by simulations. A sequential detection (SD) scheme where many secondary users cooperate to detect the same primary user is proposed. User cooperation provides diversity gains as well as facilitates using simpler local detectors. The sequential detection reduces the delay and the amount of data needed in identification of the underutilized spectrum. The decision statistics from individual detectors are combined at the fusion center (FC). The statistical properties of the decision statistics are established. The performance of the scheme is studied through theory and validated by simulations. A comparison of the SD scheme with the Neyman-Pearson fixed sample size (FSS) test for the same false alarm and missed detection probabilities is also carried out.

Original languageEnglish (US)
Pages (from-to)2690-2700
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume57
Issue number7
DOIs
StatePublished - Jul 15 2009

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Autocorrelation coefficient
  • Cooperative detection
  • Cyclic prefix
  • Sequential detection
  • Spectrum sensing

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