Line spectrum estimation from broadband power detection bits

Omar Mehanna, Nicholas D. Sidiropoulos, Efthymios Tsakonas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Line spectrum estimation from analog signal samples is a classic problem with numerous applications. However, sending analog or finely quantized signal sample streams to a fusion center is a burden in distributed sensing scenarios. Instead, it is appealing to estimate the frequency lines from a few randomly filtered broadband power measurement bits taken using a network of cheap sensors. This leads to a new problem: line spectrum estimation from inequalities. Three different techniques are proposed for this estimation task. In the first two, the autocorrelation function is first estimated nonparametrically, then a parametric method is used to estimate the sought frequencies. The third is a direct maximum likelihood (ML) parameter estimation approach that uses coordinate descent. Simulations show that the underlying frequencies can be accurately estimated using the proposed techniques, even from relatively few bits; and that the ML estimates obtained with the third technique can meet the Cramer-Rao lower bound (also derived here), when the number of sensors is sufficiently large.

Original languageEnglish (US)
Title of host publication2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013
Pages405-409
Number of pages5
DOIs
StatePublished - Oct 22 2013
Event2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013 - Darmstadt, Germany
Duration: Jun 16 2013Jun 19 2013

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Other

Other2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013
CountryGermany
CityDarmstadt
Period6/16/136/19/13

Keywords

  • Distributed spectrum sensing
  • cognitive radio
  • line spectrum estimation
  • spectral analysis

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