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On the Use of the Sparse Property of the Cyclic Autocorrelation Function to Blindly Estimate the Cyclostationarity

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Published/Copyright: September 22, 2012
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Frequenz
From the journal Frequenz Volume 66 Issue 9-10

Abstract

Based on the sparse property of the Cyclic Autocorrelation Function (CAF) in the cyclic frequency domain, we propose in this paper a new blind estimator using compressed sensing, which better estimates the Cyclic Autocorrelation Function at a given delay compared to the classical non blind estimator when using the same number of samples. Two metrics are used to evaluate this estimation: the Mean Square Error of type one and that of type two (MSE1 andMSE2). The first metric compares the estimated Cyclic Autocorrelation Vector (CAV) which is a particular vector of the CAF for a fixed delayτ, with the theoretical reference vector obtained using the CAF. The second metric compares the value of the estimated cyclic frequency with the theoretical one. Simulation results show lower (MSE1 andMSE2 for the new proposed estimator compared to the classical one under the same conditions, from the simplest scenario to the more realistic one that uses a filter at the transmission side, a reception noise, and fading channel.

Keywords: cognitive radio; spectrum sensing; compressed sensing; sparsity; detection features; orthogonal matching pursuit
PACS® (2010): 84.40.Ua
Received: 2012-5-31
Published Online: 2012-9-22
Published in Print: 2012-9-20

©2012 by De Gruyter

Articles in the same Issue

  1. 10.1515/freq-2012-frontmatter9-10
  3. Editorial
  4. Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds
  5. An Optimization Algorithm for SDR Multi-Standard Systems Using Directed Hypergraphs
  6. A Flexible CSMA based MAC Protocol for Software Defined Radios
  7. A Spectrum Sensing Network for Cognitive PMSE Systems
  8. On the Use of the Sparse Property of the Cyclic Autocorrelation Function to Blindly Estimate the Cyclostationarity
  9. Extended Cyclostationary Signatures for OFDM in the Presence of Hardware Imperfections
  10. DSP-based Mitigation of RF Front-end Non-linearity in Cognitive Wideband Receivers
  11. System-Level Mitigation of Undersampling ADC Nonlinearity for High-IF Radio Receivers
https://doi.org/10.1515/freq-2012-0050
Keywords for this article
cognitive radio; spectrum sensing; compressed sensing; sparsity; detection features; orthogonal matching pursuit

Articles in the same Issue

  1. 10.1515/freq-2012-frontmatter9-10
  3. Editorial
  4. Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds
  5. An Optimization Algorithm for SDR Multi-Standard Systems Using Directed Hypergraphs
  6. A Flexible CSMA based MAC Protocol for Software Defined Radios
  7. A Spectrum Sensing Network for Cognitive PMSE Systems
  8. On the Use of the Sparse Property of the Cyclic Autocorrelation Function to Blindly Estimate the Cyclostationarity
  9. Extended Cyclostationary Signatures for OFDM in the Presence of Hardware Imperfections
  10. DSP-based Mitigation of RF Front-end Non-linearity in Cognitive Wideband Receivers
  11. System-Level Mitigation of Undersampling ADC Nonlinearity for High-IF Radio Receivers
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