A new approach for channel blind identification based on second order cyclostationary statistics and the group delay has been proposed. In this, two methods are proposed. In both the methods, the correction is applied to the basic phase estimate for both the poles and zeros, in the group delay domain. The basic phase estimate is derived from the spectral correlation density (SCD) of the system output. In the first method, the phase correction is based on magnitude group delay. In the second method, not only the phase correction but also an improved system magnitude estimate of better variance and frequency resolution is derived based on modified magnitude group delay. The results indicate a significant improvement in performance for both the methods. For the first method in the absence of noise, the percentage normalized mean square error is reduced by about 85% over that of the existing non-parametric method. The second method in the presence of noise (SNR=5 dB), provides a reduction of 74% over the existing non-parametric method and 57% over the existing combined parametric and non-parametric methods. © 2005 Elsevier B.V. All rights reserved.

Narasimhan, S.V.; Hazarathaiah, M.; Giridhar, P.V.S.

A new approach for channel blind identification based on second order cyclostationary statistics and the group delay has been proposed. In this, two methods are proposed. In both the methods, the correction is applied to the basic phase estimate for both the poles and zeros, in the group delay domain. The basic phase estimate is derived from the spectral correlation density (SCD) of the system output. In the first method, the phase correction is based on magnitude group delay. In the second method, not only the phase correction but also an improved system magnitude estimate of better variance and frequency resolution is derived based on modified magnitude group delay. The results indicate a significant improvement in performance for both the methods. For the first method in the absence of noise, the percentage normalized mean square error is reduced by about 85% over that of the existing non-parametric method. The second method in the presence of noise (SNR=5 dB), provides a reduction of 74% over the existing non-parametric method and 57% over the existing combined parametric and non-parametric methods. © 2005 Elsevier B.V. All rights reserved.

dc.contributor.author	Narasimhan, S.V.
dc.contributor.author	Hazarathaiah, M.
dc.contributor.author	Giridhar, P.V.S.
dc.date.accessioned	2026-02-05T11:00:18Z
dc.date.issued	Channel blind identification based on cyclostationarity and group delay
dc.description.abstract	2005
dc.identifier.citation	Signal Processing, 2005, 85, 7, pp. 1275-1286
dc.identifier.issn	1651684
dc.identifier.uri	https://doi.org/10.1016/j.sigpro.2005.01.011
dc.identifier.uri	https://idr.nitk.ac.in/handle/123456789/27920
dc.subject	Correlation theory
dc.subject	Data processing
dc.subject	Identification (control systems)
dc.subject	Natural frequencies
dc.subject	Optimization
dc.subject	Parameter estimation
dc.subject	Signal to noise ratio
dc.subject	Channel blind-identification
dc.subject	Cyclostationarity
dc.subject	Group delay functions
dc.subject	Spectral correlation density (CSD)
dc.subject	Communication channels (information theory)
dc.title	A new approach for channel blind identification based on second order cyclostationary statistics and the group delay has been proposed. In this, two methods are proposed. In both the methods, the correction is applied to the basic phase estimate for both the poles and zeros, in the group delay domain. The basic phase estimate is derived from the spectral correlation density (SCD) of the system output. In the first method, the phase correction is based on magnitude group delay. In the second method, not only the phase correction but also an improved system magnitude estimate of better variance and frequency resolution is derived based on modified magnitude group delay. The results indicate a significant improvement in performance for both the methods. For the first method in the absence of noise, the percentage normalized mean square error is reduced by about 85% over that of the existing non-parametric method. The second method in the presence of noise (SNR=5 dB), provides a reduction of 74% over the existing non-parametric method and 57% over the existing combined parametric and non-parametric methods. © 2005 Elsevier B.V. All rights reserved.

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