Maximum likelihood frequency estimation in multipath Rayleigh sparse fading channels

Y V Zakharov; V M Baronkin; T C Tozer

Maximum likelihood frequency estimation in multipath Rayleigh sparse fading channels

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Maximum likelihood (ML) data-aided frequency estimation in multipath Rayleigh fading channels with sparse impulse responses Is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinear transformed path periodograms. We have found the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for non-sparse multipath fading channels. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio (SNR) at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realise this improvement. The assumption of a diagonal autocorrelation matrix Is shown to affect the accuracy performance only at high SNRs which however can often be outside the SNRs of interest.

Original language	English
Title of host publication	2002 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-5, CONFERENCE PROCEEDINGS
Place of Publication	NEW YORK
Publisher	IEEE
Pages	36-40
Number of pages	5
ISBN (Print)	0-7803-7400-2
Publication status	Published - 2002
Event	IEEE International Conference on Communications - NEW YORK Duration: 28 Apr 2002 → 2 May 2002

Conference

Conference	IEEE International Conference on Communications
City	NEW YORK
Period	28/04/02 → 2/05/02

Cite this

@inproceedings{ed7b104a551646bf8f24061c81c11df4,

title = "Maximum likelihood frequency estimation in multipath Rayleigh sparse fading channels",

abstract = "Maximum likelihood (ML) data-aided frequency estimation in multipath Rayleigh fading channels with sparse impulse responses Is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinear transformed path periodograms. We have found the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for non-sparse multipath fading channels. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio (SNR) at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realise this improvement. The assumption of a diagonal autocorrelation matrix Is shown to affect the accuracy performance only at high SNRs which however can often be outside the SNRs of interest.",

author = "Zakharov, {Y V} and Baronkin, {V M} and Tozer, {T C}",

year = "2002",

language = "English",

isbn = "0-7803-7400-2",

pages = "36--40",

booktitle = "2002 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-5, CONFERENCE PROCEEDINGS",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Communications ; Conference date: 28-04-2002 Through 02-05-2002",

}

TY - GEN

T1 - Maximum likelihood frequency estimation in multipath Rayleigh sparse fading channels

AU - Zakharov, Y V

AU - Baronkin, V M

AU - Tozer, T C

PY - 2002

Y1 - 2002

N2 - Maximum likelihood (ML) data-aided frequency estimation in multipath Rayleigh fading channels with sparse impulse responses Is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinear transformed path periodograms. We have found the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for non-sparse multipath fading channels. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio (SNR) at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realise this improvement. The assumption of a diagonal autocorrelation matrix Is shown to affect the accuracy performance only at high SNRs which however can often be outside the SNRs of interest.

AB - Maximum likelihood (ML) data-aided frequency estimation in multipath Rayleigh fading channels with sparse impulse responses Is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinear transformed path periodograms. We have found the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for non-sparse multipath fading channels. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio (SNR) at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realise this improvement. The assumption of a diagonal autocorrelation matrix Is shown to affect the accuracy performance only at high SNRs which however can often be outside the SNRs of interest.

M3 - Conference contribution

SN - 0-7803-7400-2

SP - 36

EP - 40

BT - 2002 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-5, CONFERENCE PROCEEDINGS

PB - IEEE

CY - NEW YORK

T2 - IEEE International Conference on Communications

Y2 - 28 April 2002 through 2 May 2002

ER -