TY - JOUR
T1 - SNR estimation in time-varying fading channels
AU - Wiesel, Ami
AU - Goldberg, Jason
AU - Messer-Yaron, Hagit
PY - 2006/5
Y1 - 2006/5
N2 - Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer - Rao lower bound, where it is shown that the effect of the channel's time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.
AB - Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer - Rao lower bound, where it is shown that the effect of the channel's time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.
KW - Cramer-Rao bound (CRB)
KW - Expectation-maximization (EM)
KW - Maximum-likelihood (ML) estimation
KW - Signal-to-noise ratio (SNR)
UR - http://www.scopus.com/inward/record.url?scp=33646910483&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2006.873995
DO - 10.1109/TCOMM.2006.873995
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AN - SCOPUS:33646910483
SN - 0090-6778
VL - 54
SP - 841
EP - 848
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 5
ER -