SNR estimation in time-varying fading channels

Ami Wiesel*, Jason Goldberg, Hagit Messer-Yaron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)841-848
Number of pages8
JournalIEEE Transactions on Communications
Volume54
Issue number5
DOIs
StatePublished - May 2006
Externally publishedYes

Keywords

  • Cramer-Rao bound (CRB)
  • Expectation-maximization (EM)
  • Maximum-likelihood (ML) estimation
  • Signal-to-noise ratio (SNR)

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