Non-data-aided signal-to-noise-ratio estimation
2002
Non-data-aided (NDA) signal-to-noise-ratio (SNR) estimation is considered for binary
phase shift keyingsystems where the data samples are governed by a normal
mixture distribution. Inherent estimation accuracy limitations are examined via a simple, closed-form approximation to the associated
Cramer-Rao boundwhich eliminates the need for
numerical integration. The
expectation-maximization algorithmis proposed to iteratively maximize the NDA
likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.
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