Signal-to-Noise Ratio Estimation

Of the many measures that characterize the performance of a communication receiver, signal-to-noise ratio (SNR) is perhaps the most fundamental in that many of the other measures directly depend on its knowledge for their evaluation. In the design of receivers for autonomous operation, it is desirable that the estimation of SNR take place with as little known information as possible regarding other system parameters such as carrier phase and frequency, order of the modulation, data symbol stream, data format, etc. While the maximum-likelihood (ML) approach to the problem will result in the highest quality estimator, as is typically the case with this approach, it results in a structure that is quite complex unless the receiver is provided with some knowledge of the data symbols typically obtained from data estimates made at the receiver (which themselves depend on knowledge of the SNR). SNR estimators of this type have been referred to in the literature as in-service estimators, and the evaluation of their performance has been considered in [1]. Since our interest here is in SNR estimation for autonomous operation, the focus of our attention will be on estimators that perform their function without any data symbol knowledge and, despite their ad hoc nature, maintain a high level of quality and robustness with respect to other system parameter variations. One such ad hoc SNR estimator that has received considerable attention in the past is the so-called split-symbol moments estimator (SSME) [2–5] that forms its SNR estimation statistic from the sum and difference of information extracted from the first and second halves of each received data symbol. Implicit in this estimation approach, as is also the case for the in-service estimators, is that the data rate and symbol timing are known or can be estimated. (Later on in the chapter we shall discuss how the SNR estimation procedure can be modified