A Linear Prediction Based Estimation of Signal-to-Noise Ratio in AWGN Channel

Most signal-to-noise ratio (SNR) estimation techniques
in digital communication channels derive the SNR
estimates solely from samples of the received signal after
the matched filter. They are based on symbol SNR and
assume perfect synchronization and intersymbol
interference (ISI)-free symbols. In severe channel
distortion where ISI is significant, the performance of
these estimators badly deteriorates. We propose an SNR
estimator which can operate on data samples collected at
the front-end of a receiver or at the input to the decision
device. This will relax the restrictions over channel
distortions and help extend the application of SNR
estimators beyond system monitoring. The proposed
estimator uses the characteristics of the second order
moments of the additive white Gaussian noise digital
communication channel and a linear predictor based on
the modified-covariance algorithm in estimating the SNR
value. The performance of the proposed technique is
investigated and compared with other in-service SNR
estimators in digital communication channels. The
simulated performance is also compared to the Cramér-
Rao bound as derived at the input of the decision circuit.