Open AccessA Linear Prediction Based Estimation of Signal‐to‐Noise Ratio in AWGN Channel
Author(s) -
Kamel Nidal S.,
Jeoti Varun
Publication year - 2007
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.07.0107.0012
Subject(s) - additive white gaussian noise , channel (broadcasting) , signal (programming language) , computer science , signal to noise ratio (imaging) , noise (video) , linear prediction , algorithm , speech recognition , electronic engineering , engineering , artificial intelligence , telecommunications , image (mathematics) , programming language
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.