Estimation of Signal-to-Noise Ratio in Dynamic Communication Channels Using Time-Delay Radial Basis Neural Network.

In this paper, the signal-to-noise ratio (SNR) of dynamic communication channels, using a radial basis function (RBF) neural network with time-delay structure, is estimated. The exactitude of the estimation is sufficient for systems which are based on link adaptation techniques. The proposed system for estimating SNR doesn't demand on having any prior knowledge about transmitted symbols. This feature of the proposed model results to save system resources. This saving is one of the benefits of proposed estimator as compared to transmitted data aided (TxDA) estimators. The performance index of the system, in terms of normalized mean squared error (NMSE) criterion, is achieved less than 0.001 for practical applications. [ABSTRACT FROM AUTHOR]