TY - EJOUR
AU - Sheikhan, Mansour
AU - Sadegh, Mohsen Hatami
PY - 2024
DA - February
TI - Estimation of Signal-to-Noise Ratio in Dynamic Communication Channels Using Time-Delay Radial Basis Neural Network
T2 - Majlesi Journal of Electrical Engineering
VL - 3
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/estimation-of-signal-to-noise-ratio-in-dynamic-communication-channels-using-time-delay-radial-basis-neural-network/
DO - 10.1234/mjee.v3i2.291
N2 - 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 does not 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.
IS - 2
PB - OICC Press
KW - ANN, Signal-to-Noise Ratio Estimation, Dynamic Communication Channel, Radial Basis Neural Network, Prediction, PV panel, Solar output, GUI 
EN -