PREDICTION OF EYE-DIAGRAM PARAMETERS FROM TRANSIENT AND GAIN-FREQUENCY CHARACTERISTICS USING NEURAL NETWORK
A capability of prediction of the eye-diagram width and height with using artificial neural network (ANN) was investigated. For this purpose, were simulated more than 750 examples of telecommunication channels with different transfer functions. Eye-diagrams were composed for all examples by means of convolution of random pulse sequence and pulse response and parameters of these eye-diagrams were measured. Some ANN was learned. Their input variables were transient characteristic delay time, raise time, magnitude of voltage peak and oscillation duration as well as a gain value at the half of clock rate. For each of predicted parameters distinct ANN was chosen for different ranges of input variables. Root mean square errors of eye-diagram parameters prediction using these ANN were in the range of 2 – 4%. Correlation coefficient of predicted and known values was more then 0,98. Sufficient decreasing of computational time is achieved compare with estimation of the eye width and height using eye-diagram modeling. This method can be used for optimization of communication channel characteristics when eye-diagram parameters are the components of the goal function.
Keywords: eye-diagram, transient characteristic, gain-frequency characteristic, neural network, approximation.