Feedforward dynamic neural network technique for modeling and design of nonlinear telecommunication circuits and systems

A new technique based on neural networks is presented for dynamic modeling of nonlinear telecommunication circuits in continuous time domain. The proposed feedforward dynamic neural network (FDNN) model can be developed directly from input-output large-signal measurements or simulations, without having to rely on internal details of the circuit. New formulations are derived in order to handle the important circuit-load effects in system level simulation. The resulting model is fast and can be used with connections to other circuit models, allowing us to perform efficient high-level system simulation and design. It is observed that the proposed FDNN approach provides the best overall performance of being much faster than original detailed system simulation and much more accurate than the conventional behavioral modeling approach. Examples of feedforward dynamic modeling of amplifiers, mixer and their use in telecommunication system simulation are presented, demonstrating the increased efficiency in designing telecommunications systems using the proposed technique.