Neural network modeling and identification of nonlinear channels with memory: algorithms, applications, and analytic models

Abstract

This paper proposes a neural network (NN) approach for modeling nonlinear channels with memory. Two main examples are given: (1) modeling digital satellite channels and (2) modeling solid-state power amplifiers (SSPAs). NN models provide good generalization performance (in terms of output signal-to-error ratio). NN modeling of digital satellite channels allows the characterization of each channel component. Neural net models represent the SSPA as a system composed of a linear complex filter followed by a nonlinear memoryless neural net followed by a linear complex filter. If the new algorithms are to be used in real systems, it is important that the algorithm designer understands their learning behavior and performance capabilities. Some simplified neural net models are analyzed in support of the simulation results. The analysis provides some theoretical basis for the usefulness of NNs for modeling satellite channels and amplifiers. The analysis of the simplified adaptive models explains the simulation results qualitatively but not quantitatively. The analysis proceeds in several steps and involves several novel ideas to avoid solving the more difficult general nonlinear problem.