Method and apparatus for nonlinear-channel identification and estimation of nonlinear-distorted signals

Abstract

The present disclosure provides methods and apparatuses for nonlinear channel identification and estimation of the distorted signals in digital data transmission systems that include signals traversing a dispersive nonlinear channel that produces noisy distorted signals. The identification and estimation are accomplished with an amplitude-based signal expansion and are adapted for changing nonlinear distortion effects using a Least-Means Square direct solution that includes a precomputed matrix.

Description

RELATED APPLICATION DATA[0001]This application claims the benefit of U.S. provisional patent application Ser. No. 61 / 723,564 filed Nov. 7, 2012. The entire contents of the aforementioned patent application is incorporated herein by this reference.FIELD OF INVENTION[0002]The present invention relates to digital data modulation systems that include signals traversing a dispersive nonlinear channel that produces noisy distorted signals and that employ techniques for nonlinear channel identification and estimation of the distorted signals.BACKGROUND OF THE INVENTION[0003]A digital-data transmission system transmits digital data over a transmission media where recovery of the digital data is accomplished. During transmission, the digital data is conventionally converted to discrete-time constellation signals that are selected from a finite M-ary constellation alphabet. These constellation signals are subsequently converted to continuous-time transmission signals. Prior to, or during, tra...

AI Technical Summary

Benefits of technology

The invention provides a method and estimator for nonlinear-distorted signals using a series expansion of the distortion-producing nonlinearity that improves convergence and performance compared to a conventional Volterra series expansion. Additionally, the invention enables efficient initialization and adaptation of the distorted-signal estimation.

Problems solved by technology

Typically, the nonlinear element has a zero-memory nonlinearity but linear filtering prior to and after the nonlinear element produces dispersive nonlinear distortions.

However, Karam does not describe a technique for initializing and adapting the lookup memory encoder in the presence of additive noise.

Unfortunately, the preamble length for initialization of a predistortion lookup memory encoder can be excessively large.

As a result interference cancellation with the Aschbacher identification model would not be as effective as a technique that is receiver based and minimizes a mean square error die received discrete-time signal values.

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