Novel Improvement for Nonlinear Compatibility of Least Mean Square Adaptive Algorithm

TL;DR

This paper proposes a modified LMS adaptive algorithm that incorporates nonlinear transfer functions and adaptive step control, demonstrated through optical sampling simulations to improve signal processing in nonlinear systems.

Contribution

It introduces a nonlinear adaptive filter with derivative-based inputs and adaptive step control, enhancing LMS performance for nonlinear transfer functions.

Findings

Improved LMS adaptation for nonlinear transfer functions.

Enhanced digital signal processing in optical sampling.

Effective mitigation of non-monotonic transfer effects.

Abstract

In order to improve the least mean squares (LMS) adaptation algorithm to accommodate the nonlinear transfer function, and to adjust the coefficients of adaptive filter during the actual implement of bias voltage and signal amplitude, methods are proposed and simulated to develop a nonlinear adaptive filter. The inputs to LMS are replaced by the derivatives of traditional inputs, and the step for each training iteration is adaptively controlled by the difference between target signal and actual signal. The simulation utilizes the implementation of Nyquist pulses optical sampling and works as a digital signal processing pre-compensation to reduce influence of the frequency responses on wires and devices. The simulation result shows promising improvement with the modified adaptation algorithm method in tackling Mach Zehnder modulator's non-monotonic transfer function.