Exact Equality of the MSEs for Two Types of Nonlinear Adaptive Systems: Saturation and Dead-Zone Types

Abstract

Adaptive signal processing systems, commonly utilized in applications such as active noise control and acoustic echo cancellation, often encompass nonlinearities due to hardware components such as loudspeakers, microphones, and amplifiers. Examining the impact of these nonlinearities on the overall performance of adaptive systems is critically important. In this study, we employ a statistical-mechanical method to investigate the behaviors of adaptive systems, each containing an unknown system with a nonlinearity in its output. We specifically address two types of nonlinearity: saturation and dead-zone types. We analyze both the dynamic and steady-state behaviors of these systems under the effect of such nonlinearities. Our findings indicate that when the saturation value is equal to the dead-zone width, the mean square errors (MSEs) in steady states are identical for both nonlinearity types. Furthermore, we derive a self-consistent equation to obtain the saturation value and dead-zone width that maximize the steady-state MSE. We theoretically clarify that these values depend on neither the step size nor the variance of background noise.

Keywords

• Adaptive filter
• adaptive signal processing
• system identification
• LMS algorithm
• saturation-type nonlinearity
• dead-zone-type nonlinearity