Blind Identification of SIMO Wiener Systems based on Kernel Canonical Correlation Analysis

TL;DR

This paper introduces a novel kernel-based blind identification method for SIMO Wiener systems, transforming the nonlinear problem into a linear one in RKHS and iteratively estimating linear and nonlinear components.

Contribution

It proposes a new iterative algorithm combining CCA and KCCA for blind identification of SIMO Wiener systems in small data and colored signals.

Findings

Effective on systems with as few as two output channels

Operates on small data sets and colored signals

Demonstrated through simulations

Abstract

We consider the problem of blind identification and equalization of single-input multiple-output (SIMO) nonlinear channels. Specifically, the nonlinear model consists of multiple single-channel Wiener systems that are excited by a common input signal. The proposed approach is based on a well-known blind identification technique for linear SIMO systems. By transforming the output signals into a reproducing kernel Hilbert space (RKHS), a linear identification problem is obtained, which we propose to solve through an iterative procedure that alternates between canonical correlation analysis (CCA) to estimate the linear parts, and kernel canonical correlation (KCCA) to estimate the memoryless nonlinearities. The proposed algorithm is able to operate on systems with as few as two output channels, on relatively small data sets and on colored signals. Simulations are included to demonstrate the effectiveness of the proposed technique.