Linear Channel Estimation Based on a Low-Bandwidth Observation Channel with Unknown Response

TL;DR

This paper introduces a new system identification method that estimates linear channels using a low-bandwidth measurement channel with an unknown response, leveraging a Wiener-Hammerstein scheme and a least-mean square algorithm.

Contribution

The proposed technique uniquely estimates the channel response independently of the observation channel bandwidth, enabling low-cost measurement devices.

Findings

Estimation differs from actual channel by only a scaling factor and shift

Method works with low-bandwidth, unknown-response measurement channels

Numerical examples demonstrate excellent estimation accuracy

Abstract

We propose a novel system identification technique, based on a least-mean square algorithm, allowing for the estimation of a linear channel by using an unknown-response measurement channel. The key of the technique is a memoryless nonlinear function working as uncoupling block between the estimated and observation channels, conforming a Wiener-Hammerstein scheme. We prove that this estimation, only differing from the actual channel response by a scaling factor and a temporal shift, does not depend on the observation channel bandwidth. As a consequence, this technique enables the usage of low-cost measurement devices as feedback channel. We present numerical examples of the method, supporting the proposal and displaying excellent results.