Low Complexity Turbo-Equalization: A Clustering Approach

TL;DR

This paper presents a low complexity turbo equalization method using clustered models and piecewise linear approximations to better match nonlinear relationships, significantly improving performance while maintaining linear computational complexity.

Contribution

It introduces a novel clustered turbo equalizer that employs piecewise linear models and clustering to reduce complexity and improve accuracy in soft-input channel equalization.

Findings

Performance gap narrowed between linear MMSE and LMS-based equalizers

Computational complexity remains linear in channel memory

Clustering improves the match to nonlinear likelihood relationships

Abstract

We introduce a low complexity approach to iterative equalization and decoding, or "turbo equalization", that uses clustered models to better match the nonlinear relationship that exists between likelihood information from a channel decoder and the symbol estimates that arise in soft-input channel equalization. The introduced clustered turbo equalizer uses piecewise linear models to capture the nonlinear dependency of the linear minimum mean square error (MMSE) symbol estimate on the symbol likelihoods produced by the channel decoder and maintains a computational complexity that is only linear in the channel memory. By partitioning the space of likelihood information from the decoder, based on either hard or soft clustering, and using locally-linear adaptive equalizers within each clustered region, the performance gap between the linear MMSE equalizer and low-complexity, LMS-based linear turbo equalizers can be dramatically narrowed.