Sparsening filter design for iterative soft-input soft-output detectors

Raquel G Machado,Andrew G Klein,Richard K Martin

doi:10.1186/1687-1499-2012-72

Abstract

A large body of research exists around the idea of channel shortening, where a prefilter is designed to reduce the effective channel impulse response to some smaller number of contiguous taps. This idea was originally conceived to reduce the complexity of Viterbi-based maximum-likelihood equalizers. Here, we consider a generalization of channel shortening which we term "channel sparsening". In this case, a prefilter is designed to reduce the effective channel to a small number of nonzero taps which do not need to be contiguous. When used in combination with belief-propagation-based maximum a posteriori (MAP) detectors, an analogous complexity reduction can be realized. We address the design aspects of sparsening filters, including several approaches to minimize the bit error rate of MAP detectors. We devote attention to the interaction of the sparsening filter and detector, and demonstrate the performance gains through simulation.

Highlights

Intersymbol inteference (ISI) caused by frequency selective channels is one of the chief impairments faced by modern, high data-rate communication receivers
We address the issue of sparsening filter design with the goal of minimizing the detector bit error rate (BER)
8 Conclusions In this work we have considered the design of sparsening filters as a way to reduce the complexity of iterative softinput soft-output maximum a posteriori (MAP) detectors

Summary

Introduction

Intersymbol inteference (ISI) caused by frequency selective channels is one of the chief impairments faced by modern, high data-rate communication receivers. A maximum a posteriori (MAP) or maximum-likelihood (ML) sequence estimator may be implemented using the Bahl-Cocke Jelinek-Raviv (BCJR) or Viterbi algorithm, respectively These optimal approaches, are exponentially complex in the number of channel coefficients, and suboptimal ISI compensation techniques are used in most applications. By designing the prefilter so that the combined response of the sparse channel and prefilter has a reduced, limited number of nonzero coefficients, the use of the BPbased detector becomes feasible in a wider range of applications. While much of the recent interest in channel shortening has been for application to multicarrier systems, the original idea of channel shortening [3] was proposed for a reduced-complexity hybrid prefilter/ML detector which bears some resemblance to the one considered here. To extend our proposed design method to adaptive implementations which can be employed in situations where the channel is unknown and/or slowly timevarying

System model

Noise coloration

Conclusions