Low-complexity Voronoi shaping for the Gaussian channel

TL;DR

This paper introduces low-complexity Voronoi constellations with a cubic lattice and pseudo-Gray labeling, achieving significant shaping gains and improved information rates for the Gaussian channel.

Contribution

It presents a novel design of Voronoi constellations with reduced complexity and enhanced spectral efficiency using pseudo-Gray labeling and advanced estimation methods.

Findings

Shaping gains up to 1.03 dB achieved.

Finer spectral efficiency choices demonstrated.

Higher achievable information rates with error-control coding.

Abstract

Voronoi constellations (VCs) are finite sets of vectors of a coding lattice enclosed by the translated Voronoi region of a shaping lattice, which is a sublattice of the coding lattice. In conventional VCs, the shaping lattice is a scaled-up version of the coding lattice. In this paper, we design low-complexity VCs with a cubic coding lattice of up to 32 dimensions, in which pseudo-Gray labeling is applied to minimize the bit error rate. The designed VCs have considerable shaping gains of up to 1.03 dB and finer choices of spectral efficiencies in practice. A mutual information estimation method and a log-likelihood approximation method based on importance sampling for very large constellations are proposed and applied to the designed VCs. With error-control coding, the proposed VCs can have higher achievable information rates than the conventional scaled VCs because of their inherently good pseudo-Gray labeling feature, with a lower decoding complexity.