Learning Zero Constellations for Binary MOCZ in Fading Channels

TL;DR

This paper introduces two neural network-based methods for designing zero constellations in binary modulation over fading channels, improving performance over traditional constellations by jointly learning zero locations and decoding strategies.

Contribution

It presents novel neural network approaches for zero constellation design and decoding in BMOCZ, enabling better performance and generalization to fading channels.

Findings

Learned zero constellations outperform canonical Huffman BMOCZ.

NN-based decoder generalizes well to flat-fading channels.

Performance gains come with increased computational complexity.

Abstract

In this work, we propose two methods to design zero constellations for binary modulation on conjugate-reciprocal zeros (BMOCZ). In the first approach, we treat constellation design as a multi-label binary classification problem and learn the zero locations for a direct zero-testing (DiZeT) decoder. In the second approach, we introduce a neural network (NN)-based decoder and jointly learn the decoder and zero constellation parameters. We show that the NN-based decoder can directly generalize to flat-fading channels, despite being trained under additive white Gaussian noise. Furthermore, the results of numerical simulations demonstrate that learned zero constellations outperform the canonical, Huffman BMOCZ constellation, with the proposed NN-based decoder achieving large performance gain at the expense of increased computational complexity.