Constellations on the Sphere with Efficient Encoding-Decoding for Noncoherent Communications

TL;DR

This paper introduces Grass-Lattice, a structured Grassmannian constellation for noncoherent SIMO channels, offering efficient encoding, decoding, and competitive error performance with improved power efficiency.

Contribution

The paper presents a novel Grassmannian constellation called Grass-Lattice, enabling efficient symbol generation and decoding for noncoherent communications.

Findings

Grass-Lattice achieves error rates close to optimized unstructured constellations.

It is more power efficient than existing structured constellations.

Performance surpasses coherent pilot-based schemes.

Abstract

In this paper, we propose a new structured Grassmannian constellation for noncoherent communications over single-input multiple-output (SIMO) Rayleigh block-fading channels. The constellation, which we call Grass-Lattice, is based on a measure preserving mapping from the unit hypercube to the Grassmannian of lines. The constellation structure allows for on-the-fly symbol generation, low-complexity decoding, and simple bit-to-symbol Gray coding. Simulation results show that Grass-Lattice has symbol and bit error rate performance close to that of a numerically optimized unstructured constellation, and is more power efficient than other structured constellations proposed in the literature and a coherent pilot-based scheme.