Constellation-Oriented Perturbation for Scalable-Complexity MIMO Nonlinear Precoding

TL;DR

This paper introduces a constellation-oriented perturbation (COP) method for nonlinear precoding in MIMO systems, reducing complexity and maintaining high performance, especially in large-scale networks.

Contribution

The paper proposes a novel COP technique that applies vector perturbation in the constellation domain, making complexity independent of MIMO size and scalable with constellation size.

Findings

COP achieves performance comparable to optimal VP in small MIMO.

COP outperforms sub-optimal VP methods in large MIMO.

Computational complexity is significantly reduced in large MIMO systems.

Abstract

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By this means, the computational complexity of COP is made independent to the size of multi-antenna (i.e., MIMO) networks. Instead, it is related to the size of symbol constellation. Through widely linear transform, it is shown that COP has its complexity flexibly scalable in the constellation domain to achieve a good complexity-performance tradeoff. Our computer simulations show that COP can offer very comparable performance with the optimum VP in small MIMO systems. Moreover, it significantly outperforms current sub-optimum VP approaches (such as degree-2 VP) in large MIMO whilst maintaining much lower computational complexity.