论文标题
面向星座的扰动，用于可伸缩的复杂性mimo非线性预编码
Constellation-Oriented Perturbation for Scalable-Complexity MIMO Nonlinear Precoding
论文作者
论文摘要
在本文中，提出了一种新型的非线性预码（NLP）技术，即面向星座的扰动（COP），以解决常规NLP技术固有的可伸缩性问题。 COP的基本概念是在星座域而不是符号域中应用向量扰动（VP）。通常用于常规技术。通过这种方式，COP的计算复杂性是独立于多Aantenna（即MIMO）网络的大小的。相反，它与符号星座的大小有关。通过广泛的线性变换，表明COP在星座域中具有灵活的可扩展性，以实现良好的复杂性 - 性能折衷。我们的计算机模拟表明，在小型MIMO系统中，COP可以提供与最佳VP相当的性能。此外，它在很大的MIMO中显着优于当前的次级最低VP方法（例如Leger-2 VP），同时保持较低的计算复杂性。
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.
