论文标题
Treestep：在Per-Antenna Power约束下，树木搜索矢量扰动预编码
TreeStep: Tree Search for Vector Perturbation Precoding under per-Antenna Power Constraint
论文作者
论文摘要
向量扰动预编码（VPP）可以加快大型多用量MIMO系统的下行链路数据传输，但已知是NP-HARD。尽管在总功率约束下，文献中有几种算法的VPP算法，但在Per-Antenna功率约束下，它们不适用于VPP。本文提出了一种新颖的平行树搜索算法，在每个安特纳纳功率约束下为VPP进行了\ emph {\ textbf {treesestep}}，以使用实践计算复杂性找到了VPP问题的优质解决方案。我们表明，我们的方法可以比正则化零强迫（例如正规化零强迫）提供巨大的性能增益。 We evaluate TreeStep for several large MIMO~($16\times16$ and $24\times24$) and massive MIMO~($16\times32$ and $24\times 48$) and demonstrate that TreeStep outperforms the popular polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by achieving the extremely low BER of $10^{-6}$ at a much下snr。
Vector Perturbation Precoding (VPP) can speed up downlink data transmissions in Large and Massive Multi-User MIMO systems but is known to be NP-hard. While there are several algorithms in the literature for VPP under total power constraint, they are not applicable for VPP under per-antenna power constraint. This paper proposes a novel, parallel tree search algorithm for VPP under per-antenna power constraint, called \emph{\textbf{TreeStep}}, to find good quality solutions to the VPP problem with practical computational complexity. We show that our method can provide huge performance gain over simple linear precoding like Regularised Zero Forcing. We evaluate TreeStep for several large MIMO~($16\times16$ and $24\times24$) and massive MIMO~($16\times32$ and $24\times 48$) and demonstrate that TreeStep outperforms the popular polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by achieving the extremely low BER of $10^{-6}$ at a much lower SNR.
