arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1209.0537cs.ITmath.IT
收藏

流形上干涉对齐的单边预编码器设计

One-sided Precoder Designs on Manifolds for Interference Alignment

Chen Zhang, Huarui Yin, Guo Wei

摘要:

干扰对齐(IA)是最近显示的实现$K$ -用户干扰信道的最大自由度(DoF)的技术。在本文中,我们专注于预编码器的设计流形IA。通过仅在发射机侧限制优化,它将显著减轻由前向链路和反向链路之间的交替引起的开销。首先提出了多维复杂空间中的经典最速下降(SD)算法来实现可行IA。在此基础上,对Stiefel流形上的优化问题进行了改进,提出了一种基于该流形的低维SD算法。此外,为了进一步降低复杂度,引入格拉斯曼流形,推导出相应的算法,以达到完美的IA。数值仿真结果表明,流形上的算法比已有的算法具有更好的收敛性能和更高的系统容量,并实现了最大自由度。

英文摘要:

Interference alignment (IA) is a technique recently shown to achieve the maximum degrees of freedom (DoF) of $K$-user interference channel. In this paper, we focus on the precoder designs on manifolds for IA. By restricting the optimization only at the transmitters' side, it will alleviate the overhead induced by alternation between the forward and reverse links significantly. Firstly a classical steepest descent (SD) algorithm in multi-dimensional complex space is proposed to achieve feasible IA. Then we reform the optimization problem on Stiefel manifold, and propose a novel SD algorithm based on this manifold with lower dimensions. Moreover, aiming at further reducing the complexity, Grassmann manifold is introduced to derive corresponding algorithm for reaching the perfect IA. Numerical simulations show that the proposed algorithms on manifolds have better convergence performance and higher system capacity than previous methods, also achieve the maximum DoF.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑