论文标题
增强的光束对准毫米波MIMO系统：Kolmogorov模型
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
论文作者
论文摘要
我们对基于基于机器的基于机器学习的标准的修改（称为Kolmogorov模型（KM）的修改，对毫米波（MMWave）多输入多输出（MIMO）系统中的光束对齐问题提出了增强。与以前的KM不同，其计算复杂性与问题的大小不可扩展，提出了一种以离散单调优化（DMO）为中心的新方法，从而导致复杂性大大降低。我们还提出了高级假设检验的Kolmogorov-Smirnov（KS）标准，与为常规KM开发的频率估计（FE）方法相比，该标准不需要任何主观阈值设置。显示了拟议的KM学习对MMWave束比对的功效的仿真结果。
We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.
