Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
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DOI：
10.1109/globecom42002.2020.9322149
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Qiyou Duan;Taejoon Kim;H. Ghauch
中科院分区：
文献类型：
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作者：
Qiyou Duan;Taejoon Kim;H. Ghauch
我们提出了一种改进的毫米波（mmWave）多输入多输出（MIMO）系统中的波束对准问题，基于一种基于机器学习的方法，称为Kolmogorov模型（KM）。与以往KM的计算复杂度不能随问题规模的变化而变化不同，本文提出了一种以离散单调优化（DMO）为中心的新方法，大大降低了KM的计算复杂度。我们还提出了一种用于高级假设检验的Kolmogorov-Smirnov （KS）准则，与为传统KM开发的频率估计（FE）方法相比，它不需要任何主观阈值设置。仿真结果证明了所提出的KM学习在毫米波MIMO波束对准中的有效性。
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 approach, called Kolmogorov model (KM). 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 MIMO beam alignment are presented.