Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems
Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems
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DOI：
10.1109/ieeeconf51394.2020.9443476
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Keerthana Bhogi;Chiranjib Saha;Harpreet S. Dhillon
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文献类型：
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作者：
Keerthana Bhogi;Chiranjib Saha;Harpreet S. Dhillon
本文重点研究了波束赋形码本的设计，使任何潜在信道分布的平均归一化波束赋形增益最大化。虽然现有技术使用统计信道模型，但我们利用无模型数据驱动的方法，以机器学习为基础，生成适应周围传播条件的波束成形码本。关键的技术贡献在于减少码本设计问题的无监督聚类问题的格拉斯曼流形上的集群质心形成有限大小的波束形成码本的信道状态信息（CSI），这可以有效地解决使用K均值聚类。这种方法被扩展到开发一个非常有效的全维（FD）多输入多输出（MIMO）系统与均匀平面阵列（UPA）天线的乘积码本设计过程。仿真结果表明，所提出的设计标准的能力，在学习的码本，减少码本的大小，并产生显着更高的波束形成增益相比，现有的国家的最先进的CSI量化技术。
This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel distribution. While the existing techniques use statistical channel models, we utilize a model-free data-driven approach with foundations in machine learning to generate beamforming codebooks that adapt to the surrounding propagation conditions. The key technical contribution lies in reducing the codebook design problem to an unsupervised clustering problem on a Grassmann manifold where the cluster centroids form the finite-sized beamforming codebook for the channel state information (CSI), which can be efficiently solved using K-means clustering. This approach is extended to develop a remarkably efficient procedure for designing product codebooks for full-dimension (FD) multiple-input multiple-output (MIMO) systems with uniform planar array (UPA) antennas. Simulation results demonstrate the capability of the proposed design criterion in learning the codebooks, reducing the codebook size and producing noticeably higher beamforming gains compared to the existing state-of-the-art CSI quantization techniques.