Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems
This addresses the problem of efficient CSI quantization for massive MIMO systems, which is crucial for wireless communication performance, though it appears to be an incremental improvement over existing methods.
The paper tackles the problem of designing beamforming codebooks for massive MIMO systems by proposing a model-free, data-driven approach that reduces codebook design to unsupervised clustering on a Grassmann manifold, which is solved using K-means clustering. This method reduces codebook size and achieves noticeably higher beamforming gains compared to existing state-of-the-art CSI quantization techniques.
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.