Hierarchical ML Codebook Design for Extreme MIMO Beam Management
Ryan M. Dreifuerst Robert W. Heath Jr
Beam management is a strategy to unify beamforming and channel state
information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve
multiple uses in beam management including beamforming reference signals, CSI
reporting, and analog beam training. In this paper, we propose and evaluate a
machine learning-refined codebook design process for extremely large
multiple-input multiple-output (X-MIMO) systems. We propose a neural network
and beam selection strategy to design the initial access and refinement
codebooks using end-to-end learning from beamspace representations. The
algorithm, called Extreme-Beam Management (X-BM), can significantly improve the
performance of extremely large arrays as envisioned for 6G and capture
realistic wireless and physical layer aspects. Our results show an 8dB
improvement in initial access and overall effective spectral efficiency
improvements compared to traditional codebook methods.