Hierarchical ML Codebook Design for Extreme MIMO Beam Management
This work addresses beam management challenges for wireless communication systems with large antenna arrays, representing an incremental improvement over existing codebook methods.
The paper tackles beam management for extremely large MIMO systems in 5G/6G by proposing a machine learning-based codebook design process, resulting in an 8dB improvement in initial access and overall spectral efficiency gains compared to traditional methods.
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.