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Title: Hierarchical ML Codebook Design for Extreme MIMO Beam Management
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.  more » « less
Award ID(s):
2225555 2147955 2153698 2433782 2435254
PAR ID:
10516355
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Machine Learning in Communications and Networking
Volume:
2
ISSN:
2831-316X
Page Range / eLocation ID:
688 to 702
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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