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Title: Machine Learning Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR
Beam codebooks are a recent feature to en- able high dimension multiple-input multiple-output in 5G. Codebooks comprised of customizable beamforming weights can be used to transmit reference signals and aid the channel state information (CSI) acquisition process. Codebooks are also used for quantizing feedback follow- ing CSI measurement. In this paper, we unify the beam management stages–codebook design, beam sweeping, feed- back, and data transmission–to characterize the impact of codebooks throughout the process. We then design a neural network to find codebooks that improve the overall system performance. The proposed neural network is built on translating codebook and feedback knowledge into a consistent beamspace basis similar to a virtual channel model to generate initial access codebooks. This beamspace codebook algorithm is designed to directly integrate with current 5G beam management standards without changing the feedback format or requiring additional side infor- mation. Our simulations show that the neural network codebooks improve over traditional codebooks, even in dispersive sub-6GHz environments. We further use our framework to evaluate CSI feedback formats with regard to multi-user spectral efficiency. Our results suggest that optimizing codebook performance can provide valuable performance improvements, but optimizing the feedback configuration is also important in sub-6GHz bands.  more » « less
Award ID(s):
2147955 2153698 2225555 2433782 2435254
PAR ID:
10496958
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Wireless Communications
ISSN:
1536-1276
Page Range / eLocation ID:
1 to 16
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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