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Beam codebooks are a recent feature to enable high dimension multiple-inputmultiple-output in 5G. Codebooks comprised of customizable beamforming weightscan be used to transmit reference signals and aid the channel state information(CSI) acquisition process. Codebooks are also used for quantizing feedbackfollowing CSI acquisition. In this paper, we characterize the role of eachcodebook used during the beam management process and design a neural network tofind codebooks that improve overall system performance. Evaluating a codebookrequires considering the system-level dependency between the codebooks,feedback, overhead, and spectral efficiency. The proposed neural network isbuilt on translating codebook and feedback knowledge into a consistentbeamspace basis similar to a virtual channel model to generate initial accesscodebooks. This beamspace codebook algorithm is designed to directly integratewith current 5G beam management standards without changing the feedback formator requiring additional side information. Our simulations show that the neuralnetwork codebooks improve over traditional codebooks, even in dispersivesub-6GHz environments. We further use our framework to evaluate CSI feedbackformats with regard to multi-user spectral efficiency. Our results suggest thatoptimizing codebook performance can provide valuable performance improvements,but optimizing the feedback configuration is also important in sub-6GHz bands.

ML Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR