Abstract

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover, this design requires training and test samples for each antenna placement configuration/codebook. This paper proposes a device-agnostic beam selection framework that leverages context information to propose appropriate user beams using a generic model and a post-processing unit. The generic neural network predicts the potential angles of arrival, and the post-processing unit maps these directions to beams based on the specific device's codebook. The proposed beam selection framework works well for user devices with antenna configuration/codebook unseen in the training dataset. Also, the proposed generic network has the option to be trained with a dataset mixed of samples with different antenna configurations/codebooks, which significantly eases the burden of effective model training.

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Bibtex

@misc{rezaie2022deviceagnosticbeam,
  title         = {Device-Agnostic Millimeter Wave Beam Selection using Machine Learning},
  author        = {Sajad Rezaie and João Morais and Ahmed Alkhateeb and Carles Navarro Manchón},
  year          = {2022},
  eprint        = {2211.12125},
  archivePrefix = {arXiv},
  primaryClass  = {eess.SP},
  url           = {https://arxiv.org/abs/2211.12125}
}