  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.
