  Deep learning (DL)-based solutions have emerged as promising candidates for
beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems.
Nevertheless, it remains challenging to seamlessly adapt these solutions to
practical deployment scenarios, typically necessitating extensive data for
fine-tuning while grappling with domain adaptation and generalization issues.
In response, we propose a novel approach combining Meta-Learning Domain
Generalization (MLDG) with novel data augmentation techniques during
fine-tuning. This approach not only accelerates adaptation to new channel
environments but also significantly reduces the data requirements for
fine-tuning, thereby enhancing the practicality and efficiency of DL-based
mMIMO systems. The proposed approach is validated by simulating the performance
of a backbone model when deployed in a new channel environment, and with
different antenna configurations, path loss, and base station height
parameters. Our proposed approach demonstrates superior zero-shot performance
compared to existing methods and also achieves near-optimal performance with
significantly fewer fine-tuning data samples.
