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

SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming