Meta-Transfer Learning for mmWave Beam Alignment

Abstract:Millimeter-wave (mmWave) beam alignment is critical for next-generation wireless systems, but existing approaches either update all parameters during adaptation or restrict updates to a subset of layers without adapting intermediate feature representations. We propose MTL-BA, a meta-transfer learning framework for multiple-input single-output (MISO) beam alignment that freezes a pre-trained convolutional backbone and meta-learns lightweight Scale-and-Shift (SS) adapters and a classifier head. Under an outdoor-to-indoor shift in DeepMIMO, MTL-BA achieves the highest Top-1 accuracy among the evaluated methods at 35 dB while updating $17\times$ fewer parameters than full fine-tuning and meta-training $6\times$ faster than MAML.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)

arXiv:2607.00860 [eess.SP]

(or arXiv:2607.00860v2 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2607.00860 Focus to learn more arXiv-issued DOI via DataCite

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