Meta-Transfer Learning for mmWave Beam Alignment

Excerpt

arXiv:2607.00860v2 Announce Type: replace-cross 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 lightweig