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Evidence record 356 · automatically gathered

Meta-Transfer Learning for mmWave Beam Alignment

Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while t

Record details

Published: 1 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (1 July 2026), “Meta-Transfer Learning for mmWave Beam Alignment,” evidence record 356, https://ethics.ai/record/356 (originally published by arXiv).

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