  Transfer learning (TL) techniques, which leverage prior knowledge gained from
data with different distributions to achieve higher performance and reduced
training time, are often used in computer vision (CV) and natural language
processing (NLP), but have yet to be fully utilized in the field of radio
frequency machine learning (RFML). This work systematically evaluates how radio
frequency (RF) TL behavior by examining how the training domain and task,
characterized by the transmitter/receiver hardware and channel environment,
impact RF TL performance for an example automatic modulation classification
(AMC) use-case. Through exhaustive experimentation using carefully curated
synthetic datasets with varying signal types, signal-to-noise ratios (SNRs),
and frequency offsets (FOs), generalized conclusions are drawn regarding how
best to use RF TL techniques for domain adaptation and sequential learning.
Consistent with trends identified in other modalities, results show that RF TL
performance is highly dependent on the similarity between the source and target
domains/tasks. Results also discuss the impacts of channel environment,
hardware variations, and domain/task difficulty on RF TL performance, and
compare RF TL performance using head re-training and model fine-tuning methods.
