Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey.

Area of Science:

• Machine Learning
• Wireless Communications
• Signal Processing

Background:

• Transfer learning significantly enhances computer vision and natural language processing by utilizing pre-existing knowledge.
• The application of machine learning, specifically transfer learning, in radio frequency (RF) communications is underdeveloped, lacking established frameworks for comparison and advancement.
• Existing research in RF machine learning has not fully explored transfer learning's potential for performance gains, generalization, or mitigating high training data costs.

Purpose of the Study:

• To propose a novel taxonomy tailored for transfer learning applications within radio frequency machine learning.
• To establish a consistent framework for evaluating and comparing current and future research in this domain.
• To identify key research gaps and future directions for advancing transfer learning in RF communications.

Main Methods:

• Adaptation of existing transfer learning taxonomies from other fields.
• Development of a specialized taxonomy for the unique challenges and characteristics of radio frequency data.
• Review and analysis of the current landscape of transfer learning applications in RF machine learning.

Main Results:

• A new, specialized taxonomy for transfer learning in radio frequency applications has been presented.
• The existing body of work in this specific area has been critically discussed.
• Potential avenues for future research have been outlined to guide the field's progression.

Conclusions:

• The proposed taxonomy provides a foundational structure for the burgeoning field of transfer learning in RF machine learning.
• Further research is essential to fully realize the benefits of transfer learning for performance, generalization, and cost-efficiency in wireless systems.
• Standardizing the approach to transfer learning in RF communications will accelerate innovation and adoption.