Transferring Learned Behaviors between Similar and Different Radios.

Area of Science:

• Radio Frequency Engineering
• Machine Learning
• Signal Processing

Background:

• Transfer learning (TL) is effective in various machine learning (ML) domains like NLP and computer vision.
• Extending TL to the radio frequency (RF) domain enhances RFML algorithms for spectrum awareness and transmission identification.
• RF applications of TL face challenges from hardware distortions and channel variations, unlike other domains.

Purpose of the Study:

• To evaluate the feasibility and performance of TL in RFML.
• To assess the transferability of learned behaviors between homogeneous and heterogeneous radios.
• To investigate TL for automatic modulation classification (AMC) and specific emitter identification (SEI) algorithms.

Main Methods:

• Utilized transfer learning techniques applied to RFML algorithms.
• Tested model transferability between radios of similar (homogeneous) and different (heterogeneous) construction.
• Employed both synthetic data and over-the-air experimental data for evaluation.

Main Results:

• Demonstrated promising performance benefits from applying TL to RFML algorithms.
• Showcased the effectiveness of TL for both AMC and SEI tasks.
• Indicated successful transfer of learned behaviors across different radio types.

Conclusions:

• Transfer learning is a feasible and beneficial technique for RFML.
• TL can improve the portability and performance of models for spectrum situational awareness and identification tasks.
• The study confirms the value of TL in overcoming RF-specific challenges.