Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation

Bibliographic Details
Title: Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation
Authors: Wong, Lauren J., McPherson, Sean, Michaels, Alan J.
Publication Year: 2022
Subject Terms: Electrical Engineering and Systems Science - Signal Processing
More Details: The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work begins this examination by evaluating the how radio frequency (RF) domain changes encourage or prevent the transfer of features learned by convolutional neural network (CNN)-based automatic modulation classifiers. Additionally, we examine existing transferability metrics, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), as a means to both select source models for RF domain adaptation and predict post-transfer accuracy without further training.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2206.08329
Accession Number: edsarx.2206.08329
Database: arXiv
More Details
Description not available.