The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Wong, Lauren J.; Clark, William H.; Flowers, Bryse; Buehrer, R. Michael; Michaels, Alan J.; Headley, William C.

doi:10.48550/arxiv.2010.00432

Cited by 5 publications

(8 citation statements)

References 147 publications

(204 reference statements)

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Section: Definitionsmentioning

confidence: 99%

“…The term RFML has been used in the literature to describe any application of ML to the RF domain, including cognitive radio applications and approaches relying on classical signal processing techniques and expert-defined feature extraction [14]. However, in this work, we narrow the scope of works discussed to align with the first definition of RFML in [1] and those used in [14]. More specifically, we define RFML to be approaches, techniques, and works aimed at reducing the use of expert-defined features and the amount of prior knowledge needed for the intended RF application, and we primarily discuss DL-based works that use raw RF input.…”

Section: Definitionsmentioning

confidence: 99%

“…Even for RFML works that have successfully used TL, such limits in understanding may hinder further performance improvements that might be yielded from TL techniques. Finally, these fundamental limitations in the understanding also obscures insights into long-term model behavior during deployment, which has long been a criticism of RFML and prevented commercial support and deployment [14].…”

Section: Future Workmentioning

confidence: 99%

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

Wong

Michaels

2022

Sensors

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Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning, few have demonstrated the use of transfer learning techniques for yielding performance gains, improved generalization, or to address concerns of training data costs. With modifications to existing transfer learning taxonomies constructed to support transfer learning in other modalities, this paper presents a tailored taxonomy for radio frequency applications, yielding a consistent framework that can be used to compare and contrast existing and future works. This work offers such a taxonomy, discusses the small body of existing works in transfer learning for radio frequency machine learning, and outlines directions where future research is needed to mature the field.

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Section: Definitionsmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

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

Wong

Michaels

2022

Sensors

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“…Machine learning (ML) in general and deep learning (DL) in particular has found rich applications in various domains such as computer vision (CV) and natural language processing (NLP). Motivated by the success in those domains, there has been a growing recent interest in the development of artificial intelligence driven solutions for wireless communications using radio frequency (RF) data [1]- [3]. For example, convolutional neural network (CNN) models have been used for modulation recognition [4] and channel decoding [5].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

Adesina

Hsieh

Sagduyu

et al. 2023

IEEE Commun. Surv. Tutorials

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Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in nextgeneration wireless communications are discussed.

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“…Machine Learning (ML) in general and Deep Learning (DL) in particular has found rich applications in various domains such as Computer Vision (CV) and Natural Language Processing (NLP). Motivated by the success in those domains, there has been a growing recent interest in the development of artificial intelligence driven solutions for wireless communications using radio frequency (RF) data [1]- [3]. For example, Convolutional Neural Network (CNN) model has been used for modulation recognition [4] and channel decoding [5].…”

Section: Introductionmentioning

confidence: 99%