Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study

Clark, William H.; Arndorfer, Vanessa; Tamir, Brook; Kim, Daniel; Vives, Cristian; Morris, Hunter; Wong, Lauren J.; Headley, William C.

doi:10.1109/milcom47813.2019.9020949

Cited by 11 publications

(15 citation statements)

References 8 publications

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“…The limited body of works include an approach for end-to-end communications presented in [33], as well as several that have explored multitask learning as a method to both improve the explainability and accuracy of models trained to perform automatic modulation classification (AMC). More specifically, in both [34,35], modulation classes are broken into subgroups, either by modulation type (i.e., linear, frequency, etc.) or in order to separate the modulation schemes that cause the most confusion (i.e., 16QAM and 64QAM); moreover, in [36], concept bottleneck models were used to provide inherent decision explanations while performing AMC via the prediction of a set of intermediate concepts defined prior to training.…”

Section: Multitask Learningmentioning

confidence: 99%

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: Multitask Learningmentioning

confidence: 99%

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

Wong

Michaels

2022

Sensors

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“…The first group of approaches provide intrinsic interpretability by using inherently more interpretable models either from the offset or extracted from a black box model [141]. Examples of such models include decision trees [25], [142], attention mechanisms [143], clustering algorithms, or linear/Bayesian classifiers [144]. While these methods are typically the most straightforward and provide the most useful model/decision explanations, inherently interpretable models are typically less expressive than black-box models such as deep NNs, and therefore do not provide the same level of performance.…”

Section: Interpretation/explanationmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

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“…RFML based approaches have aimed to replace the human intelligence and domain expertise required to identify and characterize these features using deep neural networks and advanced architectures, such as CNNs and Recurrent Neural Networks (RNNs), to both blindly and automatically identify separating features and classify signals of interest, with minimal pre-processing and less a priori knowledge [48], [52], [56], [57], [82]. Given the significant research in RFML-based modulation classification, it can be argued that AMC is one of the most mature fields in RFML, and has been deployed in real-world products [122].…”

Section: A Spectrum Sensingmentioning

confidence: 99%

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

Wong,

Clark,

Flowers

et al. 2020

Preprint

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While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study

Cited by 11 publications

References 8 publications

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

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

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

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

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