Practical Radio Frequency Learning for Future Wireless Communication Systems

Adesina, Damilola; Bassey, Joshua; Qian, Lijun

doi:10.1109/milcom47813.2019.9020807

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Meanwhile, within AMC transmitter imperfections are considered nuisance parameters, as the goal of AMC is to identify the modulation class, regardless of the emitter. In the case of receiver distortions [46], [126], we find that natural reception variations such as sampling rate differentials, frequency offsets, and varying SNR lead to the requirement for generalized training across each of the parameters [139].…”

Section: A Real World Considerationsmentioning

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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Section: A Real World Considerationsmentioning

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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“…Meanwhile, for applications such as AMC, transmitter imperfections are considered nuisance parameters, as the goal of AMC is to identify the modulation class, regardless of the emitter. Similarly, in the case of receiver distortions [24], [56], natural reception variations such as sampling rate differentials, frequency offsets, and varying SNR, must also be varied in the training data to encourage generalized learning [65].…”

Section: B Real-world Considerationsmentioning

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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“…For an area of research as broad as wireless communications systems, it is important to clearly show the scenarios being considered for a particular problem. In this section, we highlight three unique scenarios for RF learning as shown in Figure 7 in which the RF learning problem must be uniquely designed [19]. This is of course not an exhaustive list but we use it to highlight various possibilities and differences in RF learning.…”

Section: Rf Learning In Different Scenariosmentioning

confidence: 99%

“…In our previous work [19], we formulated a 3-class classification problem for interference identification to study Figure 1: Test accuracy for models trained using RF data at 0dB and 10dB and tested using RF data at various SNR values practical considerations for deep learning in wireless communication systems. In this work, we present a more comprehensive approach by looking at three unique scenarios for wireless communication problems namely: automatic modulation classification, type and number of users identification in a shared spectrum and spectrum monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust Deep Radio Frequency Spectrum Learning for Future Wireless Communications Systems

2020

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Engineering (OUSD(R&E)) under agreement number FA8750-15-2-0119. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Dept. of Navy or the Office of the Under Secretary of Defense for Research and Engineering (OUSD(R&E)) or the U.S. Government.

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Practical Radio Frequency Learning for Future Wireless Communication Systems

Cited by 7 publications

References 15 publications

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

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

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

Robust Deep Radio Frequency Spectrum Learning for Future Wireless Communications Systems

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