Transfer learning for dynamic RF environments

Wagle, Neeti; Frew, Eric W.

doi:10.1109/acc.2012.6315333

Cited by 11 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such tasks, the knowledge from similar scenarios, e.g., frequency bands, can be leveraged to reduce the learning time and improve the prediction accuracy. For example, a transductive TL approach is proposed in [85] to adaptively learn and predict dynamic RF environments. Particularly, a spatio-temporal Gaussian process model is developed to predict the distribution of RF variations.…”

Section: Channel Estimation and Predictionmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

View full text Add to dashboard Cite

With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

show abstract

Section: Channel Estimation and Predictionmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, a third consideration, and we believe largely an open problem as we look to scale up the deployment of RFML solutions in real-world scenarios is the use of transfer learning [140], [141], where the behaviors learned and observations collected can be shared between sensors, as well as online or incremental learning, where the behaviors learned are modified over time as a function of a changing environment. For example, automated vehicles could benefit greatly from sharing their observations with the neighboring platforms while operating in a Vehicle-to-Vehicle (V2V)/Vehicle-to-Infrastructure (V2I) environment.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…This is because the feature representation may not able to linearly separate the injected signature from the environmental multipath. This can be solved either using transfer learning approaches [47] or using 2 step tranmission for authentication. To further clarify, if the feature representation of the injected signatures made in one room is termed source domain data and the representation in another room is the target domain data, we would want the features learned from the source domain to be valid in the target domain.…”

Section: Enabling Node Movementmentioning

confidence: 99%

Injecting Reliable Radio Frequency Fingerprints Using Metasurface for The Internet of Things

Rajendran

Sun

Lin

et al. 2020

Preprint

View full text Add to dashboard Cite

In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes-based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.

show abstract

Transfer learning for dynamic RF environments

Cited by 11 publications

References 13 publications

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

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

Injecting Reliable Radio Frequency Fingerprints Using Metasurface for The Internet of Things

Contact Info

Product

Resources

About