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Radio Frequency Machine Learning (RFML) Applications
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Radio Frequency Machine Learning (RFML) Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13976

Special Issue Editors

Dr. William Chris Headley

E-Mail Website
Guest Editor
Virginia Tech Hume Center for National Security and Technology, Virginia Tech, Blacksburg, VA 24061, USA
Interests: spectrum sensing; machine learning; digital signal processing
Prof. Dr. Alan Michaels

E-Mail Website
Guest Editor
Virginia Tech Hume Center for National Security and Technology, Virginia Tech, Blacksburg, VA 24061, USA
Interests: digital communications; satellite communications; radio frequency machine learning; digital chaos; non-traditional hardware

Special Issue Information

Dear Colleagues,

In recent years, radio frequency machine learning (RFML) has seen a massive increase in interest due to the ever-increasing capabilities of state-of-the-art deep learning technologies, especially in other modalities such as image recognition, natural language processing, etc. This is especially true for spectrum sensing (signal detection, estimation, classification, and identification) and cognitive radio (intelligent digital signal processing, reconfigurable communications, etc.) applications. While this research has shown the potential applicability of deep learning to these applications for simple scenarios/assumptions, they typically do not consider real-world issues that would impact their deployment in real systems (channel effects, interference sources, etc.).

This Special Issue aims to highlight advances in the deployment and realization of these technologies in real systems. Topics include, but are not limited to:

  • RFML solutions for realistic spectral environments/scenarios;
  • RFML deployment considerations (e.g., SWaP considerations for IoT);
  • RFML intuition improvements (increased interpretability, uncertainty/reliability metrics, etc.);
  • RFML datasets for improving training/deployment outcomes (synthetic, captures, augmented, etc.);
  • Optimized toolchains and processing approaches for RFML modalities.

Dr. William Headley
Prof. Dr. Alan Michaels
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radio frequency machine learning (RFML)
  • adversarial RFML
  • RFML datasets
  • RFML intuition
  • IoT RFML
  • RFML deployment
  • spectrum sensing
  • cognitive radio

Published Papers (5 papers)

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Research

10 pages, 1350 KiB  
Communication
Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks
by Heming Yao, Yanming Zhang, Lijun Jiang, Hong Tat Ewe and Michael Ng
Sensors 2022, 22(13), 4769; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134769 - 24 Jun 2022
Cited by 3 | Viewed by 1324
Abstract
This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow’s layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared [...] Read more.
This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow’s layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method in learning the inversion of snow parameters is validated by numerical examples. The inversion results indicate that the correlation coefficient (R2) ratio between the proposed ConvNet and conventional methods reaches 4.8, while the ratio for the root mean square error (RMSE) is only 0.18. Hence, the proposed method experiments with a novel path to improve the inversion of passive microwave remote sensing through deep learning approaches. Full article
(This article belongs to the Special Issue Radio Frequency Machine Learning (RFML) Applications)
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30 pages, 1400 KiB  
Article
Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications
by Megan O. Moore, R. Michael Buehrer and William Chris Headley
Sensors 2022, 22(13), 4706; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134706 - 22 Jun 2022
Cited by 3 | Viewed by 1415
Abstract
Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times [...] Read more.
Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization. Full article
(This article belongs to the Special Issue Radio Frequency Machine Learning (RFML) Applications)
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21 pages, 746 KiB  
Article
Considerations for Radio Frequency Fingerprinting across Multiple Frequency Channels
by Jose A. Gutierrez del Arroyo, Brett J. Borghetti and Michael A. Temple
Sensors 2022, 22(6), 2111; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062111 - 09 Mar 2022
Cited by 8 | Viewed by 2703
Abstract
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects [...] Read more.
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from MCC > 0.9 (excellent) down to MCC < 0.05 (random guess), indicating that single-channel models may not maintain performance across all channels used by the transmitter in realistic operation. We proposed a training data selection technique to create multi-channel models which outperform single-channel models, improving the cross-channel average MCC from 0.657 to 0.957 and achieving frequency channel-agnostic performance. When evaluated in the presence of noise, multi-channel discriminant analysis models showed reduced performance, but multi-channel neural networks maintained or surpassed single-channel neural network model performance, indicating additional robustness of multi-channel neural networks in the presence of noise. Full article
(This article belongs to the Special Issue Radio Frequency Machine Learning (RFML) Applications)
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14 pages, 341 KiB  
Article
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey
by Lauren J. Wong and Alan J. Michaels
Sensors 2022, 22(4), 1416; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041416 - 12 Feb 2022
Cited by 14 | Viewed by 3337
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Radio Frequency Machine Learning (RFML) Applications)
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14 pages, 1218 KiB  
Article
Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
by Ke Zang, Wenqi Wu and Wei Luo
Sensors 2021, 21(19), 6410; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196410 - 25 Sep 2021
Cited by 3 | Viewed by 2205
Abstract
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connections between neurons are redundant. The large model size hinders the deployment of [...] Read more.
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connections between neurons are redundant. The large model size hinders the deployment of deep neural networks in applications such as Internet-of-Things (IoT) networks. Therefore, reducing parameters without compromising the network performance via sparse learning is often desirable since it can alleviates the computational and storage burdens of deep learning models. In this paper, we propose a sparse learning algorithm that can directly train a sparsely connected neural network based on the statistics of weight magnitude and gradient momentum. We first used the MNIST and CIFAR10 datasets to demonstrate the effectiveness of this method. Subsequently, we applied it to RNNs with different pruning strategies on recurrent and non-recurrent connections for AMC problems. Experimental results demonstrated that the proposed method can effectively reduce the parameters of the neural networks while maintaining model performance. Moreover, we show that appropriate sparsity can further improve network generalization ability. Full article
(This article belongs to the Special Issue Radio Frequency Machine Learning (RFML) Applications)
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