Joint Detection and Classification of RF Signals Using Deep Learning

Vagollari, Adela; Schram, Viktoria; Wicke, Wayan; Hirschbeck, Martin; Gerstacker, Wolfgang H.

doi:10.1109/vtc2021-spring51267.2021.9449073

Cited by 32 publications

(17 citation statements)

References 22 publications

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“…In Vagollari et al (2021), wideband spectrograms are used to perform detection, localization, and classification. Object detection is performed via YOLO adaptations using simulated datasets that cover analog and digital modulations.…”

Section: Prior Workmentioning

confidence: 99%

Large Scale Radio Frequency Wideband Signal Detection & Recognition

Boegner¹,

Vanhoy²,

Phillip³

et al. 2022

Preprint

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Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To encourage broader research with wideband operations, we introduce the WidebandSig53 (WBSig53) dataset which consists of 550 thousand synthetically-generated samples from 53 different signal classes containing approximately 2 million unique signals. We extend the TorchSig signal processing machine learning toolkit for open-source and customizable generation, augmentation, and processing of the WBSig53 dataset. We conduct experiments using state of the art (SoTA) convolutional neural networks and transformers with the WBSig53 dataset. We investigate the performance of signal detection tasks, i.e. detect the presence, time, and frequency of all signals present in the input data, as well as the performance of signal recognition tasks, where networks detect the presence, time, frequency, and modulation family of all signals present in the input data. Two main approaches to these tasks are evaluated with segmentation networks and object detection networks operating on complex input spectrograms. Finally, we conduct comparative analysis of the various approaches in terms of the networks' mean average precision, mean average recall, and the speed of inference.

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Section: Prior Workmentioning

confidence: 99%

Large Scale Radio Frequency Wideband Signal Detection & Recognition

Boegner¹,

Vanhoy²,

Phillip³

et al. 2022

Preprint

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show abstract

“…The study in [13] predominantly looked to identify LoRa signals using the YOLO neural network processing architecture. A similar piece of work, [15], also uses YOLO but extends the study to a wider range of modulations. Both [13] and [15] focused on processing pre-recorded and simulated data offline to classify the signal parameters.…”

Section: Introductionmentioning

confidence: 99%

“…A similar piece of work, [15], also uses YOLO but extends the study to a wider range of modulations. Both [13] and [15] focused on processing pre-recorded and simulated data offline to classify the signal parameters. The work in [13] also tested a single over-the-air example using a Lora bandwidth of 125 kHz and spreading factor of 9 over a range of transmitter-receiver separations.…”

Section: Introductionmentioning

confidence: 99%

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Classification of LoRa Signals With Real-Time Validation Using the Xilinx Radio Frequency System-on-Chip

2023

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This paper demonstrates a real-time LoRa Internet of Things (IoT) signal classification technique that runs on Xilinx Radio Frequency System-on-Chip (RFSoC) hardware. IoT signals are being used for wider arrays of applications and therefore awareness of their presence is important for cyber security and infrastructure protection as well as battlefield situational awareness. Within this research a dataset of LoRa waveforms is captured using the RFSoC which bounds the possible combinations of waveform parameters. Offline algorithms are tested against this data to evaluate how to extract the centre frequency, bandwidth and spreading factor. The algorithms are then adapted to run natively on the Xilinx RFSoC itself to enable real-time classification of waveforms from non-cooperative LoRa transmitters with a high degree of classification success.

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“…They tested their system over-the-air (OTA), but were only able to achieve a mAP of 0.125. In Vagollari et al [8], the authors used YOLO and FRCNN to localize signals within a spectrogram, and classify them by their modulation type. They were able to achieve an mAP of 87% and a generalized intersection over union (gIoU) of 90%.…”

Section: Introductionmentioning

confidence: 99%

Faster Region-Based CNN Spectrum Sensing and Signal Identification in Cluttered RF Environments

Morehouse¹,

Montes²,

Zhou³

2023

Preprint

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In this paper, we optimize a faster region-based convolutional neural network (FRCNN) for 1-dimensional (1D) signal processing and electromagnetic spectrum sensing. We target a cluttered radio frequency (RF) environment, where multiple RF transmission can be present at various frequencies with different bandwidths. The challenge is to accurately and quickly detect and localize each signal with minimal prior information of the signal within a band of interest. As the number of wireless devices grow, and devices become more complex from advances such as software defined radio (SDR), this task becomes increasingly difficult. It is important for sensing devices to keep up with this change, to ensure optimal spectrum usage, to monitor traffic over-the-air for security concerns, and for identifying devices in electronic warfare. Machine learning object detection has shown to be effective for spectrum sensing, however current techniques can be slow and use excessive resources. FRCNN has been applied to perform spectrum sensing using 2D spectrograms, however is unable to be applied directly to 1D signals. We optimize FRCNN to handle 1D signals, including fast Fourier transform (FFT) for spectrum sensing. Our results show that our method has better localization performance, and is faster than the 2D equivalent. Additionally, we show a use case where the modulation type of multiple uncooperative transmissions is identified. Finally, we prove our method generalizes to real world scenarios, by testing it over-the-air using SDR.

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Joint Detection and Classification of RF Signals Using Deep Learning

Cited by 32 publications

References 22 publications

Large Scale Radio Frequency Wideband Signal Detection & Recognition

Large Scale Radio Frequency Wideband Signal Detection & Recognition

Classification of LoRa Signals With Real-Time Validation Using the Xilinx Radio Frequency System-on-Chip

Faster Region-Based CNN Spectrum Sensing and Signal Identification in Cluttered RF Environments

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Joint Detection and Classification of RF Signals Using Deep Learning

Cited by 32 publications

References 22 publications

Large Scale Radio Frequency Wideband Signal Detection &amp; Recognition

Large Scale Radio Frequency Wideband Signal Detection &amp; Recognition

Classification of LoRa Signals With Real-Time Validation Using the Xilinx Radio Frequency System-on-Chip

Faster Region-Based CNN Spectrum Sensing and Signal Identification in Cluttered RF Environments

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Product

Resources

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Large Scale Radio Frequency Wideband Signal Detection & Recognition

Large Scale Radio Frequency Wideband Signal Detection & Recognition