Finding Waldo in the CBRS Band: Signal Detection and Localization in the 3.5 GHz Spectrum

Soltani, Nasim; Chaudhary, Vini; Roy, Debashri; Chowdhury, Kaushik

doi:10.1109/globecom48099.2022.10001638

Cited by 12 publications

(13 citation statements)

References 8 publications

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“…Since DL-based solutions require large quantities of data to be effective, one approach that finds broad application in the literature is that of using simulators and emulators to generate large synthetic datasets aiming to capture the different behavior of waveforms transmitted OTA under diverse channel conditions [28]. Although entirely relying upon synthetic data is sufficient to validate complex DL algorithms and demonstrate their potential in solving complex networking tasks, this approach does not necessarily transfer well to real-world applications where the input data consists of signals collected OTA [14][15][16][17][18].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Existing work on DL for spectrum classification-discussed in Section II-suffers from a number of critical issues. Most importantly, prior work has mostly relied on simulations and/or small-scale experimental datasets to evaluate performance [14][15][16][17][18], this is not without reason. Indeed, labeling real-world wideband spectrum is extremely challenging due to the coexistence of different signals in the same spectrum bands.…”

Section: Introductionmentioning

confidence: 99%

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DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning

Uvaydov¹,

Restuccia

Melodia³

2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable latency for real-time operations. In addition, spectrogram-based approaches do not achieve sufficient granularity level as they are based on object detection performed on pixels and are based on rectangular bounding boxes. For this reason, we propose a completely novel approach based on semantic spectrum segmentation, where multiple signals are simultaneously classified and localized in both time and frequency at the I/Q level. Conversely from the stateof-the-art computer vision algorithm, we add non-local blocks to combine the spatial features of signals, and thus achieve better performance. In addition, we propose a novel data generation approach where a limited set of easy-to-collect real-world wireless signals are "stitched together" to generate large-scale, wideband, and diverse datasets. Experimental results obtained on multiple testbeds (including the Arena testbed) using multiple antennas, multiple sampling frequencies, and multiple radios over the course of 3 days show that our approach classifies and localizes signals with a mean intersection over union (IOU) of 96.70% across 5 wireless protocols while performing in real-time with a latency of 2.6 ms. Moreover, we demonstrate that our approach based on non-local blocks achieves 7% more accuracy when segmenting the most challenging signals with respect to the state-of-the-art U-Net algorithm. We will release our 17 GB dataset and code.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning

Uvaydov¹,

Restuccia

Melodia³

2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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“…Classi er from a number of critical issues. Most importantly, prior art has mostly relied on simulations and/or small-scale experimental datasets to evaluate performance [92][93][94][95][96]. This is not without a reason.…”

Section: Stitchingmentioning

confidence: 99%

“…Since DL-based solutions require large quantities of data to be effective, one approach that finds broad application in the literature is that of using simulators and emulators to generate large synthetic datasets aiming to capture the different behavior of waveforms transmitted OTA under diverse channel conditions [103]. Although entirely relying upon synthetic data is sufficient to validate complex DL algorithms and demonstrate their potential in solving complex networking tasks, this approach does not necessarily transfer well to real-world applications where the input data consists of signals collected OTA [92][93][94][95][96].…”

Section: Background and Related Workmentioning

confidence: 99%

“…As mentioned before curating a supervised dataset for spectrum sensing, where the goal is to classify and localize signals in time-frequency, is very cumbersome. For this reason many works rely on utilizing simulated datasets to generate extensive and diverse datasets, however these datasets will not necessarily transfer well to OTA use [92][93][94][95][96]. This is because they do not take into account the change to the input data that may come from hardware or more realistic channel effects.…”

Section: Related Workmentioning

confidence: 99%

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Real-time spectrum sensing for inference and control

Uvaydov

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CyPA: A Cyclic Prefix Assisted DNN for Protocol Classification in Shared Spectrum

Zhang,

Krunz,

Hossain

2024

2024 International Conference on Computing, Networking and Communications (ICNC)

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Finding Waldo in the CBRS Band: Signal Detection and Localization in the 3.5 GHz Spectrum

Cited by 12 publications

References 8 publications

DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning

DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning

Real-time spectrum sensing for inference and control

CyPA: A Cyclic Prefix Assisted DNN for Protocol Classification in Shared Spectrum

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