Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal Stitching
CoRR(2024)
摘要
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 state-of-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
a latency of 2.6 ms. Moreover, we demonstrate that our approach based on
non-local blocks achieves 7
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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