This paper presents S3, an efficient wideband spectrum sensing system that can detect the real-time occupancy of bands in large spectrum. S3 samples the wireless spectrum below the Nyquist rate using cheap, commodity, low power analog-to-digital converters (ADCs). In contrast to existing sub-Nyquist sampling techniques, which can only work for sparsely occupied spectrum, S3 can operate correctly even in dense spectrum. This makes it ideal for practical environments with dense spectrum occupancy, which is where spectrum sensing is most useful. To do so, S3 leverages MEMS acoustic resonators that enable spike-train like filters in the RF frequency domain. These filters sparsify the spectrum while at the same time allow S3 to monitor a small fraction of bandwidth in every band.
We introduce a new structured sparse recovery algorithm that enables S3 to accurately detect the occupancy of multiple bands across a wide spectrum. We use our fabricated chip-scale MEMS spike-train filter to build a prototype of an S3 spectrum sensor using low power off-the-shelf components. Results from a testbed of 19 radios show that S3 can accurately detect the channel occupancies over a 418 MHz spectrum while sampling 8.5× below the Nyquist rate even if the spectrum is densely occupied.
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Paper:
Efficient Wideband Spectrum Sensing Using MEMS Acoustic Resonators
Junfeng Guan, Jitian Zhang, Ruochen Lu, Seo Hyungjoo, Jin Zhou, Songbin Gong, Haitham Hassanieh
NSDI’21, USENIX Symposium on Networked Systems Design and Implementation, April 2021
[PDF] [Slides]
Efficient Wideband Spectrum Sensing Using MEMS Acoustic Resonators
Junfeng Guan, Jitian Zhang, Ruochen Lu, Seo Hyungjoo, Jin Zhou, Songbin Gong, Haitham Hassanieh
GetMobile’22, ACM GetMobile: Mobile Computing and Communications, Dec. 2021
[PDF]