TY - GEN
T1 - Efficient wideband spectrum sensing using MEMS acoustic resonators
AU - Guan, Junfeng
AU - Zhang, Jitian
AU - Lu, Ruochen
AU - Seo, Hyungjoo
AU - Zhou, Jin
AU - Gong, Songbin
AU - Hassanieh, Haitham
N1 - Publisher Copyright:
© 2021 by The USENIX Association.
PY - 2021
Y1 - 2021
N2 - 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 niters 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.
AB - 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 niters 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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M3 - Conference contribution
AN - SCOPUS:85106157461
T3 - Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
SP - 809
EP - 823
BT - Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
PB - USENIX Association
T2 - 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
Y2 - 12 April 2021 through 14 April 2021
ER -