Efficient Wideband Spectrum Sensing Using MEMS Acoustic Resonators

This paper presents S^3, an efficient wideband spectrum sensing system that can detect the real-time occupancy of bands in large spectrum. S^3 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, S^3 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, S^3 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 S^3 to monitor a small fraction of bandwidth in every band.

We introduce a new structured sparse recovery algorithm that enables S^3 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 S^3 spectrum sensor using low power off-the-shelf components. Results from a testbed of 19 radios show that S^3 can accurately detect the channel occupancies over a 418 MHz spectrum while sampling $8.5 times below the Nyquist rate even if the spectrum is densely occupied.