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MEMS-based RF channel selection for true software-defined cognitive radio and low-power sensor communications

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1 Author(s)
Clark T. -C. Nguyen ; University of California at Berkeley

An evaluation of the potential for MEMS technologies to realize the RF front-end frequency gating spectrum analyzer function needed by true software-defined cognitive radios and ultra-low-power autonomous sensor network radios is presented. Here, RF channel selection, as opposed to band selection that removes all interferers, even those in band, and passes only the desired channel, is key to substantial potential increases in call volume with simultaneous reductions in power consumption. The relevant MEMS technologies most conducive to RF channel- selecting front-ends include vibrating micromechanical resonators that exhibit record on-chip Qs at gigahertz frequencies; resonant switches that provide extremely efficient switched-mode power gain for both transmit and receive paths; medium-scale integrated micromechanical circuits that implement on/off switchable filter-amplifier banks; and fabrication technologies that integrate MEMS together with foundry CMOS transistors in a fully monolithic low-capacitance single-chip process. The many issues that make realization of RF channel selection a truly challenging proposition include resonator drift stability, mechanical circuit complexity, repeatability and fabrication tolerances, and the need for resonators at gigahertz frequencies with simultaneous high Ω (>30,000) and low impedance (e.g., 50 W for conventional systems). Some perspective on which resonator technologies might best achieve these simultaneous attributes is provided.

Published in:

IEEE Communications Magazine  (Volume:51 ,  Issue: 4 )