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  1. Cognitive radio aims at identifying unused radio-frequency (RF) bands with the goal of re-using them opportunistically for other services. While compressive sensing (CS) has been used to identify strong signals (or interferers) in the RF spectrum from sub-Nyquist measurements, identifying unused frequencies from CS measurements appears to be uncharted territory. In this paper, we propose a novel method for identifying unused RF bands using an algorithm we call least matching pursuit (LMP). We present a sufficient condition for which LMP is guaranteed to identify unused frequency bands and develop an improved algorithm that is inspired by our theoretical result. We perform simulations for a CS-based RF whitespace detection task in order to demonstrate that LMP is able to outperform black-box approaches that build on deep neural networks.
  2. Here we present a combined RF hardware/DSP technique to synthesize effective channel diversity in single-antenna wireless systems. This allows digital suppression of out-of-band interference artifacts in widely tunable wireless receivers with one or more antennas, including artifacts from LO phase noise. A passive inductor-capacitor (LC) network provides gain and phase diversity between channels and across frequency. Since amplitude and phase of in-band artifacts are set by the amplitude and phase of the out-of-band interference that generates them, they can be suppressed in DSP without knowledge about the interferer itself. The feasibility of this approach is demonstrated mathematically, with numerical system simulations, and full circuit simulation.