Open access
Date
2020Type
- Conference Paper
ETH Bibliography
no
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Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000452591Publication status
publishedExternal links
Book title
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
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ETH Bibliography
no
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