Identifying Unused RF Channels Using Least Matching Pursuit
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Date
2020
Publication Type
Conference Paper
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no
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OPEN ACCESS
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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.
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published
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Book title
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
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Pages / Article No.
1 - 5
Publisher
IEEE
Event
21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2020) (virtual)
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Software
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09695 - Studer, Christoph / Studer, Christoph