论文标题
使用最不匹配的追踪来识别未使用的RF频道
Identifying Unused RF Channels Using Least Matching Pursuit
论文作者
论文摘要
认知无线电旨在确定未使用的无线电频段（RF）频段，目的是将其重新利用其他服务重新使用。虽然已使用压缩传感（CS）来识别RF频谱中的强信号（或干扰器），但通过子nyquist测量值识别CS测量未使用的频率似乎是未知的领域。在本文中，我们提出了一种新的方法，用于使用我们称之为最不匹配的追踪（LMP）的算法来识别未使用的RF频段。我们提出了一个充分的条件，保证LMP可以识别未使用的频带并开发出受我们理论结果启发的改进算法。我们为基于CS的RF Whitespace检测任务执行仿真，以证明LMP能够超越基于深神经网络的黑盒方法。
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.
