Compressed Sensing for Wideband Cognitive Radios
Compressed Sensing for Wideband Cognitive Radios
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DOI：
10.1109/icassp.2007.367330
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发表时间：
2007-04
期刊：
影响因子：
--
通讯作者：
Z. Tian;G. Giannakis
中科院分区：
文献类型：
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作者：
Z. Tian;G. Giannakis
在开放频谱接入的新兴范例中，认知无线电动态地感知无线电频谱环境，并且必须快速地调整其发射机参数以有效地利用可用频谱。所设想的前所未有的无线电敏捷性要求在宽带宽上进行快速准确的频谱感测，这对通常以奈奎斯特速率或高于奈奎斯特速率操作的传统频谱估计方法提出了挑战。利用开放接入网络中信号频谱的稀疏性，提出了一种适合于频谱空洞识别的压缩感知技术。子奈奎斯特速率样本被用来检测和分类频带通过基于小波的边缘检测器。由于频谱位置估计优先于细尺度信号重构，因此所提出的新颖感测算法对噪声具有鲁棒性，并且可以提供降低的采样率。
In the emerging paradigm of open spectrum access, cognitive radios dynamically sense the radio-spectrum environment and must rapidly tune their transmitter parameters to efficiently utilize the available spectrum. The unprecedented radio agility envisioned, calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges traditional spectral estimation methods typically operating at or above Nyquist rates. Capitalizing on the sparseness of the signal spectrum in open-access networks, this paper develops compressed sensing techniques tailored for the coarse sensing task of spectrum hole identification. Sub-Nyquist rate samples are utilized to detect and classify frequency bands via a wavelet-based edge detector. Because spectrum location estimation takes priority over fine-scale signal reconstruction, the proposed novel sensing algorithms are robust to noise and can afford reduced sampling rates.