Sparsity Independent Sub-Nyquist Rate Wideband Spectrum Sensing on Real-Time TV White Space
Sparsity Independent Sub-Nyquist Rate Wideband Spectrum Sensing on Real-Time TV White Space
复制标题
实时电视白空间上的稀疏性独立亚奈奎斯特速率宽带频谱感测
DOI：
10.1109/tvt.2017.2694706
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发表时间：
2017-10-01
影响因子：
6.8
通讯作者：
Liang, Ying-Chang
中科院分区：
文献类型：
--
作者：
Ma, Yuan;Gao, Yue;Liang, Ying-Chang
宽带频谱感测是认知无线电系统中非常期望的特征，其目的是增加探索频谱机会的概率。欠奈奎斯特采样技术在宽带频谱感知中引起了广泛的关注，而现有的算法只能在稀疏频谱下工作。本文提出了一种基于稀疏快速傅里叶变换的亚奈奎斯特宽带频谱感知算法，该算法利用基于稀疏快速傅里叶变换的低速模数转换器（ADC）实现不依赖于信号稀疏性的宽带感知，无需全带宽采样。为了在保持信道状态信息的同时降低信号的频谱稀疏性，我们提出了一种置换滤波算法对接收信号进行预处理。提出的宽带频谱感知算法对时域信号进行子采样，然后直接估计其频谱。我们推导并验证了所提出的算法，通过数值分析和测试它对现实世界的电视白色空间信号。结果表明，与传统的宽带频谱感知算法相比，该算法在稀疏和非稀疏宽带信号上均具有较高的检测性能，且运行时间和实现复杂度较低。
Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms.