Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace
Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace
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DOI：
10.1109/tvt.2019.2937649
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发表时间：
2019-10
影响因子：
6.8
通讯作者：
Haoran Qi;Xingjian Zhang;Yue Gao
中科院分区：
文献类型：
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作者：
Haoran Qi;Xingjian Zhang;Yue Gao
压缩感知（CS）技术已被提出用于宽带频谱感知应用，以实现亚奈奎斯特速率采样。CS恢复算法的复杂度和对噪声的检测性能是压缩频谱感知（CSS）实现的两个主要挑战。贪婪算法由于复杂度低而在CSS中特别受关注。本文首先提出了一种新的直接从次奈奎斯特观测值估计频谱稀疏度的方法，该方法可以节省贪婪追踪算法的计算量，提高恢复性能。此外，频谱稀疏估计还使得能够实现信道占用的硬检测，其中避免了用于能量检测的阈值自适应。此外，利用检测到的信号子空间维数，提出了一种联合块稀疏多测量向量（MMV）模型，该模型可以在去除噪声的同时将维数降到最小。所提出的MMV模型与噪声和降维进一步提高了检测性能，也保持了低的复杂度。最后，我们推广的硬阈值追踪（HTP）算法恢复联合块稀疏信号。在仿真中，所提出的CSS方案的检测性能和复杂度显示出显着的优越性，对多个基准计划。
Compressive sensing (CS) techniques have been proposed for wideband spectrum sensing applications to achieve sub-Nyquist-rate sampling. The complexity of CS recovery algorithm and the detection performance against noise are two of the main challenges of the implementation of compressive spectrum sensing (CSS). Greedy algorithms have been of particular interest in CSS due to low complexity. We firstly propose a novel spectrum sparsity estimation scheme directly from sub-Nyquist measurements, with which the computational effort of greedy pursuit algorithms can be saved and recovery performance improved. Besides, the spectrum sparsity estimates also enable hard detection of channel occupancy where threshold adaption for energy detection is avoided. Moreover, with the detected dimension of signal subspace, we propose to implement joint-block-sparse multiple-measurement-vector (MMV) model of CSS whose dimension can be reduced to minimum and meanwhile a large portion of noise is removed. The proposed MMV model with noise and dimension reduction further improves the detection performance and also keeps the complexity low. Finally, we generalize the hard thresholding pursuit (HTP) algorithm to recover joint-block-sparse signals. In simulations, the detection performance and complexity of the proposed CSS scheme show striking superiority against multiple benchmarking schemes.