认知无线电中实现自适应压缩频谱感知

doi:10.3969/j.issn.1001-506X.2020.01.03

Abstract

Abstract:

The spectrum is sparse in the real world, and it has enormous advantages when applying compressed sensing (CS) technology to wideband spectrum sensing. However, the sparsity is often unknown in practice, so a large number of measurements have to be chosen, which will lead to the performance degradation. To solve this problem, an adaptive compressive spectrum sensing method is proposed. The coarse sparsity estimation can be obtained by analyzing the relationship between the second derivative of compressive measurements and sparsity. Then by increasing the number of measurements and continuing iterations step by step in both training subset and test subset, the accurate sparsity estimation can be obtained while the halting criterion can be met. Simulation shows that the performance of our method is better than other traditional CS methods, which is very important for decreasing the complexity and memory. Moreover, the effectiveness of the proposed method is also verified in noise environment.

Key words: cognitive radio, compressed sensing (CS), wideband spectrum sensing, sparsity order estimation

CLC Number:

TN95

Yuan LUO, Jiaojiao DANG, Zuxun SONG, Baoping WANG. Achieving adaptive compressive spectrum sensing for cognitive radio[J]. Systems Engineering and Electronics, 2020, 42(1): 15-22.

Figures/Tables 12

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References 33

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符号	描述	设定值
N	奈奎斯特样本数	1 000
K	信号稀疏度	0~150
λ	能量检测阈值	μ/2
f_i	信号中心频率/MHz	10i
SNR	接收信噪比/dB	10~20
Δt	随机时延/μs	0~0.1
F	奈奎斯特采样率/GHz	1
α	恢复误差阈值	$\sqrt {{P_W}/{P_E}} $
E_i	信号接收功率	1

频谱恢复算法	优点	缺点
传统OMP算法	一次迭代,无需考虑何时停止,无需对稀疏度进行估计	过大的测量矩阵、过多循环匹配次数以及两者导致的恢复误差过大
逐次迭代算法	能够及时停止迭代,无需对稀疏度进行估计,能够得到较为理想的压缩比	迭代次数过多,计算复杂度随之增加,阈值固定导致受环境影响较大
本文算法	能够及时停止迭代,迭代次数很少,能够得到较为理想的压缩比,阈值根据SNR变化	需要对稀疏度进行估计

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Achieving adaptive compressive spectrum sensing for cognitive radio

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