Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
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DOI：
10.1109/tcomm.2022.3229415
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发表时间：
2022-11
影响因子：
8.3
通讯作者：
Liyang Lu;Wenbo Xu;Yue Wang;Zhi Tian
中科院分区：
文献类型：
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作者：
Liyang Lu;Wenbo Xu;Yue Wang;Zhi Tian
为了在真实的时间内获得较高的感知精度，提出了两种基于块正交匹配追踪（BOMP）算法的宽带压缩频谱感知（CSS）方案。这些方案旨在通过自适应地调整所需测量的数量而不引入不必要的采样冗余来可靠地恢复频谱。为此，成功恢复所需的测量的最小数量首先推导出其概率下限。然后，CSS方案提出了收紧导出的下限，其中的关键是通过一个通用的采样控制算法（SCA）的非线性指数指标的设计。特别是，采样控制BOMP（SC-BOMP）是通过现有的BOMP和建议的SCA的整体集成开发。为了快速实现，修改后的版本的SC-BOMP进一步开发，通过探索块正交性的测量矩阵的子相干性的形式，这允许更多的压缩采样方面的较小的下限的测量的数量。这种快速SC-BOMP方案实现了复杂度和性能之间的期望折衷。仿真结果表明，这两种SC-BOMP方案优于其他基准算法。
This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.