Research on Deterministic Measurement Matrix of Power Line Carrier Compressed Sensing
Research on Deterministic Measurement Matrix of Power Line Carrier Compressed Sensing
复制标题
电力线载波压缩感知确定性测量矩阵研究
DOI：
10.1088/1742-6596/2095/1/012017
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Yuqi Dong
中科院分区：
文献类型：
--
作者：
Jiliang Jin;Liyun Xing;Miao Yang;Jianqiang Shen;Yuqi Dong
抽象。验证确定性矩阵应用于基于压缩感知的电力线载波通信的优越性。分析了常用的确定性测量矩阵的研究现状，并进行了仿真比较。结果表明，基于混沌映射生成的不同类型的确定性测量矩阵比高斯随机矩阵具有更高的重构精度和重构效率。然后，根据仿真结果和PLCC信号的特点，结合八阶Chebyshev混沌和稀疏循环的思想，设计了Chebyshev稀疏循环（CSC）测量矩阵。实际电路测量表明，当压缩率为40%和60%时，CSC的重构损耗分别比Chebyshev混沌测量矩阵和Chebyshev循环测量矩阵高0.72dB和0.49dB。显然，本文设计的CSC测量矩阵可以有效地提高重建精度。
Abstract. To verify the advantages of deterministic matrix applied to power line carrier communication (PLCC) based on compressed sensing (CS). This article analyzed the research status of commonly used deterministic measurement matrices, and made simulation comparison. It is found that different types of deterministic measurement matrices generated based on chaotic mapping had higher reconstruction accuracy and higher reconstruction efficiency than Gaussian random matrix. Then, according to simulation results and the characteristics of PLCC signal, the Chebyshev sparse circulant (CSC) measurement matrix was designed by combining eighth-order Chebyshev chaotic and the idea of sparse and circulant. Actual circuit measurement shows that when compression rate was 40% and 60%, the reconstruction loss of CSC is 0.72dB and 0.49dB higher than that of Chebyshev chaotic measurement matrix and Chebyshev circulate measurement matrix, respectively. Obviously, the CSC measurement matrix designed in this paper can effectively improve the reconstruction accuracy.
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DOI：
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期刊：
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