基于负熵最大化的压缩感知信道估计算法

doi:10.12305/j.issn.1001-506X.2021.04.31

Abstract

Abstract:

In the 5G era, with the growth of mobile devices and network traffic, the generation of large-scale machine-type communications, and the combination of space-based satellite networks and terrestrial cellular backbone networks, mobile wireless communication system uses broader frequency spectrum. It results in a more visible sparsity of the channel, which enables the compressed sensing technology to be used in wireless communication. The existing compressed sensing channel estimation algorithm is improved by the negative entropy maximization algorithm and l_p regularization. The traditional mean square error of the minimization error function is replaced by the negative entropy of the maximization objective function to accommodate the non-Gaussian noise of the channel. Sparse constraints use more precise l_p regularization to enhance the sparse representation of channel estimation algorithms. Research shows that the algorithm can not only improve the accuracy of channel estimation, but also enhance the robustness against noise. On the other hand, fewer pilots can be used to achieve more accurate channel estimation, which has the effect of improving system spectrum utilization.

Key words: 5G, channel estimation, compressed sensing, maximum negative entropy, l_p regularization

CLC Number:

TN92

Yingxin ZHAO, Changfeng WANG, Hong WU, Ming ZHANG, Yingjie HUANG, Legeng WANG, Zhiyang LIU. Channel estimation algorithm based on compressed sensing with maximizing negative entropy[J]. Systems Engineering and Electronics, 2021, 43(4): 1126-1132.

Figures/Tables 12

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References 18

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系统参数	设置值
信号采样周期/s	1/15 000/128
基带调制	正交幅度调制
FFT/IFFT长度	128
信道编码	Turbo码
子载波间隔/kHz	15
子载波数	72
循环前缀长度	32

算法	参数
LASSO	λ=3, η=4×10^-3, c=1
加权l₁	λ=3, η=4×10^-3, c=1, w₀=1
LASSO算法	w₀：范数权重迭代初始值
负熵最大化+正则化信道估计算法	λ=2, η=2.5×10^-3, c=1

算法	平均运行时间/s
LASSO	0.081
加权l₁范数LASSO	0.156
负熵最大化+l_0.5	0.106

算法	平均运行时间/s
LASSO	0.142
加权l₁范数LASSO	0.343
负熵最大化+l_0.5	0.211

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Channel estimation algorithm based on compressed sensing with maximizing negative entropy

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