Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments
Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments
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DOI：
10.1109/hmwc.2014.7000208
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Guan Gui;Li Xu;F. Adachi
中科院分区：
文献类型：
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作者：
Guan Gui;Li Xu;F. Adachi
在高移动性无线通信和信号处理中，经常会遇到非高斯噪声干扰下的欠定逆稀疏信号重构问题。这些问题可以通过寻找合适的目标函数的最小值来解决，该目标函数由不同混合范数的数据拟合项和正则化项组成。基于高斯噪声假设，验证了两个混合范数（即2/ 1和1∞/ 1）是有效且稳定的稀疏信号重构算法。然而，这两种算法都无法在非高斯噪声环境下重建稳定的信号。本文提出了一种稳定的最小绝对偏差（LAD）算法（即l_1 / l_1），实现了利用信号稀疏结构信息和减轻非高斯噪声干扰两方面的目的。首先，通过蒙特卡罗仿真选择算法的正则化参数。然后，利用不同非高斯环境下的实验结果验证了算法的有效性。
Underdetermined inverse sparse signal reconstruction problems in the presence of non-Gaussian noise interference are often encountered in high-mobility wireless communications and signal processing. These problems can be solved by finding the minimizer of a suitable objective function which consists of a data-fitting term and a regularization term with different mixed-norms. Based on the Gaussian-noise assumption, two mixed norms (i.e. ℓ2/ℓ1 and ℓ∞/ℓ1) were confirmed as effective as well as stable algorithms for reconstructing sparse signals. However, the two algorithms are unable to reconstruct signal stable under non-Gaussian noise environments. In this paper, we propose a stable least absolute deviation (LAD) algorithm (i.e., ℓ1/ℓ1) for achieving two aspects: exploiting signal sparse structure information as well as mitigating the non-Gaussian noise interference. First of all, regularization parameter of the proposed algorithm is selected via Monte Carlo simulations. Then, experimental results in different non-Gaussian environments are used to demonstrate the effectiveness of the proposed algorithm.