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arXiv 2006.02890cs.ITmath.IT
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基于基数约束最小二乘的二值测量鲁棒译码

Robust Decoding from Binary Measurements with Cardinality Constraint Least Squares

Zhao Ding, Junjun Huang, Yuling Jiao, Xiliang Lu, Zhijian Yang

摘要:

1比特压缩采样的主要目标是从$m$二进制测量中解码具有稀疏水平$s$的$n$维信号。由于存在非线性、噪声和符号翻转,这是一项具有挑战性的任务。在本文中,基数约束最小二乘法提出了作为一个期望的解码器。我们证明,直到一个恒定的$c$,具有高概率,建议的解码器实现了极大极小估计误差只要$m \geq \mathcal{O}( s\log n)$。在计算上,我们利用广义牛顿算法(GNA)来解决基数约束最小化问题的成本解决最小二乘问题,在每次迭代的小尺寸。我们证明,以高概率,估计误差之间的GNA的输出和潜在的目标的$\ell_{\infty}$范数衰减到$\mathcal{O}(\sqrt{\frac{\log n }{m}}) $后,在最多$\mathcal{O}(\log s)$迭代。此外,如果靶信号是可检测的,则可以在$\mathcal{O}(\log s)$步骤中以高概率回收底层支持物。大量的数值模拟和比较与国家的最先进的方法来说明我们提出的解码器的鲁棒性和GNA算法的效率。

英文摘要:

The main goal of 1-bit compressive sampling is to decode $n$ dimensional signals with sparsity level $s$ from $m$ binary measurements. This is a challenging task due to the presence of nonlinearity, noises and sign flips. In this paper, the cardinality constraint least square is proposed as a desired decoder. We prove that, up to a constant $c$, with high probability, the proposed decoder achieves a minimax estimation error as long as $m \geq \mathcal{O}( s\log n)$. Computationally, we utilize a generalized Newton algorithm (GNA) to solve the cardinality constraint minimization problem with the cost of solving a least squares problem with small size at each iteration. We prove that, with high probability, the $\ell_{\infty}$ norm of the estimation error between the output of GNA and the underlying target decays to $\mathcal{O}(\sqrt{\frac{\log n }{m}}) $ after at most $\mathcal{O}(\log s)$ iterations. Moreover, the underlying support can be recovered with high probability in $\mathcal{O}(\log s)$ steps provided that the target signal is detectable. Extensive numerical simulations and comparisons with state-of-the-art methods are presented to illustrate the robustness of our proposed decoder and the efficiency of the GNA algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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