Low-Complexity Implementation of Convex Optimization-Based Phase Retrieval
Low-Complexity Implementation of Convex Optimization-Based Phase Retrieval
复制标题
基于凸优化的相位检索的低复杂度实现
DOI：
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发表时间：
2017
影响因子：
4.7
通讯作者：
J. Kahn
中科院分区：
文献类型：
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作者：
Sercan Ö. Arik;J. Kahn
相位恢复在光学成像、通信和传感中具有重要的应用。提升问题的维数允许相位恢复近似为高维空间中的凸优化问题。基于凸优化的相位恢复已被证明具有高精度，但其低复杂度的实现尚未被探索。在本文中，我们研究了其低复杂度实现的三种基本方法：投影梯度法，Nesterov加速梯度法，和交替方向乘法器（ADMM）。我们推导出相应的估计算法，并评估其复杂性。我们比较了它们在直接检测模分复用应用领域的性能。我们证明，它们产生小的估计罚款（小于0.2 dB的发射机处理和小于0.6 dB的接收机均衡），同时产生低的计算成本，因为它们的实现复杂性都规模平方未知参数的数量。在这三种方法中，ADMM以最少的迭代次数和最少的运算量达到收敛。
Phase retrieval has important applications in optical imaging, communications, and sensing. Lifting the dimensionality of the problem allows phase retrieval to be approximated as a convex optimization problem in a higher dimensional space. Convex optimization-based phase retrieval has been shown to yield high accuracy, yet its low-complexity implementation has not been explored. In this paper, we study three fundamental approaches for its low-complexity implementation: the projected gradient method, the Nesterov accelerated gradient method, and the alternating direction method of multipliers (ADMM). We derive the corresponding estimation algorithms and evaluate their complexities. We compare their performance in the application area of direct-detection mode-division multiplexing. We demonstrate that they yield small estimation penalties (less than 0.2 dB for transmitter processing and less than 0.6 dB for receiver equalization) while yielding low computational cost, as their implementation complexities all scale quadratically in the number of unknown parameters. Among the three methods, ADMM achieves convergence after the fewest iterations and the fewest computational operations.
影响因子：
6.1
作者：
Rodriguez, Jose A.;Xu, Rui;Miao, Jianwei
通讯作者：
Miao, Jianwei