ANM-PhaseLift: Structured line spectrum estimation from quadratic measurements

Zhe Zhang, Z. Tian
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Abstract

PhaseLift is a noted convex optimization technique for phase retrieval that can recover a signal exactly from amplitude measurements only, with high probability. Conventional PhaseLift requires a relatively large number of samples that sometimes can be costly to acquire. This paper focuses on some practical applications where the signal of interest is composed of a few Vandermonde components, such as line spectra. A novel phase retrieval framework, namely ANM-PhaseLift, is developed that exploits the Vandermonde structure to alleviate the sampling requirements. Specifically, the atom set of amplitude-based quadratic measurements is identified, and atomic norm minimization (ANM) is introduced into PhaseLift to considerably reduce the number of measurements that are needed for accurate phase retrieval. The benefit of ANM-PhaseLift is particularly attractive in applications where the Vandermonde structure is presented, such as massive MIMO and radar imaging.
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ANM-PhaseLift:基于二次测量的结构化线谱估计
PhaseLift是一种著名的相位恢复凸优化技术,它可以仅从幅度测量中精确地恢复信号,并且具有高概率。传统的PhaseLift需要相对大量的样品,有时采集成本很高。本文重点介绍了一些实际应用中,感兴趣的信号是由几个范德蒙德分量组成的,如线谱。提出了一种新的相位检索框架,即ANM-PhaseLift,该框架利用Vandermonde结构来减轻采样要求。具体来说,识别了基于幅度的二次测量的原子集,并将原子范数最小化(ANM)引入PhaseLift,从而大大减少了精确相位检索所需的测量次数。在采用Vandermonde结构的应用中,例如大规模MIMO和雷达成像,ANM-PhaseLift的优势尤其具有吸引力。
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