Capacity-Approaching PhaseCode for Low-Complexity Compressive Phase Retrieval

TL;DR

This paper introduces a novel, capacity-approaching algorithm for low-complexity compressive phase retrieval that nearly achieves the theoretical measurement limit using sparse-graph codes, with practical linear-time recovery.

Contribution

It presents the first constructive algorithm that approaches the fundamental measurement limit for compressive phase retrieval, utilizing irregular sparse-graph codes for efficient recovery.

Findings

Almost all non-zero components recovered with just over 4K measurements

Achieves optimal ${ m O}(K)$ time and memory complexity

A modular architecture combining compressive sensing and phase retrieval layers

Abstract

In this paper, we tackle the general compressive phase retrieval problem. The problem is to recover a K-sparse complex vector of length n, $x\in \mathbb{C}^n$, from the magnitudes of m linear measurements, $y=|Ax|$, where $A \in \mathbb{C}^{m \times n}$ can be designed, and the magnitudes are taken component-wise for vector $Ax\in \mathbb{C}^m$. We propose a variant of the PhaseCode algorithm, and show that, using an irregular left-degree sparse-graph code construction, the algorithm can recover almost all the K non-zero signal components using only slightly more than 4K measurements under some mild assumptions, with optimal time and memory complexity of ${\cal O}(K)$. It is known that the fundamental limit for the number of measurements in compressive phase retrieval problem is $4K - o(K)$. To the best of our knowledge, this is the first constructive capacity-approaching compressive phase retrieval algorithm. As a second contribution, we propose another variant of the PhaseCode algorithm that is based on a Compressive Sensing framework involving sparse-graph codes. Our proposed algorithm is an instance of a more powerful "separation" architecture that can be used to address the compressive phase-retrieval problem in general. This modular design features a compressive sensing outer layer, and a trigonometric-based phase-retrieval inner layer. The compressive-sensing layer operates as a linear phase-aware compressive measurement subsystem, while the trig-based phase-retrieval layer provides the desired abstraction between the actually targeted nonlinear phase-retrieval problem and the induced linear compressive-sensing problem. Invoking this architecture based on the use of sparse-graph codes for the compressive sensing layer, we show that we can exactly recover a signal from only the magnitudes of its linear measurements using only slightly more than 6K measurements.