Phase Retrieval Without Small-Ball Probability Assumptions

TL;DR

This paper demonstrates that for subgaussian measurements, including Bernoulli, signals can be uniquely reconstructed without small-ball assumptions, broadening the scope of phase retrieval guarantees.

Contribution

It proves stability, uniqueness, and uniform recovery for phase retrieval with subgaussian measurements without small-ball conditions, extending previous results.

Findings

Unique reconstruction for non-peaky signals with Bernoulli measurements

PhaseLift guarantees with subgaussian measurements without small-ball assumptions

Uniform recovery of all signals with Bernoulli measurements with erasures

Abstract

In the context of the phase retrieval problem, it is known that certain natural classes of measurements, such as Fourier measurements and random Bernoulli measurements, do not lead to the unique reconstruction of all possible signals, even in combination with certain practically feasible random masks. To avoid this difficulty, the analysis is often restricted to measurement ensembles (or masks) that satisfy a small-ball probability condition, in order to ensure that the reconstruction is unique. This paper shows a complementary result: for random Bernoulli measurements, there is still a large class of signals that can be reconstructed uniquely, namely those signals that are "non-peaky." In fact, this result is much more general: it holds for random measurements sampled from any subgaussian distribution D, without any small-ball conditions. This is demonstrated in two ways: first, a proof of stability and uniqueness, and second, a uniform recovery guarantee for the PhaseLift algorithm. In all of these cases, the number of measurements m approaches the information-theoretic lower bound. Finally, for random Bernoulli measurements with erasures, it is shown that PhaseLift achieves uniform recovery of all signals (including peaky ones).