Robust Instance Optimal Phase-Only Compressed Sensing

TL;DR

This paper establishes the uniform instance optimality and robustness of phase-only compressed sensing (PO-CS), demonstrating near-optimal stability under noise and adversarial corruption, and extending guarantees to all signals in the unit sphere.

Contribution

It strengthens previous nonuniform results to a uniform guarantee over the entire signal space and analyzes robustness to noise and corruption in PO-CS.

Findings

Achieves uniform instance optimality for all signals in the unit sphere.

Shows robustness of the estimator to bounded noise, with error increment proportional to noise level.

Demonstrates resilience to adversarial measurement corruption with controlled error increase.

Abstract

Phase-only compressed sensing (PO-CS) concerns the recovery of sparse signals from the phases of complex measurements. Recent results show that sparse signals in the standard sphere $\mathbb{S}^{n-1}$ can be exactly recovered from complex Gaussian phases by a linearization procedure, which recasts PO-CS as linear compressed sensing and then applies (quadratically constrained) basis pursuit to obtain $\mathbf{x}^\sharp$. This paper focuses on the instance optimality and robustness of $\mathbf{x}^{\sharp}$. First, we strengthen the nonuniform instance optimality of Jacques and Feuillen (2021) to a uniform one over the entire signal space. We show the existence of some universal constant $C$ such that $\|\mathbf{x}^\sharp-\mathbf{x}\|_2\le Cs^{-1/2}\sigma_{\ell_1}(\mathbf{x},\Sigma^n_s)$ holds for all $\mathbf{x}$ in the unit Euclidean sphere, where $\sigma_{\ell_1}(\mathbf{x},\Sigma^n_s)$ is the $\ell_1$ distance of $\mathbf{x}$ to its closest $s$-sparse signal. This is achieved by showing the new sensing matrices corresponding to all approximately sparse signals simultaneously satisfy RIP. Second, we investigate the estimator's robustness to noise and corruption. We show that dense noise with entries bounded by some small $\tau_0$, appearing either prior or posterior to retaining the phases, increments $\|\mathbf{x}^\sharp-\mathbf{x}\|_2$ by $O(\tau_0)$. This is near-optimal (up to log factors) for any algorithm. On the other hand, adversarial corruption, which changes an arbitrary $\zeta_0$-fraction of the measurements to any phase-only values, increments $\|\mathbf{x}^\sharp-\mathbf{x}\|_2$ by $O(\sqrt{\zeta_0\log(1/\zeta_0)})$. The developments are then combined to yield a robust instance optimal guarantee that resembles the standard one in linear compressed sensing.