On one-stage recovery for ΣΔ-quantized compressed sensing

Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. When such a signal is acquired according to the principles of CS, the measurements still take on values in the continuum. In today's "digital" world, a subsequent quantization step, where these measurements are replaced with elements from a finite set is crucial. We focus on one of the approaches that yield efficient quantizers for CS: EA quantization, followed by a one-stage tractable reconstruction method, which was developed in [20] with theoretical error guarantees in the case of sub-Gaussian matrices. We propose two alternative approaches that extend the results of [20] to a wider class of measurement matrices including (certain unitary transforms of) partial bounded orthonormal systems and deterministic constructions based on chirp sensing matrices.