Upscaling Vector Approximate Message Passing

Nikolajs Skuratovs; Michael Davies

ICASSP 2020accepted0 citations

Upscaling Vector Approximate Message Passing

Abstract

In this paper we consider the problem of recovering a signal x of size N from noisy and compressed measurements y = Ax + w of size M, where the measurement matrix A is right-orthogonally invariant (ROI). Vector Approximate Message Passing (VAMP) demonstrates great reconstruction results for even highly ill-conditioned matrices A in relatively few iterations. However, performing each iteration is challenging due to either computational or memory point of view. On the other hand, a recently proposed Conjugate Gradient (CG) Expectation Propagation (CG-EP) framework is able to sacrifice some performance for efficiency, but requires access to exact singular spectrum of A. In this work we develop a CG-VAMP algorithm that does not require such information, is feasible to implement and converges to the neighborhood of the original VAMP.

BibTeX

@inproceedings{icassp2020_upscalingvectora,
  title = {Upscaling Vector Approximate Message Passing},
  author = {Nikolajs Skuratovs and Michael Davies},
  booktitle = {ICASSP 2020},
  year = {2020}
}