English

New Conditions for Sparse Phase Retrieval

Information Theory 2014-08-18 v2 math.IT Numerical Analysis Optimization and Control

Abstract

We consider the problem of sparse phase retrieval, where a kk-sparse signal xRn (or Cn){\bf x} \in {\mathbb R}^n \textrm{ (or } {\mathbb C}^n\textrm{)} is measured as y=Ax,{\bf y} = |{\bf Ax}|, where ARm×n (or Cm×n respectively){\bf A} \in {\mathbb R}^{m \times n} \textrm{ (or } {\mathbb C}^{m \times n}\textrm{ respectively)} is a measurement matrix and |\cdot| is the element-wise absolute value. For a real signal and a real measurement matrix A{\bf A}, we show that m=2km = 2k measurements are necessary and sufficient to recover x{\bf x} uniquely. For complex signal xCn{\bf x} \in {\mathbb C}^n and ACm×n{\bf A} \in {\mathbb C}^{m \times n}, we show that m=4k2m = 4k-2 phaseless measurements are sufficient to recover x{\bf x}. It is known that the multiplying constant 44 in m=4k2m = 4k-2 cannot be improved.

Keywords

Cite

@article{arxiv.1310.1351,
  title  = {New Conditions for Sparse Phase Retrieval},
  author = {Mehmet Akçakaya and Vahid Tarokh},
  journal= {arXiv preprint arXiv:1310.1351},
  year   = {2014}
}

Comments

6 pages

R2 v1 2026-06-22T01:40:37.143Z