2014/01/17 by Sheng Cai, Cai, Sheng, Mayank Bakshi +5
Computer Science · Engineering · Mathematics · #Blind Source Separation Techniques #Direction-of-Arrival Estimation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1401.4269
19 pages
openalex publication_date 2014/01/17 · arxiv created 2014/01/25 · arxiv updated 2014/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Suppose \bf x is any exactly k-sparse vector in ℂn. We present a class of phase measurement matrix A in ℂm× n, and a corresponding algorithm, called SUPER, that can resolve \bf x up to a global phase from intensity measurements |A\bf x| with high probability over A. Here |A\bf x| is a vector of component-wise magnitudes of A\bf x. The SUPER algorithm is the first to simultaneously have the following properties: (a) it requires only \cal O(k) (order-optimal) measurements, (b) the computational complexity of decoding is \cal O(klog k) (near order-optimal) arithmetic operations.