Open AccessAdaptive one‐bit quantisation via approximate message passing with nearest neighbour sparsity pattern learning
Author(s) -
Cao Hangting,
Zhu Jiang,
Xu Zhiwei
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2017.0568
Subject(s) - computer science , nearest neighbour , message passing , bit (key) , algorithm , k nearest neighbors algorithm , artificial intelligence , pattern recognition (psychology) , speech recognition , theoretical computer science , parallel computing , computer network
In this study, the problem of recovering structured sparse signals with a priori distribution whose structure patterns are unknown is studied from one‐bit adaptive (AD) quantised measurements. A generalised approximate message passing (GAMP) algorithm is utilised, and an expectation maximisation (EM) method is embedded in the algorithm to iteratively estimate the unknown a priori distribution. In addition, the nearest neighbour sparsity pattern learning (NNSPL) method is adopted to further improve the recovery performance of the structured sparse signals. Numerical results demonstrate the effectiveness of GAMP‐EM‐AD‐NNSPL method with both simulated and real data.