Adaptive one-bit quantisation via approximate message passing with nearest neighbour sparsity pattern learning

作者：Cao, Hangting; Zhu, Jiang^*; Xu, Zhiwei

来源：IET Signal Processing, 2018, 12(5): 629-635.

DOI：10.1049/iet-spr.2017.0568

• quantisation (signal)
• message passing
• learning (artificial intelligence)
• compressed sensing
• expectation-maximisation algorithm
• iterative methods
• adaptive one-bit quantisation
• approximate message passing
• nearest neighbour sparsity pattern learning
• structured sparse signal recovery
• a priori distribution
• generalised approximate message passing
• expectation maximisation method
• iterative estimation
• GAMP-EM-AD-NNSPL method

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.

• 出版日期2018-7
• 单位浙江大学