3 accepted papers
This paper proposes generalized approximate message passing (GAMP) for reconstruction of sparse signals from generalized linear measurements. The signal sparsity is assumed to grow sublinearly in the signal dimension, in contrast to conventional linear sparsity. State evolution is utilized to design…
This paper proposes decentralized generalized approximate message-passing (D-GAMP) for compressed sensing in a tree-structured network. In contrast to conventional GAMP that needs a central node to gather all measurements on the network, D-GAMP utilizes consensus propagation via message-passing betw…
This paper addresses the reconstruction of sparse signals from spatially coupled, linear, and noisy measurements. A unified framework of rigorous state evolution is established for developing long-memory message-passing (LM-MP) in spatially coupled systems. LM-MP utilizes all previous messages to co…