DOI number:10.1109/LSP.2018.2876806

Journal:IEEE Signal Processing Letters

Abstract:Generalized approximate message passing (GAMP) is an efficient algorithm for the estimation of independent identically distributed random signals under generalized linear model. The sum-product GAMP has long been recognized as an approximate implementation of the sum-product loopy belief propagation. In this letter, we propose to view the message passing in a new perspective of expectation propagation (EP). Comparing with the previous methods that were based on Taylor expansions, the proposed EP method could unify the derivations for the real and the complex GAMP, with a difference only in the setup of Gaussian densities.

First Author:Q. Zou, H. Zhang, C. Wen, S. Jin, R. Yu

Indexed by:Journal paper

Volume:25

Issue:12

ISSN No.:1558-2361

Translation or Not:no

Date of Publication:2018-10-18