A New Insight into GAMP and AMP
arXiv:1907.02247 · doi:10.1109/TVT.2019.2926229
Abstract
A concise expectation propagation (EP) based message passing algorithm (MPA) is derived for the general measurement channel. By neglecting some high-order infinitesimal terms, the EP-MPA is proven to be equivalent to the Generalized Approximate Message Passing (GAMP), which exploits central limit theorem and Taylor expansion to simplify the belief propagation process. Furthermore, for additive white gaussian noise measurement channels, EP-MPA is proven to be equivalent to the AMP. Such intrinsic equivalence between EP and GAMP/AMP offers a new insight into GAMP and AMP via a unified message passing rule for non-linear processing, and may provide clues towards building new MPAs in solving more general non-linear problems.
arXiv comment6 pages, 4 figures, 2 tables
References in corpus (12)
- Message Passing Algorithms for Compressed Sensing
- Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation
- Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing
- Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems
- Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection
- A Unified Bayesian Inference Framework for Generalized Linear Models
- Hybrid Approximate Message Passing
- Capacity Optimality of AMP in Coded Systems
- Rigorous Dynamics of Expectation-Propagation-Based Signal Recovery from Unitarily Invariant Measurements
- Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes
- A comment on the "A unified Bayesian inference framework for generalized linear models"
- Capacity Optimality of AMP in Coded Systems