An Approximate Message Passing Framework for Side Information

Abstract

Approximate message passing (AMP) methods have gained recent traction in sparse signal recovery. Additional information about the signal, or \emph{side information} (SI), is commonly available and can aid in efficient signal recovery. This work presents an AMP-based framework that exploits SI and can be readily implemented in various settings for which the SI results in separable distributions. To illustrate the simplicity and applicability of our approach, this framework is applied to a Bernoulli-Gaussian (BG) model and a time-varying birth-death-drift (BDD) signal model, motivated by applications in channel estimation. We develop a suite of algorithms, called AMP-SI, and derive denoisers for the BDD and BG models. Numerical evidence demonstrating the advantages of our approach are presented alongside empirical evidence of the accuracy of a proposed state evolution.

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Cited by in corpus (5)

• Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
• Efficient Massive Machine Type Communication (mMTC) via AMP
• On Massive IoT Connectivity with Temporally-Correlated User Activity
• An Analysis of State Evolution for Approximate Message Passing with Side Information
• Rigorous State Evolution Analysis for Approximate Message Passing with Side Information