Work

Parametric Bilinear Generalized Approximate Message Passing

Year: 2016

Type: article

Abstract: We propose a scheme to estimate the parameters bi and cj of the bilinear form zm= Σi,jbizm(i,j)cjfrom noisy measurements {ym}mM=1, where ymand zmare related through an arbitrary likelihood function and zm(i,j)are known. Our scheme is based on generalized approximate message passing (G-AMP): it treats biand cjas random variables and zm(i,j)as an i.i.d. Gaussian 3-way tensor in order to derive a tractable simplification of the sum-product algorithm in the large-system limit. It generalizes previous instances of bilinear G-AMP, such as those that estimate matrices B and C from a noisy measurement of Z = BC, allowing the application of AMP methods to problems such as self-calibration, blind deconvolution, and matrix compressive sensing. Numerical experiments confirm the accuracy and computational efficiency of the proposed approach.

Source: IEEE Journal of Selected Topics in Signal Processing

DOI: 10.1109/jstsp.2016.2539123

Authors: Jason T. Parker, Philip Schniter

Institutions: United States Air Force Research Laboratory, The Ohio State University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Shanghai Ocean University

Awards: Collaborative Research: I/UCRC for The Center for Surveillance Research, Message-Passing Strategies for High-Dimensional Inference, CIF: Small: Collaborative Research: Next Generation Communications with Low-Resolution ADCs: Fundamentals and Practical Design