Generalized Turbo Signal Recovery for Nonlinear Measurements and Orthogonal Sensing Matrices

Abstract

In this study, we propose a generalized turbo signal recovery algorithm to estimate a signal from quantized measurements, in which the sensing matrix is a row-orthogonal matrix, such as the partial discrete Fourier transform matrix. The state evolution of the proposed algorithm is derived and is shown to be consistent with that obtained with the replica method. Numerical experiments illustrate the excellent agreement of the proposed algorithm with theoretical state evolution.

To appear in the 2016 IEEE International Symposium on Information Theory (ISIT 2016)

References in corpus (3)

• Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices
• Turbo Compressed Sensing with Partial DFT Sensing Matrix
• A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices

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