Blind sensor calibration using approximate message passing

DOI10.1088/1742-5468/2015/11/P11013zbMath1456.94017arXiv1406.5903OpenAlexW3101575158MaRDI QIDQ3302149

Christophe Schülke, Lenka Zdeborová, Francesco Caltagirone

Publication date: 11 August 2020

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1406.5903

Mathematics Subject Classification ID

Bayesian inference (62F15) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Uses Software

• CVX

Cites Work

• Bayesian signal reconstruction for 1-bit compressed sensing
• Approximate message-passing with spatially coupled structured operators, with applications to compressed sensing and sparse superposition codes
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Information, Physics, and Computation
• A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
• Convex Optimization Approaches for Blind Sensor Calibration Using Sparsity
• Statistical Physics of Spin Glasses and Information Processing
• Adaptive Blind Background Calibration of Polynomial-Represented Frequency Response Mismatches in a Two-Channel Time-Interleaved ADC
• The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing
• Sparse nonnegative solution of underdetermined linear equations by linear programming

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