A score-matching procedure driven by state evolution trains a neural network to replace the analytically intractable MMSE denoiser, allowing Bayes-GAMP to achieve its asymptotic optimality for arbitrary complex nonlinear observation mappings under ideal training.
Score-based V AMP with Fisher- information-based Onsager correction
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A three-module SC-VAMP receiver for LDPC codes over nonlinear channels achieves waterfall BER performance that approaches capacity estimates as block length increases.
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State-Evolution-based Score Matching for Generalized Approximate Message Passing
A score-matching procedure driven by state evolution trains a neural network to replace the analytically intractable MMSE denoiser, allowing Bayes-GAMP to achieve its asymptotic optimality for arbitrary complex nonlinear observation mappings under ideal training.
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Three-Module SC-VAMP for LDPC-Coded Nonlinear Channels
A three-module SC-VAMP receiver for LDPC codes over nonlinear channels achieves waterfall BER performance that approaches capacity estimates as block length increases.