Vector Approximate Message Passing With Arbitrary I.I.D. Noise Priors
CoRR(2024)
摘要
Approximate message passing (AMP) algorithms are devised under the
Gaussianity assumption of the measurement noise vector. In this work, we relax
this assumption within the vector AMP (VAMP) framework to arbitrary independent
and identically distributed (i.i.d.) noise priors. We do so by rederiving the
linear minimum mean square error (LMMSE) to accommodate both the noise and
signal estimations within the message passing steps of VAMP. Numerical results
demonstrate how our proposed algorithm handles non-Gaussian noise models as
compared to VAMP. This extension to general noise priors enables the use of AMP
algorithms in a wider range of engineering applications where non-Gaussian
noise models are more appropriate.
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关键词
Approximate message passing,expectation propagation,non-Gaussian noise,inference algorithms
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