Derivatives of Mutual Information in Gaussian Channels

The I-MMSE formula connects two important quantities in information theory and estimation theory: the mutual information and the minimum mean-squared error (MMSE). It states that in a scalar Gaussian channel, the derivative of the mutual information with respect to the signal-to-noise ratio (SNR) is one-half of the MMSE. Although any derivative at a fixed order can be computed in principle, a general formula for all the derivatives is still unknown. In this paper, we derive this general formula for vector Gaussian channels. The obtained result is remarkably similar to the classic cumulant-moment relation in statistical theory.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Information Theory
Published
2024-08-21
DOI
https://doi.org/10.1109/tit.2024.3447224
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Derivatives of Mutual Information in Gaussian Channels

Minh-Toan Nguyen
1 citations
IEEE Transactions on Information Theory
Neural Networks and Applications
0.35
article

Derivatives of Mutual Information in Gaussian Channels

Minh-Toan Nguyen
article en
1 citations

Abstract

The I-MMSE formula connects two important quantities in information theory and estimation theory: the mutual information and the minimum mean-squared error (MMSE). It states that in a scalar Gaussian channel, the derivative of the mutual information with respect to the signal-to-noise ratio (SNR) is one-half of the MMSE. Although any derivative at a fixed order can be computed in principle, a general formula for all the derivatives is still unknown. In this paper, we derive this general formula for vector Gaussian channels. The obtained result is remarkably similar to the classic cumulant-moment relation in statistical theory.

IEEE Transactions on Information TheoryVol. 70(11)
GIPSA-Lab (FR)
Openalex Percentile: Top 35%
Neural Networks and Applications
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.