超越 I-MMSE 关系:高斯信道中的互信息导数

IF 2.2 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Minh-Toan Nguyen
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引用次数: 0

摘要

I-MMSE 公式连接了信息论和估计理论中的两个重要量:互信息和最小均方误差(MMSE)。该公式指出,在标量高斯信道中,互信息相对于信噪比(SNR)的导数是 MMSE 的二分之一。虽然原则上可以计算固定阶次的任何导数,但所有导数的一般公式仍然未知。在本文中,我们推导出了矢量高斯信道的一般公式。所得到的结果与统计理论中经典的积矩关系极为相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory 工程技术-工程:电子与电气
CiteScore
5.70
自引率
20.00%
发文量
514
审稿时长
12 months
期刊介绍: The IEEE Transactions on Information Theory is a journal that publishes theoretical and experimental papers concerned with the transmission, processing, and utilization of information. The boundaries of acceptable subject matter are intentionally not sharply delimited. Rather, it is hoped that as the focus of research activity changes, a flexible policy will permit this Transactions to follow suit. Current appropriate topics are best reflected by recent Tables of Contents; they are summarized in the titles of editorial areas that appear on the inside front cover.
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