Accession Number:

ADA190316

Title:

Information Capacity of Gaussian Channels

Descriptive Note:

Technical rept.

Corporate Author:

NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF STATISTICS

Personal Author(s):

Report Date:

1987-12-01

Pagination or Media Count:

24.0

Abstract:

Information capacity of Gaussian channels is one of the basic problems of information theory. Shannons results for white Gaussian channels and Fanos waterfilling analysis of stationary Gaussian channnels are two of the best-known works of early information theory. Results are given here which extend to a general framework these results and others due to Gallager and to Kadota, Zakai, and Ziv. The development applies to arbitrary Gaussian channels when the channel noise has sample paths in a separable Banach space, and to a large class of Gaussian channels when the noise has sample paths in a linear topological vector space. Solutions for the capacity are given for both matched and mismatched channels. Keywords Gaussian channels Channel capacity Shannon theory Information theory.

Subject Categories:

  • Information Science
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE