Information Capacity of Gaussian Channels

Abstract

Information capacity of Gaussian channels is one of the basic problems of information theory. Shannon's results for white Gaussian channels and Fano's 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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA190316

Entities

People

  • Charles R. Baker

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Banach Space
  • Channel Capacity
  • Engineering
  • Frequency
  • Functional Analysis
  • Gaussian Channels
  • Gaussian Noise
  • Hilbert Space
  • Information Theory
  • Mathematical Models
  • Models
  • New York
  • Probability
  • Stationary
  • Stochastic Processes
  • Vector Spaces
  • White Noise

Readers

  • Mathematical Modeling and Probability Theory.
  • Radio communications and signal processing.

Technology Areas

  • Space