CONDITIONS FOR OPTIMUM DIGITAL COMMUNICATION WITH APPLICATION TO DELTA MODULATION

Abstract

A procedure for optimization of a class of binary communication systems is presented and applied. The message set or transmitter input is taken to be a real-valued sample sequence from a stochastic process with discrete parameter. The transmitter may be any time-varying, nonlinear operator with domain of the real valued input and range to the binary numbers. The transmission medium of noisy channel linking the transmitter and receiver is to be characterized by the conditional probabilities of all possible received binary sequences given any transmitted sequence. The receiver may be any real-valued, time-varying, nonlinear operator on the received binary sequences. The optimization conditions obtained are discussed and the relationship between an optimum communication system and a delta modulation system indicated. It is shown that, for the quadratic loss function and any noisy channel, a delta modulation system is an allowable representation of the optimum binary system. Preliminary results on an extended digital communication system model are discussed and areas for further research are indicated.

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Document Details

Document Type
Technical Report
Publication Date
Mar 05, 1963
Accession Number
AD0405053

Entities

People

  • Terrence Fine

Organizations

  • Harvard University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Communication Systems
  • Computers
  • Delta Modulation
  • Digital Communications
  • Distribution Functions
  • Estimators
  • Modulation
  • Modulators
  • Optimization
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Pulse Code Modulation
  • Random Variables
  • Simulations
  • Statistics
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Computer Programming and Software Development.
  • Operations Research
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms