Scheduling Link Activation in Multihop Radio Networks by Means of Hopfield Neural Network Techniques

Abstract

We address the problem of link activation or scheduling in multihop packet radio networks, a contention-free form of channel access that is appropriate for many military communication applications. It is well known that this problem, in almost all of its forms, is a combinatorial-optimization problem of high complexity. We approach this problem by use of a Hopfield neural network model in which the method of Lagrange multipliers is used to vary dynamically the values of the coefficients used in the connection weights. Two forms of the scheduling problem are considered. In the first, communication requirements are specified in terms of the number of packets that must be transmitted over each link in the network. In the second, an additional constraint is incorporated, namely that the sequence of link activations along any multihop path must be preserved. Extensive software simulation results demonstrate the effectiveness of this approach in producing schedules of optimal length. Issues associated with the extension of this approach to the joint routing/scheduling problem are discussed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 03, 1991
Accession Number
ADA241242

Entities

People

  • Anthony Ephremides
  • Craig M. Barnhart
  • Jeffrey E. Wieselthier

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Calorific Value
  • Code Division Multiple Access
  • Communication Channels
  • Communication Networks
  • Communication Systems
  • Equations
  • Equations Of Motion
  • Heuristic Methods
  • Information Systems
  • Military Communications
  • Monte Carlo Method
  • Multiple Access
  • Neural Networks
  • Radio Communications
  • Standards

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Fully Networked C3
  • Fully Networked C3 - Command and Control