Interconnection Algorithms in Multi-Hop Packet Radio Topologies

Abstract

We consider a two-cluster system in multi-hop packet radio topologies. Each cluster deploys a limited sensing random access algorithm, and contains local users who transmit their packets only via the algorithm in their own cluster. The system also contains marginal users, who may transmit their packers via either one of the algorithms in the two clusters. For the above system, we adopt a limited sensing random access algorithm per cluster that has been previously studied. This algorithm utilizes binary, collision versus noncollision, feedback per slot, and in the presence of the limit Poisson user model and the absence of marginal back per slot, and in the presence of the limit Poisson user model and the absence of marginal users its throughout is 0. 43. We consider a dynamic interconnection policy for the marginal users, and we then study the overall system performance in the presence of limit Poisson user populations. Specifically, we study the stability regions of the system and the per packet expected delays. Our interconnection policy accelerates the marginal users, presenting them with a significant delay advantage over the local users. This is desirable when the marginal users transmit high priority data, for example.

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Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1988
Accession Number
ADA197433

Entities

People

  • Liu Ming
  • M. Paterakis
  • P. Papantoni-kazakos

Organizations

  • University of Virginia

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Business Administration
  • Computations
  • Computer Science
  • Electrical Engineering
  • Engineering
  • Ergodic Processes
  • Lyapunov Functions
  • Markov Chains
  • Materials
  • Materials Science
  • Military Research
  • Multiple Access
  • Probability
  • Random Variables
  • Time Intervals

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Mathematical Modeling and Probability Theory.