On the Performance of a Class of Random Access Algorithms in the Presence of Limitations on Waiting Times.

Abstract

This document discusses the random access problem in the presence of hard limitations on the per packet waiting and access time. The authors describe and analyze a class of random access algorithms in this case, where the limit Poisson user model is adopted. For two specific algorithms in the class, they present quantitative results regarding output rate, delays, and proportion of rejected packets. Considered is a packet network with independent and identical users and a common transmission channel. It is required that the channel time be slotted, and that tranmissions be then synchronous (each packet transmission may only start at the beginning of some slot). At the end of each slot a feedback is received. The feedback is common to all users, and contains information about the acivity of the channel in the current slot. It is assumed that if more than one packets are simultaneously transmitted during the same slot, a collision event occurs, and that the information in the transmitted packets is lost. A prespecified algorithm, performed independently by each user, is used to schedule the retransmission of collided packets in future slots. The feedback information provided by the channel is basic for the operation of the algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1986
Accession Number
ADA172697

Entities

People

  • L. Georgiadis
  • M. Paterakis
  • P. Papantoni-kazakos

Organizations

  • University of Virginia

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Access Time
  • Algorithms
  • Applied Mathematics
  • Applied Mechanics
  • Business Administration
  • Computations
  • Electrical Engineering
  • Engineering
  • Markov Chains
  • Materials
  • Materials Science
  • Plastic Explosives
  • Probability
  • Random Variables
  • Schools
  • Sequences
  • Universities

Fields of Study

  • Computer science

Readers

  • Radio communications and signal processing.
  • Regression Analysis.