Controlled random access MAC for network utility maximization in wireless networks

Robert J. McCabe, Nikolaos M. Freris, P. R. Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There has been much recent interest in protocol design for wireless networks based on maximizing a network utility function. A significant advance in recent years is the observation that a decomposition of the Lagrangian suggests an approach where transmissions are scheduled to minimize backpressure. However, a satisfactory Medium Access Control (MAC) protocol that can realize such a scheduling algorithm is notably missing, and that is the goal of this paper. We present a candidate random access MAC protocol that extends an existing algorithm to calculate the access probabilities. We also consider the online adaptation of access probabilities using local information about queue lengths and active links. In addition, we also modify the backpressure algorithm itself, by incorporating a minimum hop bias to alleviate the inherent problem of routing loops. We have implemented a general purpose simulation framework to study the comparative performance of network management protocols for congestion control, routing, MAC, and their cross-layer interaction. Using this, we compare the performance of our scheme with the leading schemes.

Original languageEnglish (US)
Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2350-2355
Number of pages6
ISBN (Print)9781424431243
DOIs
StatePublished - 2008
Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
Duration: Dec 9 2008Dec 11 2008

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other47th IEEE Conference on Decision and Control, CDC 2008
Country/TerritoryMexico
CityCancun
Period12/9/0812/11/08

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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