Statistical routing for cognitive random access networks

Emiliano Dall'Anese, Georgios B. Giannakis

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

Abstract

A novel approach to multi-hop routing for cognitive random access is developed under channel gain uncertainty constraints. Motivated by the inherent randomness of the propagation medium, the novel routing strategy leverages pairwise decoding probabilities to randomly route packets to neighboring nodes. The resultant cross-layer optimization framework not only provides optimal routes in a well-defined sense, but also yields transmission probabilities and transmit-powers, thus enabling cognizant adaptation of networking, medium access, and physical layer parameters to the operational environment. The relevant optimization problem is non-convex and hence hard to solve in general. Nevertheless, a successive convex approximation approach is employed to efficiently find a Karush-Kuhn-Tucker solution. Enticingly, the fresh look advocated here permeates benefits also to conventional multi-hop random access networks in the presence of channel uncertainty.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2957-2960
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Routing
  • cross-layer optimization
  • multi-hop wireless networks
  • random access
  • successive convex approximation

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