Statistical routing for multihop wireless cognitive networks

Emiliano Dall'Anese, Georgios B Giannakis

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

To account for the randomness of propagation channels and interference levels in hierarchical spectrum sharing, a novel approach to multihop routing is introduced for cognitive random access networks, whereby packets are randomly routed according to outage probabilities. Leveraging channel and interference level statistics, the resultant cross-layer optimization framework provides optimal routes, transmission probabilities, and transmit-powers, thus enabling cognizant adaptation of routing, medium access, and physical layer parameters to the propagation environment. The associated optimization problem is non-convex, and hence hard to solve in general. Nevertheless, a successive convex approximation approach is adopted to efficiently find a Karush-Kuhn-Tucker solution. Augmented Lagrangian and primal decomposition methods are employed to develop a distributed algorithm, which also lends itself to online implementation. Enticingly, the fresh look advocated here permeates benefits also to conventional multihop wireless networks in the presence of channel uncertainty.

Original languageEnglish (US)
Article number6331688
Pages (from-to)1983-1993
Number of pages11
JournalIEEE Journal on Selected Areas in Communications
Volume30
Issue number10
DOIs
StatePublished - Oct 29 2012

Keywords

  • Routing
  • channel uncertainty
  • cognitive radios
  • convex approximation
  • cross-layer optimization
  • distributed computation
  • multihop wireless networks
  • random access

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