Cross-layer optimization and receiver localization for cognitive networks using interference tweets

Antonio G. Marques, Emiliano Dall'Anese, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


A cross-layer resource allocation scheme for underlay multi-hop cognitive radio networks is formulated, in the presence of uncertain propagation gains and locations of primary users (PUs). Secondary network design variables are optimized under long-term probability-of-interference constraints, by exploiting channel statistics and maps that pinpoint areas where PU receivers are likely to reside. These maps are tracked using a Bayesian approach, based on 1-bit messages-here refereed to as »interference tweet»-broadcasted by the PU system whenever a communication disruption occurs due to interference. Although nonconvex, the problem has zero duality gap, and it is optimally solved using a Lagrangian dual approach. Numerical experiments demonstrate the ability of the proposed scheme to localize PU receivers, as well as the performance gains enabled by this minimal primary-secondary interplay.

Original languageEnglish (US)
Article number6746253
Pages (from-to)641-653
Number of pages13
JournalIEEE Journal on Selected Areas in Communications
Issue number3
StatePublished - Mar 2014


  • Bayesian estimation
  • Cognitive radio networks
  • Lagrange dual
  • cross-layer optimization
  • receiver localization


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