Learning interference strategies in cognitive ARQ networks

Sina Firouzabadi, Marco Levorato, Daniel O'Neill, Andrea Goldsmith

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

10 Scopus citations

Abstract

Cognitive radios, which enable the coexistence on the same bandwidth of licensed primary and unlicensed secondary users, have the potential for dramatically increasing the efficiency of wireless networks. In this paper, we propose an on line learning algorithm to optimize the transmission strategy of secondary users in interference mitigation scenarios, where the secondary users are allowed to superimpose their transmission onto those of the primary users. Due to practical limitations, the secondary users have access to only a fraction of the current state of the primary users' network. Therefore, the strategy of the secondary users is defined on a reduced state space. Numerical results show that the proposed practical learning algorithm operates close to the performance of the system under full knowledge.

Original languageEnglish (US)
Title of host publication2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
DOIs
StatePublished - 2010
Externally publishedYes
Event53rd IEEE Global Communications Conference, GLOBECOM 2010 - Miami, FL, United States
Duration: Dec 6 2010Dec 10 2010

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference

Other

Other53rd IEEE Global Communications Conference, GLOBECOM 2010
CountryUnited States
CityMiami, FL
Period12/6/1012/10/10

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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