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Experience cooperative sharing in cross-layer cognitive radio for real-time multimedia communication

Published: 26 October 2011 Publication History

Abstract

The central problem in cognitive radio (CR) is how to adapt, given a wireless environment, so as to operate while meeting the constraints of a primary incumbent in a spectrum band. Adaptation for transmission of real-time multimedia lends itself to a cross-layer approach. This paper discusses a cross-layer CR scheme for real-time multimedia transmission that jointly adapt transmit bit rate and source-channel coding when sharing the spectrum with a primary network through DS-CDMA. While a cross-layer approach results in a notable reduction in distortion and congestion rate, it increases the complexity of the algorithm. This issue is addressed by introducing a novel cross-layer cooperative scheme that without performance loss reduces by as much as thirty times the number of iterations needed to achieve convergence by the cognitive algorithm. This scheme extends to a cross-layer setting, the docitive paradigm, by having secondary nodes share with a newcomer node their learned awareness of the environment and the associated results for actions.

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Cited By

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  • (2019)Cross Layer Routing in Cognitive Radio Networks using Deep Reinforcement Learning2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885918(1-6)Online publication date: Apr-2019
  • (2016)A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2016.253992318:3(1717-1757)Online publication date: Nov-2017
  • (2013)Cross layer design for equal and unequal loss protection frameworks in cognitive radio networksComputers and Electrical Engineering10.1016/j.compeleceng.2012.12.01939:2(571-581)Online publication date: 1-Feb-2013

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  1. Experience cooperative sharing in cross-layer cognitive radio for real-time multimedia communication

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    cover image ACM Other conferences
    CogART '11: Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
    October 2011
    372 pages
    ISBN:9781450309127
    DOI:10.1145/2093256
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Universitat Pompeu Fabra
    • IEEE
    • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
    • River Publishers: River Publishers
    • CTTC: Technological Center for Telecommunications of Catalonia
    • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 26 October 2011

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    • (2019)Cross Layer Routing in Cognitive Radio Networks using Deep Reinforcement Learning2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885918(1-6)Online publication date: Apr-2019
    • (2016)A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2016.253992318:3(1717-1757)Online publication date: Nov-2017
    • (2013)Cross layer design for equal and unequal loss protection frameworks in cognitive radio networksComputers and Electrical Engineering10.1016/j.compeleceng.2012.12.01939:2(571-581)Online publication date: 1-Feb-2013

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