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Dynamic Rate and Channel Selection in Cognitive Radio Systems.

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Title: Dynamic Rate and Channel Selection in Cognitive Radio Systems.
Authors: Combes, Richard, Proutiere, Alexandre
Source: IEEE Journal on Selected Areas in Communications. May2015, Vol. 33 Issue 5, p910-921. 12p.
Subjects: Cognitive radio, Bandwidth allocation, Machine learning, Radio transmitters & transmission, Packet switching, Mathematical optimization
Abstract: In this paper, we investigate dynamic channel and rate selection in cognitive radio systems that exploit a large number of channels free from primary users. In such systems, transmitters may rapidly change the selected (channel, rate) pair to opportunistically learn and track the pair offering the highest throughput. We formulate the problem of sequential channel and rate selection as an online optimization problem and show its equivalence to a structured multiarmed-bandit problem. The structure stems from inherent properties of the achieved throughput as a function of the selected channel and rate. We derive fundamental performance limits satisfied by any channel and rate adaptation algorithm and propose algorithms that achieve (or approach) these limits. In turn, the proposed algorithms optimally exploit the inherent structure of the throughput. We illustrate the efficiency of our algorithms using both test-bed and simulation experiments, in both stationary and nonstationary radio environments. In stationary environments, the packet successful transmission probabilities at the various channel and rate pairs do not evolve over time, whereas in nonstationary environments, they may evolve. In practical scenarios, the proposed algorithms are able to track the best channel and rate quite accurately without the need for any explicit measurement of and feedback on the quality of the various channels. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Journal on Selected Areas in Communications is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Dynamic Rate and Channel Selection in Cognitive Radio Systems.
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  Data: <searchLink fieldCode="AR" term="%22Combes%2C+Richard%22">Combes, Richard</searchLink><br /><searchLink fieldCode="AR" term="%22Proutiere%2C+Alexandre%22">Proutiere, Alexandre</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+radio%22">Cognitive radio</searchLink><br /><searchLink fieldCode="DE" term="%22Bandwidth+allocation%22">Bandwidth allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+transmitters+%26+transmission%22">Radio transmitters & transmission</searchLink><br /><searchLink fieldCode="DE" term="%22Packet+switching%22">Packet switching</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: In this paper, we investigate dynamic channel and rate selection in cognitive radio systems that exploit a large number of channels free from primary users. In such systems, transmitters may rapidly change the selected (channel, rate) pair to opportunistically learn and track the pair offering the highest throughput. We formulate the problem of sequential channel and rate selection as an online optimization problem and show its equivalence to a <bold>structured</bold> multiarmed-bandit problem. The structure stems from inherent properties of the achieved throughput as a function of the selected channel and rate. We derive fundamental performance limits satisfied by <bold>any</bold> channel and rate adaptation algorithm and propose algorithms that achieve (or approach) these limits. In turn, the proposed algorithms optimally exploit the inherent structure of the throughput. We illustrate the efficiency of our algorithms using both test-bed and simulation experiments, in both stationary and nonstationary radio environments. In stationary environments, the packet successful transmission probabilities at the various channel and rate pairs do not evolve over time, whereas in nonstationary environments, they may evolve. In practical scenarios, the proposed algorithms are able to track the best channel and rate quite accurately without the need for any explicit measurement of and feedback on the quality of the various channels. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Journal on Selected Areas in Communications is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        PageCount: 12
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      – SubjectFull: Cognitive radio
        Type: general
      – SubjectFull: Bandwidth allocation
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Radio transmitters & transmission
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      – SubjectFull: Packet switching
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      – SubjectFull: Mathematical optimization
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              Text: May2015
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