MCS: A Distributed Multi-User Channel Selection Algorithm for Cognitive Radio Networks
Multi-arm bandit (MAB) theory have recently been in use to plan choice issues with exploration-exploitation tradeoff. Dynamic channel assignment in cognitive radio (CR) systems is one of critical applications. In this work, we quickly outline MAB issues with its conceivable applications to cognitive...
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| Vydáno v: | 2019 International Conference on Information Technology (ICIT) s. 47 - 52 |
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01.12.2019
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| Abstract | Multi-arm bandit (MAB) theory have recently been in use to plan choice issues with exploration-exploitation tradeoff. Dynamic channel assignment in cognitive radio (CR) systems is one of critical applications. In this work, we quickly outline MAB issues with its conceivable applications to cognitive radio systems. We first demonstrate a MAB issue where a solitary client either explores a channel to assemble data to improve its present scenario, or exploits the channel officially chosen dependent on the data that it has as of now gathered. At that point we thought about a completely disseminated framework, where there's no involvement of a centralized element, with the qualities of each channel being unidentified and may differ for every client. We at last propose a MCS (Multi-User Channel Selection) Algorithm and perform simulation based on multi-player multiarmed bandit approach for dynamic cognitive Ad-hoc networks and compare the result with the existing Musical Chair (MC) approach. An extensive simulation is performed which proves that the proposed MCS algorithm is better than the existing approach. |
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| AbstractList | Multi-arm bandit (MAB) theory have recently been in use to plan choice issues with exploration-exploitation tradeoff. Dynamic channel assignment in cognitive radio (CR) systems is one of critical applications. In this work, we quickly outline MAB issues with its conceivable applications to cognitive radio systems. We first demonstrate a MAB issue where a solitary client either explores a channel to assemble data to improve its present scenario, or exploits the channel officially chosen dependent on the data that it has as of now gathered. At that point we thought about a completely disseminated framework, where there's no involvement of a centralized element, with the qualities of each channel being unidentified and may differ for every client. We at last propose a MCS (Multi-User Channel Selection) Algorithm and perform simulation based on multi-player multiarmed bandit approach for dynamic cognitive Ad-hoc networks and compare the result with the existing Musical Chair (MC) approach. An extensive simulation is performed which proves that the proposed MCS algorithm is better than the existing approach. |
| Author | Singh, Shivani Mishra, Debani Prasad Satapathy, Shaswat |
| Author_xml | – sequence: 1 givenname: Shaswat surname: Satapathy fullname: Satapathy, Shaswat organization: Department of Computer Science, IIIT Bhubaneswar, India – sequence: 2 givenname: Shivani surname: Singh fullname: Singh, Shivani organization: Department of Computer Science, IIIT Bhubaneswar, India – sequence: 3 givenname: Debani Prasad surname: Mishra fullname: Mishra, Debani Prasad organization: Department of Electrical Engineering, IIIT Bhubaneswar, India |
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| Snippet | Multi-arm bandit (MAB) theory have recently been in use to plan choice issues with exploration-exploitation tradeoff. Dynamic channel assignment in cognitive... |
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| SubjectTerms | Cognitive-radio-networks e-greedy Multi-armed-bandit reward UCB |
| Title | MCS: A Distributed Multi-User Channel Selection Algorithm for Cognitive Radio Networks |
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