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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Published in:2019 International Conference on Information Technology (ICIT) pp. 47 - 52
Main Authors: Satapathy, Shaswat, Singh, Shivani, Mishra, Debani Prasad
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2019
Subjects:
Cognitive-radio-networks
e-greedy
Multi-armed-bandit
reward
UCB
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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.
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
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  givenname: Shivani
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  organization: Department of Computer Science, IIIT Bhubaneswar, India
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  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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StartPage 47
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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