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QoS driven Channel Selection Algorithm for Opportunistic Spectrum Access

Navikummar Modi 1 Philippe Mary 2 Christophe Moy 1
IETR - Institut d'Électronique et des Technologies du numéRique
Abstract : In this paper, we propose a novel machine learning algorithm called quality of service upper confidence bound (QoS-UCB) for the opportunistic spectrum access (OSA) scenario. The proposed algorithm selects an optimal channel in terms of occupancy and quality, e.g. signal to noise ratio (SNR) for transmission. It allows secondary users (SU) to learn the spectrum not only on the vacancy point of view but also on the expected transmission quality by selecting two distinguishable exploration coefficients. Our contribution is threefold: i) We propose a new learning algorithm achieving optimal trade-off between exploration and exploitation when the OSA scenario is modeled as a rested Markov multi-armed bandit (MAB) problem. ii) We state that under mild conditions on the state transition probabilities of Markov chains, the regret of the QoS-UCB policy behaves logarithmically over time. iii) We numerically compare our scheme with an existing UCB1 in OSA context and also show that the QoS-UCB outperforms traditional UCB1 in terms regret.
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Submitted on : Tuesday, January 5, 2016 - 6:19:03 PM
Last modification on : Monday, October 5, 2020 - 9:50:30 AM



Navikummar Modi, Philippe Mary, Christophe Moy. QoS driven Channel Selection Algorithm for Opportunistic Spectrum Access. IEEE International Workshop on Advances in Software Defined Radio Access Networks and Context-aware Cognitive Networks 2015, Dec 2015, San Diego, United States. ⟨10.1109/GLOCOMW.2015.7413996⟩. ⟨hal-01251221⟩



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