2019
DOI: 10.1109/access.2019.2948935
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Joint Channel Selection and Data Scheduling in HF Jamming Environment: An Interference-Aware Reinforcement Learning Approach

Abstract: In this paper, we study the joint problem of multichannel selection and data scheduling for high-frequency (HF) communication under jamming environment. Prior anti-jamming work mainly discussed fixed transmission time and considered saturated scenarios in which the agent always had packets to transmit. But HF dynamic spectrum environment and time-varying communication demand make traditional anti-jamming methods ineffective. To cope with above challenge, dynamic transmission time and packet scheduling are cons… Show more

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Cited by 3 publications
(1 citation statement)
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“…ϵ-greedy learning algorithm is a low-complexity RL algorithm that can balance the tradeoff between exploration and exploitation through the adjustment of ϵ. In [27], Li et al proposed an interference-aware RL algorithm to solve the joint problem of multichannel selection and data scheduling. In [28], Talekar and Terdal proposed a solution for optimal channel selection and routing and applied RL to select the best channel for routing.…”
Section: Introductionmentioning
confidence: 99%
“…ϵ-greedy learning algorithm is a low-complexity RL algorithm that can balance the tradeoff between exploration and exploitation through the adjustment of ϵ. In [27], Li et al proposed an interference-aware RL algorithm to solve the joint problem of multichannel selection and data scheduling. In [28], Talekar and Terdal proposed a solution for optimal channel selection and routing and applied RL to select the best channel for routing.…”
Section: Introductionmentioning
confidence: 99%