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• W3010591070 abstract "Opportunistic spectrum access (OSA) is envisioned to support the spectrum demand of future- generation wireless networks. In practice, primary channels are usually correlated and network dynamics is unknown a-priori. This entails a great challenge on sensing policy design, and conventional model-based methods are generally inapplicable. In this paper, we propose a novel Gaussian process reinforcement learning (GPRL) based model-free solution to enable the fast sensing policy optimization in OSA. In essence, Gaussian process is embedded in RL framework as a Q-function approximator to efficiently utilize the past learning experience. A novel kernel function is first tailor designed to measure spectrum data correlation. Then a covariance-based exploration strategy is developed to strike a better trade-off between the exploration and exploitation in RL. Our simulation results show that the proposed GPRL can obtain a near-optimal policy with significantly reduced learning period compared with deep reinforcement learning." @default.
• W3010591070 created "2020-03-13" @default.
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• W3010591070 date "2019-12-01" @default.
• W3010591070 modified "2023-10-09" @default.
• W3010591070 title "Gaussian Process Reinforcement Learning for Fast Opportunistic Spectrum Access" @default.
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• W3010591070 doi "https://doi.org/10.1109/globecom38437.2019.9013555" @default.
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