Gaussian Process Reinforcement Learning for Fast Opportunistic Spectrum Access

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 alpha-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.