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In this study, an opportunistic spectrum access model with learning strategy is presented for cognitive radio system. Consider a hidden Markov model in learning process, where the ternary hypothesis testing scheme is proposed to perform sensing with the goodness of fit testing. By using a gradient method, the secondary user can estimate the channel patterns and keep up with the variations of the primary user activities. An opportunistic access channel capacity is introduced to evaluate the quality of service of the objective licensed channel. Also, a partially observable Markov decision process framework is presented to exploit spectrum holes. Further analysis shows that, unlike the binary hypothesis testing where the idle state is always protected, the idea of the proposed ternary hypothesis testing puts both the idle and busy state in the same position, which reflects the real state of the licensed channel more precisely. Simulation results indicate that the proposed ternary hypothesis testing scheme outperforms the conventional binary hypothesis testing for both the goodness of fit testing and the energy detection.