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Abstract
Spectrum efficiency (SE) and energy efficiency (EE) are two key design objectives in future wireless communication networks. In traditional cognitive radio (CR) network, a secondary user (SU) performs spectrum sensing at the start of each time slot, which may waste energy and the transmission opportunities for the SU. In order to improve the SE and EE, a CR network using energy harvesting techniques is considered. A partially observable Markov decision process (MDP) framework and an MDP framework are used to determine the optimal spectrum sensing energy, the transmit energy and spectrum sensing interval to maximize the long-term average weighed sum of the throughput of the SU and the interference caused to the PU. Simulation results show that the proposed algorithm can significantly increase the average throughput of the SU and EE compared with the traditional spectrum scheme, at the cost of the small but tolerable interference caused to the PU.
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