Abstract
In this paper, the dynamic control approaches for spectrum sensing are proposed, based on the theory that prediction is synonymous with data compression in computational learning. Firstly, a spectrum sensing sequence prediction scheme is proposed to reduce the spectrum sensing time and improve the throughput of secondary users. We use Ziv-Lempel data compression algorithm to design the prediction scheme, where spectrum band usage history is utilized. In addition, an iterative algorithm to find out the optimal number of spectrum bands allowed to sense is proposed, with the aim of maximizing the expected net reward of each secondary user in each time slot. Finally, extensive simulation results are shown to demonstrate the effectiveness of the proposed dynamic control approaches of spectrum sensing.
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