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- W2436547160 abstract "AbstractEfficient spectrum sensing is an important requirement for the success of the cognitive radio system. Presence of primary users over a specific band has to be monitored periodically in each time slot. Throughput of the system can be improved by sensing only those channels with higher probability of being idle. In this paper, we suggest a prediction-based spectrum sensing scheme and propose two simple and fast approaches based on Bayesian inference to predict the probability of a busy/idle next state. Further analysis is done to study the impact of various parameters associated with them.This channel prediction will help to select suitable channels for spectrum sensing from a rank list prepared based on the probability of channel being idle. It is seen that channel ranking using Bayesian approaches closely follow actual ranking. Proposed approaches are compared for their prediction performance and the computational complexity, with other approaches based on EWMA, HMM, and Neural Networks, which are..." @default.
- W2436547160 created "2016-06-24" @default.
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- W2436547160 date "2016-04-02" @default.
- W2436547160 modified "2023-09-22" @default.
- W2436547160 title "Bayesian analysis of spectrum occupancy prediction in cognitive radio" @default.
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- W2436547160 doi "https://doi.org/10.1080/23080477.2016.1182360" @default.
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