Learning based channel allocation in Cognitive Radio Network

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The channel selection plays an important role for uninterrupted and efficient cognitive communication. In the cognitive radio network, a secondary user senses each channel one after another in different time slots. Based on comparative analysis of sensing results, the SU selects the best channel or the idle channel for cognitive communication. However, the reliable channel selection in real time may be affected severely due to the variation of the operational parameters. Therefore, learning based sensing and allocation mechanism has been incorporated in this paper to enhance channel sensing and allocation performance in the cognitive radio network

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