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Markovian Model Based Channel Allocation in Cognitive Radio Networks

Cognitive radio can be considered as an enabling technology to utilize the white spaces through efficient spectrum sharing techniques. Through the Shanon capacity formula, it is clear that channel capacity is crucial for communication when licensed and unlicensed users share the channels. Further, to address the channel capacity, signal to interference plus noise ratio (SINR) plays an important role for channel allocation as it provides the bands for the channel capacity. In this paper, the concept of expected SINR is introduced, leading to a novel approach for channel allocation in cognitive radio networks based on SINR using the Markov chain. The proposed scheme is validated by simulations, showing an improvement of 13% in channel allocation compared to the SINR-based channel allocation approach.