MAC protocol selection based on machine learning in cognitive radio networks

The cognitive radio network is intelligent in adapting to the dynamic network environment, and it also provides a new way to solve the application limitation of nowadays MAC protocols. In this paper, an adaptive MAC protocol selection scheme based on machine learning is proposed for cognitive radio networks. This scheme can combine the respective advantages of the competitive protocols and non-competitive protocols according to the network loads, and help the network nodes to select the MAC protocol that best suits the current network circumstance. Simulation results validate our proposal that it always select the appropriate MAC protocol that best suits the network circumstance. It is also proven that, the accuracy of the proposed MAC protocol selection strategy far surpasses that of existing work.