A novel channel access algorithm based on clusters and MAB model in cognitive vehicular network

Considering the cognitive channel access problem of vehicle nodes in cognitive vehicular networks with heavy traffic environment,a channel access algorithm called clusters-UCB which based on clusters and MAB model was proposed.The cooperation of cluster members could improve perception accuracy and enhance the learning speed.And using improved multi-user UCB algorithm,cluster heads could quickly search out the optimal channel in a distributed way,which could make the network asymptotically achieve the optimal slot throughput.Simulation results show that with respect to UCB algorithm and ε-greedy algorithm,the regret of the proposed algorithm is lower and the speed of approaching logarithmic form is faster.What's more,clusters-UCB can effectively reduce the number of collisions when clusters access the cognitive channels,ensuring the fairness of the channel access and achieving better slot throughput.