Improved Cuckoo Algorithm for Spectrum Allocation in Cognitive Vehicular Network

In traditional cognitive wireless network, most studies on spectrum allocation are on the basis of static network topology. However, the vehicles in the cognitive vehicular network have high-speed mobility and the network topology changes frequently, which makes spectrum allocation more challenging. In this paper, the above factors are considered and a connection between the remaining available time of the primary user and the time required by the cognitive vehicle is established in our spectrum allocation model. To maximize network throughput under the heterogeneous spectrum environment, a rapid convergence algorithm that adapts to a dynamic cognitive vehicular network environment for solving this problem is necessary. Therefore, the improved adaptive binary cuckoo search (IABCS) algorithm that incorporates the simplex method into the adaptive binary cuckoo algorithm is proposed. The experimental results indicate that comparing with the original standard cuckoo search $(CS)$ algorithm and the improved particle swarm optimization (PSO) algorithm, the spectrum allocation method based on the improved adaptive cuckoo algorithm converges faster and achieves higher throughput.