EWA Selection Strategy with Channel Handoff Scheme in Cognitive Radio
Experience Weighted Attraction (EWA) learning allows Cognitive Radio (CR) to learn radio environment communication channel characteristics online. An improved EWA selection strategy based on channel switch mechanism is proposed in this paper to meet CR application's practical requirement. By accumulating history channel experience, it can predict, select and change the current optimal communication channel, dynamic ensure the quality of communication links and finally reduce system communication outage probability. Sensitivity and stability verification is conducted by focusing on the comparison with Q learning algorithm after establishment of a wireless simulation space with time slot sequences that extremely imitates real CR wireless environments.
Wirel. Pers. Commun.