Intelligent decision strategy for adaptive resource management in wireless cognitive network

With the development of cognitive radio technology, more surrounding cognition information is available to make wireless cognitive network self-adaptive to dynamic conditions of wireless networks. However, due to increasing cognition information, it is an interesting problem to achieve optimal strategies in numbers of adjustable parameters for relatively wide adjustable capacity in resource management of wireless cognitive networks. In this paper, an intelligent decision strategy with learning-reasoning mechanism and decision-evaluation process is proposed to classify, select and optimize the large adjustable parameters for network traffic end-to-end QoS requirements in wireless cognitive networks. Non-Dominated Sorting Genetic Algorithm and Fuzzy Decision Making are introduced in learning-reasoning strategy to abstract cognition information to “knowledge”, and save the “knowledge” into history-case database. Complex Combinatorial Optimization Probability method is used in decision-evaluation process to search for optimal solution of resource management in wireless cognitive networks. By simulations, the performances show that the proposed intelligent decision strategy can guarantee end-to-end QoS in dynamic conditions of wireless cognitive networks.