7th International Conference on Communications and Networking in China

Research Article

Intelligent Decision Strategy for Adaptive Resource Management in Wireless Cognitive Network

  • @INPROCEEDINGS{10.1109/ChinaCom.2012.6417459,
        author={ZhenYong Wang and Zhenbang Wang},
        title={Intelligent Decision Strategy for Adaptive Resource Management in Wireless Cognitive Network},
        proceedings={7th International Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={9},
        keywords={wireless cognitive network resource management end-to-end qos},
        doi={10.1109/ChinaCom.2012.6417459}
    }
    
  • ZhenYong Wang
    Zhenbang Wang
    Year: 2012
    Intelligent Decision Strategy for Adaptive Resource Management in Wireless Cognitive Network
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2012.6417459
ZhenYong Wang1,*, Zhenbang Wang1
  • 1: Harbin Institute of Technology
*Contact email: zywang@hit.edu.cn

Abstract

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.