Artificial Mapping for Dynamic Resource Management of Cognitive Radio Networks
This paper sheds light on the artificial mapping schemes for adaptive network collaboration of cognitive radio networks. The analysis shows that the superiority of the DHTbased overlay is due to its link state aggregation property, which establishes global convergence for link state aggregation among a scalable number of nodes. Recognizing that fuzzy logic inference can better handle uncertainty, fuzziness, and incomplete information in node convergence report, FuzzyConvergence is developed as a novel approach to aggregate wireless node control with affordable message overload. The proposed Artificial Mapping Tree (AMT) built upon the new convergence scheme has achieved noticeably better performance than the state-of-theart proactive spectrum coordination with moderately increased network throughput for convergence validation.