Reconfiguration decision making in cognitive wireless network

Facing the challenges of dynamic adaptation capabilities in the time-varying environment of cognitive wireless networks (CWNs), we introduce reconfiguration capabilities that flexibly and dynamically adapt to changing wireless environments and service requirements. As an essential characteristic of CWNs, the cognitive reconfiguration can meet user requirements, realize interoperability between heterogeneous networks, make full use of radio resources and adapt to time-varying environments to achieve end-to-end requirements. However, the reconfiguration implementation is still challenging due to the need for complex environment cognition, multi-objective optimization, autonomic decision-making and end-to-end requirement extraction. As an intelligent technology for solving complex issues, we apply adaptive neuro-fuzzy inference system (ANFIS) techniques in this paper to address these challenges in cognitive reconfiguration for self-learning and optimal decision making based on multi-domain cognition results. Moreover, this paper designs a generic ANFIS cognitive reconfiguration system including three functional entities, which are the context management module, multi-domain database and ANFIS optimization module. Finally, numerous results prove the effective performance improvements of the ANFIS based reconfiguration solution in CWN for global end-to-end goals.