Cross-layer parameters reconfiguration in cognitive radio networks using ant colony optimization

As one of the essential characteristics for CRN, the cognitive reconfiguration can automatically adjust the cross-layer parameters to meet the user requirements, realize interoperability between heterogeneous networks and adapt to the time-varying environment. However, the cross-layer parameters reconfiguration implementation is still challenging due to its need for complex environment cognition and multi-objects optimization. In this direction, ant colony optimization (ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the adaption. The aim of this paper is to present a generic cross-layer parameters reconfiguration framework including indispensable function entities for autonomous reconfiguration decision making with regard to the multiple and complex objectives. Finally, numerous results prove the effective performance improvements of ACO based reconfiguration solution in CRN.