Dynamic Channel Assignment Using Ant Colony Optimization for Cognitive Radio Networks

Considering the inevitable trends for heterogeneous network convergence, Cognitive Radio Network (CRN) concept has been proposed with some essential characteristics to achieve adaptation and global end-to-end goals. This motivates a more flexible and effective dynamic channel assignment scheme which can utilize the licensed spectrum effectively through reusing idle licensed spectrum opportunistically. This paper focuses on the dynamic channel assignment which offers optimal resource allocation mechanism to satisfy the requirement of users and networks in transmission. Owing to the optimization problem of channel assignment is constituted as a nonlinear programming, we propose the use of Ant Colony Optimization (ACO) algorithm as a way to manage and assign channel resource dynamically in CRNs. The ACO, as an intelligent technique, has the capacity to solve the complex multi-objective optimization problem and simplify the computational process. Finally, the dynamic channel assignment algorithm is simulated, and the numerical results with detailed are analyzed.