GA-CSS: Genetic Algorithm Based Control Channel Selection Scheme for Cognitive Radio Networks

The problem of spectrum scarcity has given the birth to the cognitive radio network (CRN) that offers innovative advantages over fixed spectrum devices. Moreover, it also provides the solution to cater the congestion among the low power devices that are operating in the ISM band. In CRNs, the cognitive radio (CR) dynamically selects the available channel and then adapts itself according to the characteristics of the channel. However, the random appearance of the primary radio (PR) degrades the transmission of a CR which needs to be addressed properly in order to have real world application of the CRN. In this paper, we propose a genetic algorithm based control-channel selection scheme (GA-CSS) for centralized CRN that considers the random arrival of the PRs, data rate and bit error rate (BER) to select the optimal control-channel. Simulation results show that our proposed scheme outperforms the traditional schemes in terms of control data transmission time and collision rate with PRs. Moreover, the BER is under the tolerable limit.