A Modified Genetic Algorithm for Resource Allocation in Cognitive Radio Networks

Abstract

Cognitive radio network (CRN) has a capability to sense the conditions of their operating environment. However, the resources allocation scheme is still inefficient because it is generated randomly and can lead to interference among the users. In this paper, we propose a modified genetic algorithm as a method for resource allocation in the cognitive radio network. In this work, the chromosome represents the channel interface index. The objective is to find the optimal resource allocation scheme of the nodes in the network in order to minimize the interference and maximize the network throughput. We have modified an encoding scheme and the fitness function of GA to assign the best channel combination of the cognitive radio network. The simulation results showed that the allocation channel using modified GA is capable of improving the network throughput.