Interference Mitigation in Cognitive Radio Network Based on Grey Wolf Optimizer Algorithm

Abstract

Cognitive Radio Network (CNR) is a dynamic network where the users can adjust spectrum usage dynamically in accordance to the operational environment to minimize interference. However, it still has a major problem regarding the channel allocation used by the nodes. This problem exists because channel allocations are completely randomly generated so that they might cause interference to users on the same channel. To handle resource allocation problems in the CRN, the authors proposed a solution using the Grey Wolf Optimizer (GWO). This optimizer algorithm is an optimization included in the metaheuristic algorithm with the source of inspiration from the behavior of the gray wolf colony in hunting prey. In this job, Alpha serves as a prime candidate in finding the best channel. The ultimate goal of using this GWO optimization is to get the most optimal channel allocation scheme for each node in the cognitive radio network so that it has minimal interference and maximum network throughput. The authors have modified the fitness function and coding scheme of GWO to get the best share of resources from the CRN. From the simulations tested, the results showed that channel allocation using the GWO algorithm was able to increase throughput and reduce network interference.