Optimized cognitive radio network crn using genetic algorithm and artificial bee colony algorithm

Abstract

In the present communication scenario, a solution that can overcome the scarcity of spectrum is Cognitive Radio (CR). To know how much spectrum the Primary User (PU) has occupied, a very important activity used by the CR is spectrum sensing. For sensing the spectrum hole, there are many techniques that have been suggested in literatures. A spectrum pool is formed by the CR user; it comprises all the spectrum holes that are in the spectrum range; it then chooses a suitable one for its future use. Using appropriate spectrum sharing policy, there can be an increase in the channel capacity. A tough issue in Cognitive Radio Network (CRN) is the spectrum prediction; there are many sub topics that are involved like the channel status prediction and the prediction of PU activity, radio environment and transmission rate. One mechanism for topology management is referred to as clustering. This organizes the nodes in logical sets so that the performance of the network is enhanced. The objectives of clustering include supporting collaborative tasks like channel access and channel sensing alongside network stability as well as scalability which is critical for CR functioning. The channel availability being dynamic, it changes with time as well as location, thus novel clustering algorithms must be formulated to tackle these issues that are inherent to CRs. This work proposes a lowest ID clustering algorithm which chooses a certain node as the cluster leader which has the lowest ID. This leader is referred to as Cluster Head (CH). The other algorithm that selects a node having the highest number of neighboring nodes as the CH is referred to as the maximum node degree clustering algorithm. newline