Revenue maximization in a CRN using Real Coded Genetic Algorithm

In this paper we consider a cognitive radio network consisting of primary users (PUs) and a set of secondary users (SUs). The spectrum has been divided into channels using frequency division multiple access (FDMA). The PUs are licensed to use the channels. When the channels are idle, i.e., not used by the PUs then PUs lease the vacant spectrum for earning revenue. While the SUs bid for the channels the PUs selects the purchaser offering the highest of all bid values. The main objective is to benefit the seller. Here, using Real Coded Genetic Algorithm (RCGA) we solve the above mentioned single objective function. We also optimize the problem using differential evolution (DE) algorithm and make a comparison of fitness values with RCGA.