An ordinal optimization like GA for improved OFDMA system carrier allocations

Different intelligent techniques have been proposed to solve the downlink resource allocation in orthogonal frequency division multiple access (OFDMA)-based networks. These include mathematical optimization, game theory and heuristic algorithms. In an attempt to improve the performance of traditional genetic algorithm (GA), we propose a novel improved GA (IGA) which uses a new mutation operator as well as adopts concepts of ordinal optimization (OO) for selecting GA parameters such as the initial population and the stopping criteria in a manner that meets the quality of service requirements for different types of OFDMA users despite the large search space. Performance of IGA over different fitness functions published in literature, shows improved resource allocation results over regular GA and motivates follow on research.