Maximization of minimal throughput using genetic algorithm in MIMO underlay cognitive radio networks

In cognitive radio networks (CRNs), the most critical issue is increasing the throughput of secondary users (SUs) while assuring the quality of service (QoS) of primary users (PUs). In this paper, a proposed optimal power allocation scheme using genetic algorithm (GA) is suggested for a multiple-input-multiple-output (MIMO) system in CRN. This scheme is used to maximize the secondary throughput under interference constraints in a system model of multiple SU pairs coexisting with multiple PU pairs in an underlay spectrum sharing network. For the sake of comparison, the minimal throughput among all SUs is compared with other power allocation schemes, namely, maximum-minimum-throughput-based power assignment (MMTPA) and equal power assignment (EPA). Simulation results show that, our proposed scheme gives the maxmin secondary throughput among all other stated schemes but with additional computational complexity which is reduced by reducing the population size. Unlike MMTPA, our proposed approach maximizes the throughput of all SUs not only the minimal throughput among all SUs.