RF power amplifiers (PA) are a major source of nonlinearity in a communication system. Accurate behavioral models are indispensable for PA linearization. To describe nonlinear characteristics of power amplifiers, a support vector machine (SVM) based modeling method is presented. The kernel approach and duality theory are employed to train the PA model. Simulation results show that the proposed model provides more accurate prediction of PA output signal compared with classic neural network models.
Phosphorene, one of the graphene counterparts, is believed to have promising potential to be utilized in nanoelectronics due to its significant properties. Phosphorene has a nonplanar puckered structure with high anisotropy, which enables the elastic strain or external field to tune its electronic structure. In this work, we propose a nanodevice model based on an armchair phosphorene nanoribbon (APNR) with normal-metal electrodes and study the tuning effect of elastic strain and electric field on the electronic transport properties. We first confirm that the APNR can be driven to be of metallic conduction with linear dispersion around the Fermi level, by applying a critical compressive strain. After applying a perpendicular electric field, the APNR turns out to be a band insulator. Furthermore, we calculate the dc conductance and density of states (DOS) of the nanodevice, where the APNR is connected to normal-metal electrodes. The numerical results show that in the absence of an electric field, the nanodevice possesses peak values of conductance and DOS at the Fermi level. Once the electric field is applied, a gap emerges around the Fermi level in the conductance, which suggests that the nanodevice is turned off by the external electric field. Our investigation on the present system could be useful in the development of a field-effect nanodevice based on monolayer phosphorene.
There has been intensive research in memoryless nonlinear behavioral modeling of power amplifiers (PAs). But in broadband communication systems, memory effects of PAs can no longer be ignored and traditional memoryless model cannot accurately characterize the input-output relationship of PAs. In order to treat memory effects and reduce the complexity of general Volterra model, a new behavioral PA model based on modified Volterra series is proposed. Since the characteristics of power amplifiers change during transmission time, a recursive least squares algorithm with size-fixed observation matrices is developed to update the parameters of the PA model. This identification algorithm, which uses only the latest sample data to identify the parameters, can decrease computational complexity and data storage space needed for identification. Simulations are carried out to validate the performances of the proposed PA model and identification algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.