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Abstract

Abstract---It is important to understand the architecture of Non linear systems and the components that are responsible for introducing nonlinear distortion. In order to evaluate the effect of nonlinear distortion on system performance the modeling and simulation is the requirement of present scenario of modern wireless communication systems. We have presented the orthogonalization of the behavioral model is beneficial for the better prediction of nonlinear distortion and extracts the uncorrelated component of the nonlinear output that is responsible for the degradation of system performance. Orthogonalization of power series nonlinear model is performed. Nonlinearity in wireless communication system is mainly introduced by nonlinear devices which are organized in the design of the transmitter and the receiver. In order to figure out the effect of nonlinear distortion on system performance, it is important to understand the architecture of these systems and the components that are responsible for introducing nonlinear distortion. It is also important to evaluate the modeling and simulation of these systems. Modeling and Simulation is an important step for an efficient design of modern wireless communication systems. Nonlinearity and nonlinear distortion is introduced by nonlinear behavior of nonlinear devices and their relationship to the performance of wireless communication system will be discussed. The main nonlinear devices that produce nonlinear distortion are power amplifiers, mixers in wireless transmitters and low noise amplifiers in wireless receivers [1]. Keywords---DownlinkA traditional method is to first linearize the system then perform model order reduction on the linear system. But it is well known that this method cannot give good approximation results. The ''quadratic reduction method'' presented is based on the idea of approximating the nonlinear system by a quadratic system through dropping the terms of more than two degree in Taylor expansion of the nonlinear term [1] . The bilinearization method proposed in [2] first approximating the original nonlinear system by a bilinear system, then doing order reduction on this approximate bilinear system by using the Volterra series representation of bilinear system in control theory. Variational equation model order reduction method in [3] is based on the variational equation theory in [1]. By this method, the original nonlinear system is changed into several linear systems, then order reduction is done on each linear system. Another reduction method is proposed in [4], which uses the derivatives of the state variable to form an orthogonal projection matrix, and then reduce the original system by projection with this orthogonal matrix. There are also some other nonlinear model order reduction methods such as the trajectory piecewise linear method in [5]. In this paper another method to achieve linearization is presented as Orthogonalized Nonlinear Model for nonlinear system. II NONLINEAR MODEL FOR ORTHOGONALIZATIONAs studied in recent years, the ortho...