Study On Nonlinear Modeling Techniques Of Single Band ROF Front Transmission System

In order to satisfy the demand of high speed data communication, the Radio over fiber(ROF) technology has emerged as that times. ROF with high broadband transmission, large capacity, strong anti-interference, affordable, wireless features convenient access, received widespread attention, and the research and application of the technology. However, there is a strong dynamic nonlinear distortion in the ROF link under the action of large dynamic range OFDM modulation signal, and the power efficiency and communication quality of the system are affected by ROF system. In addition, radio frequency power amplifier is the key equipment of wireless communication system, and it also has strong static and dynamic nonlinear distortion. Therefore, whether it is a ROF link or the RF power amplifier, there is a strong nonlinear distortion, both need to eliminate and suppress the nonlinear distortion.The traditional method is to separate the ROF link and power amplifier to solve nonlinear distortion, which makes the system structure complex. Therefore, it is urgent to study the nonlinear modeling and linearization of ROF link and power amplifier as a whole.In this paper, some power amplifier linearization technology and ROF technology are introduced. Through the experimental simulation of communication system, including building a ROF link and power amplifier. On the nonlinear ROF link, and the nonlinearity of the power amplifier is studied and analyzed. Through the existing linearization technology, application Matlab and Toolkit software use digital predistortion models modeling, through model comparison between the number of coefficients, training data complexity, modeling accuracy (NMSE) to compare modeling results.The results show that the nonlinear modeling of LTE signal using parallel box model suitable for OFDM such a large dynamic range. In the nonlinear model of ROF link system, the precision is improved, and the polynomial model of generalized . memory polynomial is reduced by 42% in the number of coefficients, which can reduce the complexity of the system.