Study On Predistortion Technique For Nonlinear Power Amplifier With Memory

The predistortion linearization technique is investigated for the nonlinear power amplifier with memory in communication system. Neural network and SVM(Support Vector Machine) are adopted to model the predistorter to linearize the nonlinear power amplifier with memory.We summarize the development trends of the modern communication system first, and then expound that it not only meets the realistic requirement but also agrees with the development direction of modern communication to study the predistortion technique for the nonlinear amplifier with memory.Separation predistortion method is proposed to linearize the power amplifier with special structure like Wiener. The method identifies the inverse systems of memory subsystem and nonlinear subsystem separately, and then putts them together to construct a Hammerstein system which is the accurate predistorter for the Wiener amplifier. Due to it converts an identifying problem for complex system into one for simple system, the method is benefit to both speeding up course of parameters identifying and acquiring predistorter model with great precision. If more efficient algorithms for the two simple systems are introduced in, the separation method will perform better.The direct learning structure is adopted to train neural network. For the input training samples are more close to the actual input signals in statistical features, the generalization capability of the neural network predistorter based on direct learning structure should be better. This is verified by simulation result.SVM is utilized by us to model the predistorter of the nonlinear amplifier with memory at first time so far as we know. Features of local kernel function, global kernel function and combination kernel function are analyzed. The performances in the cases of different kernel functions are studied by simulation, and the results show that the combination kernel function is the best choise to model predistorter. In addition, simulation results also show that the SVM predistorter performs more robust than the neural network predistorter does.The modified SMO algorithm proposed by Keerthi performs significantly faster than the original SMO by introducing two threshold parameters, the final bias value can be calculated by averaging the two threshold values, so the accuracy of the bias value will be influenced if the two threshold values fail to satisfy the optimality condition. The reason of violating optimality condition is analysed and the algorithm to find the bias value is deduced from the primal problem of regression. By analyzing the variation range of the bias value, the new algorithm is proved to be an optimality problem of one dimension convex function. By adopting golden section algorithm to solve the optimality problem to get bias value, we improve the ability of the SVM predistorter to linearize the nonlinear amplifier with memory.A novel tube compressing model is proposed for SVM regression problem. The model can forecast the support vectors of the regression function under smallε-insensitive value by learned function under largerε-insensitive value, the new training samples correspond to the support vectors are extracted , so the problem scale is decreased and the training efficiency will be improved.In the last part of this dissertation, the works on the predistortion technique for nonlinear power amplifier with memory that we have done are summarized, the next research directions and key points are predicted.