Modeling And Predistortion Method For RF Power Amplifiers With Memory In Broadband Communications

Since the number of users and broadband communication traffic rise rapidly,radio frequency resources are becoming more and more scarce. To enhance spectrum efficiency, linear modulation and multicarrier techniques are widely adopted in modem wireless communication systems. Their non-constant envelope signals require to be amplified linearly. However, in order to increase power efficiency, PAs are often driven into the saturation region, where nonlinear distortion is severe.Linearization techniques play a key role to resolve the conflict between spectrum efficiency and power efficiency.Predistortion is a promising technique to linearize power amplifiers. Its effect heavily depends on the corresponding PA model. In broadband communication systems, such as WCDMA and OFDM, the memory effects can no longer be ignored and the performance of traditional memoryless modeling and predistortion method is seriously degraded.In this paper, methodologies of nonlinear systems are applied to model and predistort PAs with memory effects in broadband communication systems. The contents of the paper are as follows.1. The main nonlinear characteristics of power amplifiers and the basic modeling theory are described.2. Methods of analyzing memory effects of PAs are proposed. In broadband communication systems, memory effects of PAs are significant and memoryless model cannot analyze the phenomena resulted by memory effects. After investigating the phenomena and the causes of PA memory effects, methods based on Hammerstein model and Volterra series are proposed to analyze the dynamic PA characteristic curves and IMD asymmetry respectively. Therefore, the theoretical models can explain the observation results well.3. A wavelet network based method is developed to model RF PAs. Firstly, a complex wavelet network with tapped delay lines is applied to construct the model of PAs with memory. Secondly, the network is initialized according to the training data set, the wavelet function and the characteristics of the PA. Finally, the established network is trained by gradient techniques. Since the proposed mode can process complex signals directly, much simpler network architecture is achieved. The simulation results show that compared with the previously published BP model, the proposed model provides faster convergence rate and better performance in the time domain and frequency domain.4. The principles of predistortion techniques are described. The look-up-table based and workfunction based predistorter are discussed and compared in detail.5. A predistortion method based on simplifed Volterra series is proposed to linearize power amplifiers. The general Volterra model can hardly be utilized to design the higher order predistors with memory because of its high computational complexity. In this paper, a predistortion method using simplifed Volterra series is developed. The general Volterra model is simplified according to the PA characteristics and then the predistorter is designed using the simplified model. The indirect learning architechture is applied to implement the linearization system and the recursive least squares algorithm with a sliding rectangular window is utilized to identify the predistorter parameters. Therefore, the predistortion system can trace the variation of the PA characteristics effectively and the data storage space and computational complexity are also decreased. Finally, the performance of the proposed predistortion method is validated through computer simulations.