关键词: 功率放大器, 预失真, 模糊神经网络, 记忆非线性, 简化粒子群优化算法

Abstract: To overcome the pre-distortion limitations of structure and precision for nonlinear power amplifiers with memory in wireless communication system, an adaptive pre-distortion method with a dual-loop learning structure based on fuzzy neural network model identification is proposed. This method is based on real-valued time-delay fuzzy neural network model, and uses simplified particle swarm optimization algorithm to ensure network parameters with indirect structure off-line training as the initial of the pre-distortion model. Then it uses least mean square algorithm to adjust the parameters of pre-distorter adaptively with direct structure on-line training, and fits the nonlinearity and memory effect of power amplifier. This method has simple structure, fast convergence speed and high precision, and avoids falling into the local optimum. The results show that this scheme makes adjacent channel power ratio improve about 7 dB than the method of classic dual-loop learning structure, and improve the linearity of the power amplifier obviously. Therefore the simulation results verify the feasibility of the method.

Key words: power amplifier, pre-distortion, fuzzy neural network, nonlinearity with memory, simplified particle swarm optimization algorithm