基于间接学习结构的改进功放非线性失真补偿算法

摘要

随着无线通信的迅猛发展，频谱资源紧张和功放功率效率低等问题亟待解决。虽然采用高阶调制方式可以缓解资源紧张问题，但信号通过功放产生的非线性失真不仅严重影响带内通信的可靠性，还会影响邻近信道。因此，鉴于基带自适应数字预失真补偿技术，将16QAM调制作为测试信号，建立功放非线性模型和预失真模型，基于间接学习结构，利用归一化最小均方（NLMS）算法来获取预失真器补偿参数。特别地，引入平均邻近信道功率比（ACPR）作为优化目标函数来确定NLMS算法的最佳步长因子，使得在满足传输性能的前提下对邻近信道的干扰降低至最小。仿真结果证明了所提改进算法的有效性。%With the rapid development of wireless communication, the limited spectrum resource and low efficiency of power amplifier becomes a problem needing prompt solution. Although the adoption of high order modulation could relieve the shortage of spectrum resource, the power amplifier may bring about nonlinear distortion and degrade the performance of wireless communication system. Based on baseband adaptive digital pre-distortion technique and with 16QAM as the test signal, the nonlinear model for amplifier and pre-distorter are established, and based on indirect-learning architecture and with NLMS(normalized least mean square) algorithm, the compensation parameters of pre-distorter also acquired. In particular, with the introduced average ACPR(adjacent channel power ratio) as the target function to determine the optimal step factor of NLMS, and on the premise of satisfying the transmission performance requirement, the interferences onto the adjacent channel are largely eliminated. Simulation results indicate the validity of the proposed algorithm.