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传统的基于间接学习结构的预失真技术通常仅考虑对单个地面功放或卫星功放进行补偿,线性化性能有限;文中联合地面功放与卫星功放的非线性失真和卫星信道的实际传输特性,提出了一种适合透明卫星功放的星地一体间接学习预失真算法。该算法利用记忆深度为5、非线性阶数为9的奇偶项多项式作为预失真器,同时采用最小均方(LMS)与递归最小二乘(RLS)联合的算法自适应更新预失真器的系数,以兼顾运算量和收敛速度。仿真结果表明,经过星地一体预失真后,卫星高功放输出端APSK星座图误差矢量幅度改善值达到91.52%,带外功率谱抑制平均提升了11.41dB,系统线性化性能非常理想。
Traditional predistortion techniques based on indirect learning structure usually only consider compensating a single ground power amplifier or satellite power amplifier and have limited linearization performance. In the paper, the nonlinear distortion of combined ground power amplifier and satellite amplifier and the actual transmission characteristics of satellite channels are proposed. An Indirect Learning Predistortion Algorithm Suitable for Transparent Satellite Power Amplifier. The algorithm uses the odd and even polynomials with a depth of 5 and a non-linear order of 9 as the predistorter, and adaptively updates the coefficients of the predistorter by a combination of least mean square (LMS) and recursive least squares (RLS) , To take into account the amount of computation and convergence speed. The simulation results show that the APSK constellation error vector amplitude of the satellite high-power amplifier output reaches 91.52% and the out-of-band power spectrum rejection increases by 11.41dB after the integrated pre-distorter, and the system linearization performance is very satisfactory.