稀疏的归一化功放模型及预失真应用
发布时间：2018-07-17 01:43
【摘要】：针对射频功放的非线性特性进行了研究,提出一种新的稀疏化的Volterra级数模型。该模型基于压缩感知算法,将稀疏系统的辨识等效为信号的重构问题,利用正则正交匹配(ROMP)算法对核系数进行稀疏化并选择出活跃的核系数。将提出的模型与记忆多项式(MP)模型、通用记忆多项式(GMP)模型进行比较,较MP模型的建模精度提升10.7 dB,模型系数减少25%;较GMP模型的建模精度提升3.9 dB,模型系数减少57.65%。仿真结果表明,提出的方法实现了良好的预失真线性化性能,极大地降低了模型系数,优于传统的功放行为模型,由此验证对功放的线性化技术发展具有参考价值。
[Abstract]:The nonlinear characteristics of RF power amplifier are studied and a new sparse Volterra series model is proposed. Based on the compression sensing algorithm, the model equates the identification of sparse systems to the problem of signal reconstruction. The regularized orthogonal matching (ROMP) algorithm is used to sparse the kernel coefficients and select the active kernel coefficients. The proposed model is compared with the memory polynomial (MP) model and the general memory polynomial (GMP) model. Compared with the MP model, the modeling accuracy is increased by 10.7 dB and the model coefficient is reduced by 25 dB, and the modeling precision is increased by 3.9 dB and the model coefficient is reduced by 57.65 dB compared with that of the GMP model. The simulation results show that the proposed method achieves good predistortion linearization performance, greatly reduces the model coefficient, and is superior to the traditional power amplifier behavior model, which has a reference value for the development of power amplifier linearization technology.
【作者单位】： 辽宁工程技术大学电子与信息工程学院;
【基金】：国家自然科学基金面上项目(61372058) 辽宁省教育厅科学研究一般项目(L2015209) 辽宁省高等学校重点实验室资助项目(LJZS007)
【分类号】：TN722.75
，
本文编号：2128469
[Abstract]:The nonlinear characteristics of RF power amplifier are studied and a new sparse Volterra series model is proposed. Based on the compression sensing algorithm, the model equates the identification of sparse systems to the problem of signal reconstruction. The regularized orthogonal matching (ROMP) algorithm is used to sparse the kernel coefficients and select the active kernel coefficients. The proposed model is compared with the memory polynomial (MP) model and the general memory polynomial (GMP) model. Compared with the MP model, the modeling accuracy is increased by 10.7 dB and the model coefficient is reduced by 25 dB, and the modeling precision is increased by 3.9 dB and the model coefficient is reduced by 57.65 dB compared with that of the GMP model. The simulation results show that the proposed method achieves good predistortion linearization performance, greatly reduces the model coefficient, and is superior to the traditional power amplifier behavior model, which has a reference value for the development of power amplifier linearization technology.
【作者单位】： 辽宁工程技术大学电子与信息工程学院;
【基金】：国家自然科学基金面上项目(61372058) 辽宁省教育厅科学研究一般项目(L2015209) 辽宁省高等学校重点实验室资助项目(LJZS007)
【分类号】：TN722.75
，
本文编号：2128469
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