An Accurate Complexity-Reduced Simplified Volterra Series for RF Power Amplifiers
An Accurate Complexity-Reduced Simplified Volterra Series for RF Power Amplifiers
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DOI：
10.2528/pierc13121201
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发表时间：
2014
影响因子：
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通讯作者：
Gang Sun;Cuiping Yu;Yuan’an Liu;Shulan Li;Jiu-chao Li
中科院分区：
文献类型：
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作者：
Gang Sun;Cuiping Yu;Yuan’an Liu;Shulan Li;Jiu-chao Li
为射频功率放大器 (PA) 推出了精确、复杂性降低的简化 Volterra (ACR-SV) 系列。它在传统简化Volterra（SV）级数的基础上，分别考虑无记忆非线性和记忆效应，同时连接非线性记忆效应（NME）以提高模型的精度。所提出的 ACR-SV 模型使用由两个调制信号（WCDMA 1001 信号和带宽为 40MHz 的单载波 16QAM 信号）驱动的 GaN F 类 PA 进行评估。正向建模和 DPD 应用的实验结果表明，所提出的 ACR-SV 模型优于记忆多项式（MP）模型、增强复杂性降低的广义记忆多项式（ACR-GMP）和 SV 模型。与MP模型相比，ACR-SV模型在正向建模中归一化均方误差（NMSE）提高了2.61dB，在DPD应用中平均邻道功率比（ACPR）提高了3.7/4.2dB，模型系数数量减少了13%。与ACR-GMP模型相比，ACR-SV模型的NMSE改善了1.39dB，ACPR改善了0.7/0.6dB，模型系数数量相当。与 SV 模型相比，ACR-SV 模型实现了类似的模型精度，但系数降低了约 53%。
An accurate complexity-reduced simplifled Volterra (ACR-SV) series is introduced for RF power ampliflers (PAs). Based on the conventional simplifled Volterra (SV) series, it takes memoryless nonlinearity and memory efiect into consideration separately, while connected with a nonlinear memory efiect (NME) in order to increase accuracy of the model. The proposed ACR-SV model is assessed using a GaN Class-F PA driven by two modulated signals (a WCDMA 1001 signal and a single carrier 16QAM signal with 40MHz band width). The experimental results in forward modeling and DPD application demonstrate that the proposed ACR-SV model outperforms the memory polynomial (MP) model, the augmented complexity-reduced generalized memory polynomial (ACR-GMP), and the SV model. Compared with the MP model, the ACR-SV model shows a normalized mean square error (NMSE) improvement of 2.61dB in forward modeling, average adjacent channel power ratio (ACPR) improvement of 3.7/4.2dB in the DPD application with less 13% number of model coe-cients. In comparison with the ACR-GMP model, the ACR-SV model shows NMSE improvement of 1.39dB, ACPR improvement of 0.7/0.6dB with comparable number of model coe-cients. In contrast with the SV model, the ACR-SV model achieves similar model accuracy, but reduces approximately 53% of coe-cients.