Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory
Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory
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DOI：
10.1109/tcsi.2011.2177007
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
M. Y. Cheong;S. Werner;M. Bruno;J. Figueroa;J. Cousseau;R. Wichman
中科院分区：
文献类型：
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作者：
M. Y. Cheong;S. Werner;M. Bruno;J. Figueroa;J. Cousseau;R. Wichman
我们提出了新颖的直接和间接学习预失真器（PD），其采用新的基带单纯正则分段线性（SCPWL）函数。通过改变 SCPWL 功能的段数可以轻松控制所提出的 PD 的性能。与基于多项式的 PD 相比，我们基于 SCPWL 的 PD 在建模强非线性方面更加稳健，并且对输入噪声不太敏感。特别是，我们表明，间接学习 SCPWL-PD 的反馈路径中出现的噪声对性能的影响可以忽略不计，而多项式对应项则遭受噪声引起的系数偏差。我们考虑基于 Hammerstein 和基于内存的 SCPWL PD 的自适应实现；前者需要识别的参数较少，而后者则可以更直接地识别参数。在推导 PD 算法时，我们避免了单独的 PA 识别步骤，从而可以实现真正的实时或逐个样本实现，而无需交替使用 PA 和 PD 识别过程。然而，为了实现 Hammerstein PD 的高效逐样本算法，我们需要绕过相关的非凸成本函数问题。这是通过采用修改后的参数线性维纳模型来完成的，该模型的参数可以显式或隐式地用于间接和直接学习。广泛的模拟证实，所提出的 SCPWL PD 优于其多项式对应物，特别是当间接学习结构的反馈路径中存在噪声时。 802.16d WiMAX 系统中的飞思卡尔 MRF6S23100H AB 类 PA 的电路级仿真也验证了这一点。
We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system.