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首页> 外文期刊>Circuits and Systems I: Regular Papers, IEEE Transactions on >Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory
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Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory

机译：带存储器的非线性功率放大器的自适应分段线性预失真器

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摘要

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.
机译：我们提出了新颖的直接和间接学习预失真器（PD），它采用了新的基带简单正则分段线性（SCPWL）函数。通过更改SCPWL功能的段数，可以轻松控制提议的PD的性能。与基于多项式的PD相比，我们基于SCPWL的PD对强大的非线性建模更健壮，并且对输入噪声不那么敏感。尤其是，我们表明，在间接学习SCPWL-PD的反馈路径中出现的噪声对性能的影响可忽略不计，而多项式对应项则受到噪声引起的系数偏差的影响。我们考虑基于Hammerstein和基于内存的SCPWL PD的自适应实现。前者具有较少的待识别参数，而后者则使参数识别更为直接。在推导PD算法时，我们避免了一个单独的PA识别步骤，该步骤允许真正的实时或逐样本实施，而无需使用交替的PA和PD识别程序。但是，要获得有效的Hammerstein PD逐样本算法，我们需要绕过相关的非凸成本函数的问题。这是通过使用修改后的参数线性维纳模型完成的，该模型的参数可以显式或隐式用于间接和直接学习。大量的仿真证实，拟议的SCPWL PD优于其多项式对应，特别是当间接学习结构的反馈路径中存在噪声时。通过在802.16d WiMAX系统中的Freescale MRF6S23100H AB类PA上进行电路级仿真，也验证了这一点。

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