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Steady-State Performance Analysis and Step-Size Selection for LMS-Adaptive Wideband Feedforward Power Amplifier Linearizer

机译:LMS自适应宽带前馈功率放大器线性化器的稳态性能分析和步长选择

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摘要

Balancing between power amplifier (PA) linearity and power efficiency is one of the biggest implementation challenges in radio communication transmitters. Among various linearization methods, the feedforward linearization technique is a fairly established principle offering a good tradeoff between linearity and power-efficiency even under wideband operation. Moreover, adaptive techniques for such linearizer have been proposed in literature to track parameter changes in the main PA and other circuitry. Among those, least mean squares (LMS) method for adapting signal cancellation loop (SCL) and error cancellation loop (ECL) coefficients is an attractive low-complexity alternative. In this paper, we carry out extensive closed-form performance analysis of the achievable intermodulation distortion (IMD) reduction of the overall LMS-adaptive feedforward linearizer, as a function of the used step-sizes and essential waveform statistics. Such analysis is currently missing from the state-of-the-art literature. Both memoryless nonlinearities and Wiener–Hammerstein type PA memory models are studied for which IMD suppression expressions are derived. Comprehensive computer simulations are also provided to illustrate the accuracy of the analysis when practical OFDM waveforms are used. Design examples are given as well where the analysis results are used to choose proper linearizer step-sizes to meet given transmitter spectral mask specifications.
机译:功率放大器(PA)线性度和功率效率之间的平衡是无线电通信发射机中最大的实现挑战之一。在各种线性化方法中,前馈线性化技术是一项相当成熟的原理,即使在宽带操作下,也可以在线性度和功率效率之间取得良好的平衡。此外,文献中已经提出了用于这种线性化器的自适应技术,以跟踪主功率放大器和其他电路中的参数变化。其中,用于调整信号消除回路(SCL)和误差消除回路(ECL)系数的最小均方(LMS)方法是一种有吸引力的低复杂度替代方案。在本文中,我们根据所使用的步长和基本波形统计量,对整个LMS自适应前馈线性化器可实现的互调失真(IMD)降低进行了广泛的闭式性能分析​​。当前的最新文献中缺少这种分析。研究了无记忆非线性和Wiener-Hammerstein型PA记忆模型,从中得出了IMD抑制表达式。还提供了全面的计算机仿真,以说明使用实际OFDM波形时分析的准确性。还给出了设计示例,其中使用分析结果来选择合适的线性化器步长,以满足给定的发射机频谱模板要求。

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