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Behavioral Modeling of Nonlinearities and Memory Effects in Power Amplifiers.

机译：功率放大器的非线性行为建模和记忆效应。

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摘要

High data rates and tight spectral limitations in wireless communication systems require high fidelity waveform transmission with minimal distortion. The waveforms currently being used, or envisioned for the future, coupled with the need for higher efficiency radio frequency (RF) power amplifiers (PA), are driving transmitters to include predistortion and to adopt advanced architectures. PA behavioral models are required for system simulations of these advanced architectures and digital predistortion (DPD) algorithms, to quickly evaluate the component impact on system performance. One advanced technology of particular interest in this work is envelope tracking (ET) where the supply voltage is modulated at the envelope rate, keeping the RF signal operating rail to rail, which changes the performance of the RFPA.;This dissertation focuses on accurate estimation of nonlinear behavioral models for PAs: both memoryless and with memory effects. Memory effects are the byproduct of physics-based or circuit-based changes within the amplifier with responses on a baseband time scale (rather than at the carrier frequency). When these memory effects become pronounced, they create a shift in the memoryless model. In order to accurately predict system performance, especially when the system includes a DPD block, it is critical to generate behavioral models for RFPAs which capture the shifts in the memoryless model.;The thesis presents a new model, termed the Blackbox augmented behavioral characteristics (ABC) model, which includes the response of the internal states of the circuit. Its extraction is demonstrated from measured data using pulsed or modulated waveforms. The generation of modulated waveforms for instrumentation systems has length restrictions and input sequences are used repetitively, so they should be circular. Techniques that synthesize circular waveforms suitable for measurement systems, directly from long modulated waveforms are presented. An "expected gain" model is developed in this work, and a methodology for extracting waveform specific, expected gain models efficiently from measurements of modulated signals (such as WCDMA) is presented. These expected gain models are then applied to DPD. A technique that leverages the circular stationary measurements, memory mitigation, is presented. The DPD algorithm identifies and compensates systematic distortions in the waveform, generating input signals that achieve optimal outputs, compensating for all deterministic memory of the system and quantifying the measurement limits of the system. A number of systematic measurement impairments are encountered in these experiments. Techniques that compensate for these impairments, including phase drift and time alignment are presented.;The thesis also describes behavioral modeling include demonstrating DPD using new techniques that stem from truncating and thresholding the Volterra series. When compared with two other published truncated Volterra series forms on standard Class AB PAs, all three memory models perform equally well. When applied to modeling ETPAs, however, the new thresholded Volterra series model is more efficient, using less than a third of the coefficients to describe the ETPA nonlinear memory effects. In this work, techniques are applied experimentally to RFPAs, from handset PAs to base station PAs, built with a wide variety of materials: HBTs on GaAs, LDMOS devices on Si, and HFET devices on GaN, and GaN HFET devices on Si.
机译：无线通信系统中的高数据速率和严格的频谱限制要求具有最小失真的高保真波形传输。当前正在使用或预想的波形，再加上对更高效率的射频（RF）功率放大器（PA）的需求，正在驱动发射机以包括预失真并采用先进的架构。这些高级架构和数字预失真（DPD）算法的系统仿真需要使用PA行为模型，以快速评估组件对系统性能的影响。这项工作中特别令人关注的一项先进技术是包络跟踪（ET），其中以包络率调制电源电压，使RF信号工作在轨对轨之间，从而改变了RFPA的性能。 PA的非线性行为模型：无记忆和具有记忆效应。记忆效应是放大器内基于物理或基于电路的变化的副产品，其响应在基带时标上（而不是在载波频率上）。当这些记忆效应变得明显时，它们会在无记忆模型中产生偏移。为了准确预测系统性能，尤其是当系统包含DPD块时，至关重要的是为RFPA生成行为模型，以捕获无记忆模型中的变化。;本文提出了一种新模型，称为Blackbox增强行为特征（ ABC）模型，其中包括电路内部状态的响应。使用脉冲或调制波形从测量数据中演示了其提取。仪器系统的调制波形的生成具有长度限制，并且重复使用输入序列，因此它们应该是圆形的。提出了直接从长调制波形中合成适合于测量系统的圆形波形的技术。在这项工作中开发了“预期增益”模型，并提出了一种从调制信号（例如WCDMA）的测量中有效提取特定波形的预期增益模型的方法。然后将这些预期的增益模型应用于DPD。提出了一种利用循环静止测量，缓解内存的技术。 DPD算法识别并补偿波形中的系统失真，生成可实现最佳输出的输入信号，补偿系统的所有确定性存储器并量化系统的测量范围。在这些实验中会遇到许多系统的测量缺陷。提出了可以弥补这些缺陷的技术，包括相位漂移和时间对准。本文还描述了行为建模，包括使用新技术演示DPD，该技术源自截断和阈值Volterra级数。与标准AB类PA上的其他两个已发布的截短的Volterra系列表格比较时，所有这三种内存模型的性能都一样好。但是，当将其应用于ETPA建模时，新的阈值Volterra级数模型效率更高，使用不到三分之一的系数来描述ETPA非线性记忆效应。在这项工作中，从手机PA到基站PA的RFPA实验性应用了多种材料：GaAs上的HBT，Si上的LDMOS器件以及GaN上的HFET器件以及Si上的GaN HFET器件。

著录项

  • 作者

    Draxler, Paul J.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 259 p.
  • 总页数 259
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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