Closed-form analysis for indirect learning digital pre-distorter for wideband wireless systems

Indirect learning architecture (ILA) for digital pre-distortion (DPD) is commonly used to linearize power amplifiers (PA). To the author’s best knowledge, most of the DPD results in the literature obtain the matrix form of the least-square solution in order to get the DPD coefficients numerically. There exists no explicit closed-form for these coefficients that can be used as plug-and-play in simulations, or used for further closed-form analysis of important measures such as signal-to-noise ratio (SNR) and mean square error (MSE), bit-error rate (BER), …etc. In this paper, we analyze the ILA-DPD system for general memory-polynomial PA models. We provide a closed-form solution for the DPD coefficients. We first present the analytical methodology for deriving the mathematical expressions for each DPD coefficient and then introduce an open-access code that generates the DPD coefficients in symbolic form that is used to mathematically model the DPD. We consider case studies for PA and show that the analytical DPD solution matches the Monte Carlo simulations. Moreover, we also provide a closed-form solution for the iterative adaptive ILA-DPD. Our analysis shows that in the case of a large training block length the non-iterative DPD achieves approximately the same performance as an iterative DPD with a shorter training block length. System impairments are also considered, e.g. the thermal noise and the quantization noise in analog–digital conversion (ADC). We derive the normalized mean square error (NMSE) for the transmit chain in the presence of these impairments. The NMSE expression is verified through numerical simulations.     

基于ARM+FPGA的短波功放数字预失真硬件平台设计

数字预失真技术作为一种功放线性化的关键技术, 可有效提高功放线性度. 为高效实现短波功放数字预失真算法, 改善功放线性度, 设计并实现了一款以ARM+FPGA为核心的短波功放数字预失真硬件平台, 该平台使系统具有更好的可移植性、可重用性和扩展性. 在对设计的整体硬件框架进行阐述的同时, 着重讨论了模拟电路模块的设计, 并分析了短波功放预失真线性化采用的方案. 整机测试结果表明, 设计的新平台能够满足数字预失真的要求.     

功率放大器非线性特性及预失真建模

信号的功率放大器是电子通信系统的关键器件之一,功放的输出信号相对于输入信号可能产生非线性变形,这将带来无益的干扰信号,研究其机理并采取措施改善,具有重要意义.通过利用无记忆非线性功放和记忆非线性功放的实测数据用数学方法对其分别进行建模,而后使用前置预失真器的方法改善功放的非线性特性,并对其中预失真器的建模做了研究,采用间接学习型结构构建预失真器模型.仿真结果显示功放非线性模型结合预失真器模型能够很好地逼近实际情况,并且能很好地抑制带外频谱扩展.     

功率放大器失真问题研究及预失真建模

电子器件中的功率放大器常常伴随着非线性失真效应.为解决此类非线性失真问题,通过研究无记忆和有记忆功放的失真特性,运用最小二乘法来构建多种形式的特性拟合函数,在选择了效果较好的特性拟合函数基础上,根据实际约束条件进行预失真模型的建立,使失真处理后的输出信号趋于线性,最后从信号的功率谱密度的角度出发检验预失真模型的补偿效果,证明模型具有较好的可行性和准确性.     

Ka频段GaN功放的预失真线性化器设计

基于满足目前高效复杂的调制技术对功率放大器线性化度越来越高的需求的目的,采用模拟预失真技术设计了一种线性化器,用来改善Ka频段氮化镓(GaN)固态功放的非线性化失真。通过改进传统的并联式二极管预失真电路,采用开环技术,将两个肖特基二极管并联。改变二极管的偏置状态,得到不同的改善程度的预失真信号。结合使用专用电磁仿真软件ADS2013做电路仿真,通过参数扫描得到二极管偏置状态的初始值,为实物调试提供理论基础。通过对已加工的实物测试,结果表明：增益幅度补偿达到6.4dB,相位补偿达到28°。     

Closed-form MSE and achievable rate for indirect learning DPD

The digital pre-distortion (DPD) signal processing is an effective way to mitigate the power amplifier (PA) nonlinearity effect. For communication systems containing DPD and PA, it is difficult to acquire performance metrics closed-forms for any DPD architecture since there was no mathematical expression for each DPD coefficient. Usually, researchers look for more efficient DPD algorithms for DPD coefficients (compared to the existing ones) in terms of computational complexity, delay, power consumption, etc. Consequently, the performance is evaluated through intensive simulation. In this paper, we show how one can exploit the results of our recent work to mathematically model the indirect learning architecture (ILA) DPD and efficiently derive important measures in communication systems, e.g. normalized mean square error (NMSE), achievable rate, and signal-to-noise plus distortion ratio (SNDR). The author would like to clarify that this work might be the first one to provide closed-form analysis for DPD systems. We think the provided framework/analysis will open the door to other researchers/engineers to plug their own assumptions and derive the performance metrics. The derived expressions of the performance metrics (NMSE, SNDR, and achievable rate) are validated through Monte Carlo simulations. We also derive a closed-form expression for the achievable rate bound for the transmit chain. Moreover, we analytically study the effect of the thermal noise and the quantization noise, in the analog-digital conversion (ADC) process, on the NMSE and achievable rate. The analytical expressions are validated through numerical simulations.