Power amplifier behavioral model adaptive pruning using conjugate gradient‐based greedy algorithm

Starting from the greedy theory, this paper presents a novel adaptive greedy scheme for the power amplifier (PA) behavioral model adaptive pruning. The proposed scheme incorporates the stochastic conjugate gradient (SCG) principle into the subspace pursuit (SP) greedy algorithm, which can considerably offer improved tracking capabilities and faster convergence compared to other anterior adaptive greedy algorithms. Compared to conventional nonsparse methods, simulation results show that the proposed scheme can efficiently reduce the model order and computational complexity but almost have the comparable model performance with the full model. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

压缩感知在分段式双反馈预失真器的模型简化研究

针对预失真器中模型参数过多的问题,从压缩感知(compressed sensing, CS)的理论出发,提出了一种用于宽带射频功率放大器(power amplifier, PA)的预失真器模型简化方法。在稀疏度自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)算法的基础上进行改进,提出了基于频域陷波的相关支集选择SAMP算法(RSS-FNSAMP)。该算法能够对PA行为模型进行简化,而后将其运用在提出的分段式双反馈DPD系统中,以补偿被带内残差所掩盖的带外失真,在增强了系统稳定性的同时降低了其复杂度,提升了DPD线性化效果。为了验证该方法,使用20 MHz LTE信号驱动一个35 dBm的F类功率放大器。实验结果表明,归一化均方误差(normalized mean squared error, NMSE)与ILA-SAMP、ILA-DOMP和分段式双反馈-DOMP相比提升了3～5 dB,相邻信道功率比(adjacent channel power radio, ACPR)改善了25 dBc,表明该方法能够在降低模型参数数目的同时,提升功放...     

卫星通信系统中适用于16QAM信号的一种预失真方法

提出了一种用于卫星通信系统中由记忆功率放大器引起的16QAM信号非线性失真的高效数字自适应补偿方法。该方法使用通用记忆多项式模型和间接学习结构构造预失真系统,并使用QR分解递归最小二乘算法动态更新预失真参数。针对QR分解递归最小二乘算法不能直接更新系数矢量的问题,提出了一种称为＂权值冲洗＂的高效权值更新方法。计算机仿真结果表明该方法具有收敛速度快、系统性能稳定、补偿效果好等特点,并适合于大规模电路硬件实现。     

A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion

In this paper, we propose a new sparse framework for the design of the behavioral model and digital predistorter of a broadband power amplifier (PA). We start by formulating the Volterra kernel to multidimensional memory polynomial by considering the high‐order dynamic truncation of the Volterra model. Then we show how an estimate of the most significant coefficients may be obtained using a matching pursuit (MPT) algorithm by exploiting the sparsity of the model. After the indices of the nonzero coefficients are roughly estimated, the block exact Householder inverse QR‐decomposition‐based recursive least squares (HIQRD‐RLS) algorithm is utilized to estimate the sparse model complex coefficients. For broadband nonlinear PAs, the proposed approach is demonstrated to achieve the best performance among the well‐known traditional approaches in terms of in‐band and out‐of‐band specifications. The proposed approach is also validated by evaluating the digital predistortion (DPD) performance on a Class‐AB PA in terms of adjacent channel power ratio (ACPR). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.