arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.03763eess.SP
收藏

一种基于深度卷积神经网络的非线性功率放大器行为建模的通用架构

A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network

Xin Hu, Zhijun Liu, You Li, Lexi Xu, Sun Zhang, Qinlong Li, Jia Hu, Wenhua Chen, Weidong Wang, Mohamed Helaoui, Fadhel M. Ghannouchi

摘要:

功率放大器的非线性是限制无线传输系统容量的主要因素之一。非线性损伤由功率放大器的非线性失真和调制器缺陷确定。阐述了建立功率放大器非线性模型的Volterra模型、几种紧凑型Volterra模型和神经网络模型。然而,这些模型的计算成本增加,并且随着信号带宽或载波聚合的数量变宽,它们的实现需要更多的信号处理资源。一种完全不同的方法使用深度卷积神经网络从训练数据中学习,以计算出非线性失真。在这项工作中,提出了一种基于深度实值卷积神经网络(DRVCNN)的低复杂度通用架构,以构建功率放大器的非线性行为。由于多个输入中的每一个都相当于一个输入向量,DRVCNN张量权重是从训练数据中构建的,这要归功于当前和历史依赖于时间的项I和Q,它们是输入的分量。验证了该通用框架在单载波和多载波功率放大器建模中的有效性。

英文摘要:

Nonlinearity of power amplifier is one of the major limitations to the achievable capacity in wireless transmission systems. Nonlinear impairments are determined by the nonlinear distortions of the power amplifier and modulator imperfections. The Volterra model, several compact Volterra models and neural network models to establish a nonlinear model of power amplifier have all been demonstrated. However, the computational cost of these models increases and their implementation demands more signal processing resources as the signal bandwidth gets wider or the number of carrier aggregation. A completely different approach uses deep convolutional neural network to learn from the training data to figure out the nonlinear distortion. In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier. With each of the multiple inputs equivalent to an input vector, the DRVCNN tensor weights are constructed from training data thanks to the current and historical envelope-dependent terms, I, and Q, which are components of the input. The effectiveness of the general framework in modeling single-carrier and multi-carrier power amplifiers is verified.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑