基于gpu的射频功率放大器数字预失真线性器的设计与实现

Wantao Li, G. Montoro, P. Gilabert
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引用次数: 1

摘要

本文提出了一个自动生成计算效率高的射频(RF)功率放大器(PA)行为模型的过程,该模型可用于数字预失真(DPD)线性化。DPD行为模型的实现基于个人计算机(PC)架构,包括具有计算统一设备架构(CUDA)内核的最新图形处理单元(gpu)。利用并行计算能力,实现了当前5G新空口通信所需的高基带吞吐量和固有的DPD带宽扩展。将给出DPD实现的细节,并讨论GPU DPD的优缺点。实验结果将验证DPD在硬件在环(HITL)环境中的实现,该环境采用系统级片(SoC) PA在100 MHz 5G-NR信号下工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of a GPU-Based Digital Predistortion Linearizer for RF Power Amplifiers
This paper presents an automated process for generating computationally efficient radiofrequency (RF) power amplifier (PA) behavioural models that can be used for digital-predistortion (DPD) linearization. The implementation of the DPD behavioral model is based on the personal computer (PC) architecture including recent graphical process units (GPUs) with compute unified device architecture (CUDA) cores. By utilizing the parallel computing capability, the proposed implementation can achieve high baseband throughput required by nowadays 5G new radio (NR) communications and the inherent DPD bandwidth expansion. The details of the DPD implementation will be given and the advantages and disadvantages of the GPU DPD will be discussed. Experimental results will validate the DPD implementation in a hardware-in-the-loop (HITL) environment with a system-on-chip (SoC) PA operated with a 100 MHz 5G-NR signal.
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