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Updated: Feb 8, 2026

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来自神经网络硬件加速的下一代数字预扭曲:趋势,挑战和未来
IEEE transactions on neural networks and learning systems
|February 6, 2026
概括
基于神经网络 (NN) 的数字预扭曲 (DPD) 为下一代通信系统提供了先进的准确性. 本综述分析了NN-DPD技术和硬件加速,以实现高效的实时实现,解决计算挑战.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 下一代通信系统在实时信号处理方面面临着计算方面的挑战,特别是数字预变形 (DPD).
- 传统的DPD方法难以达到现代系统所需的准确性和适应性.
- 基于神经网络 (NN) 的 DPD 是有前途的,但面临着复杂性和可扩展性等实施障碍.
研究的目的:
- 综合审查基于 NN 的 DPD 技术.
- 分析硬件加速策略,以有效实时实现NN-DPD.
- 确定可扩展和节能DPD的挑战和未来方向.
主要方法:
- 对DPD的各种NN架构 (深度,卷积,反复,混合) 的审查和分析.
- 评估硬件加速平台,包括GPU,FPGA和ASIC.
- 评估挑战,如评估标准和现实世界的验证.
主要成果:
- 与传统方法相比,基于NN的DPD提供了卓越的建模精度和适应性.
- 不同的NN架构和硬件平台在性能和效率方面存在不同的权衡.
- 主要挑战包括分散的评估指标和有限的实际部署.
结论:
- 在先进的通信系统中,基于NN的DPD对于将功率放大器非线性线性化至关重要.
- 硬件加速对于克服NN-DPD的计算复杂性至关重要.
- 未来的研究应该专注于模型-硬件代码设计和芯片上学习,以获得可扩展,节能解决方案.
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