增强功率放大器中的射频指纹生成:不等间隔多音设计方法和注意事项

Chengyu Fan;Junting Deng;Ethan Chen;Vanessa Chen
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摘要

随着物联网(IoT)设备和通信标准的快速发展,对数据安全性的要求也越来越高,尤其是在硬件资源有限的情况下。除了传统的软件级数据加密外,物理层安全技术,如特定设备的射频指纹(RFF),正在成为有前途的解决方案。本文首先总结了先前关于时间戳 RFF 生成和可重构功率放大器 (PA) 设计的技术。随后,介绍了一种创新的两级功率放大器,该放大器采用 65-nm CMOS 工艺设计,包含一个可重构的 A 级和一个 Doherty 放大器,可生成 4096 个时间戳 RFF,且不会引入带内功率变化。多个 3 位电阻式数模转换器 (RDAC) 用于控制两级功率放大器内的体偏压单元,有助于生成大量可区分的 RFF。随后,提出了时变不等间隔多音(USMT)技术,将可用的时间戳 RFF 数量从 4096 个进一步提高到 16 384 个。利用 64-QAM WiFi-6E 广告数据包的 RFF 验证结果表明,在 5.39-5.41-GHz 频段内采用时变 USMT 发送的 RFF 成功生成了 16 384 种不同的 RFF 模式。此外,测量结果表明,在生成的模式中,超过 11 504 个 RFF 可被分类,准确率超过 99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing RF Fingerprint Generation in Power Amplifiers: Unequally Spaced Multitone Design Approaches and Considerations
The rapid growth of Internet of Things (IoT) devices and communication standards has led to an increasing demand for data security, particularly with limited hardware resources. In addition to conventional software-level data encryption, physical-layer security techniques, such as device-specific radio frequency fingerprints (RFFs), are emerging as promising solutions. This article first summarizes prior arts on timestamped RFFs generation and reconfigurable power amplifier (PA) designs. Following that, an innovative 2-stage PA incorporating a reconfigurable class A stage with a Doherty amplifier, designed in 65-nm CMOS to generate 4096 timestamped RFFs without introducing in-band power variation, is presented. Multiple 3-bit resistive digital-to-analog converters (RDACs) are applied to control body biasing units within the two-stage PA, facilitating the generation of massive and distinguishable RFFs. Subsequently, time-varying unequally spaced multitone (USMT) techniques are proposed to further elevate the count of available timestamped RFFs from 4096 to 16 384. The validation results of RFFs utilizing 64-QAM WiFi-6E advertising packets, employing time-varying USMT transmitted within the 5.39–5.41-GHz band, confirm the successful generation of 16 384 distinct RFF patterns. Moreover, the measurement results demonstrate that more than 11 504 RFFs among the generated patterns can be classified with an accuracy exceeding 99%.
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