Enhancing RF Fingerprint Generation in Power Amplifiers: Unequally Spaced Multitone Design Approaches and Considerations

Abstract

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%.

Keywords

• Combinatorial randomness (CR)
• Internet of Things (IoT)
• machine learning
• neural networks
• power amplifier (PA)
• radio frequency fingerprint (RFF)