Class-E Power Amplifiers Incorporating Fingerprint Augmentation With Combinatorial Security Primitives for Machine-Learning-Based Authentication in 65 nm CMOS

Shen, Yuyi; Xu, Jiachen; Yi, Jinho; Chen, Ethan; Chen, Vanessa

doi:10.1109/tcsi.2022.3141336

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…Advanced digital-modulation schemes, such as quadrature amplitude modulation (QAM), have been widely used for many modern wireless systems, ranging from Wi-Fi and Internet-of-Things (IoTs) [1]- [5] to the emerging millimetre-wave (mmwave) network [6]- [16]. Although employing such complex modulation schemes could significantly enhance spectrum efficiency, it results in an increased design complexity for power amplifiers (PAs) due to the relatively large peak-toaverage power ratio (PAPR) of signal.…”

Section: Introductionmentioning

confidence: 99%

A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS

Chen

et al. 2023

IEEE Trans. Circuits Syst. I

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In this work, a ten-way power-combined power amplifier is designed using a load modulated balanced amplifier (LMBA)-based architecture. To provide the required magnitude and phase controls between the main and control-signal paths of the LMBA, an unequal power splitter and a phase compensation network are proposed. As proof of concept, the designed power amplifier is implemented in a 45-nm SOI CMOS process. At 40 GHz, it delivers a 25.1 dBm Psat with a peak power-added efficiency (PAE) of 27.9%. At 6-dB power back-off level, it achieves 1.39 times drain efficiency enhancement over an ideal Class-B power amplifier. Using a 200-MHz single-carrier 64-QAM signal, the designed amplifier delivers an average output power of 16.5 dBm with a PAE of 13.1% at an EVMrms of -23.9 dB and ACPR of -25.3 dBc. The die size, including all testing pads, is only 1.92 mm 2 . To the best of the authors' knowledge, compared with the other recently published silicon-based LMBAs, this design achieves the highest Psat.Index Terms -Doherty amplifier, load-modulated balanced amplifier (LMBA), millimeter-wave (mm-wave) power amplifier, power back-off capability, radio-frequency integrated circuits (RFIC), silicon-on-insulator (SOI).

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Section: Introductionmentioning

confidence: 99%

A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS

Chen

et al. 2023

IEEE Trans. Circuits Syst. I

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“…All these methods are valid in physical layer security verification. However, different methods require certain application conditions; some require a high signal-to-noise ratio (SNR) [12,35], some require good channel quality [36], some completely offline [18,37], and some use different types of devices [5], and some require large amounts of training data [38,39].…”

Section: Introductionmentioning

confidence: 99%

RF Signal Feature Extraction in Integrated Sensing and Communication

Wang,

Sun,

Zhang

et al. 2023

IET Signal Processing

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Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB.

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“…non-linear problems. Research has been widely conducted to explore microwave structure behavior prediction using DNN [4], [5] Preliminary results have demonstrated that well-trained deep neural networks (DNN) can promptly and accurately predict the behaviors of microwave structures sharing similar characteristics [6]- [10]. RF engineers can thus save much time by doing electromagnetic (EM) simulations by reusing welltrained models.…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction

Dang

Watkins

et al. 2023

IEEE Trans. Circuits Syst. II

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Microwave structure behavior prediction enables the estimation of circuit response over a frequency range, playing a crucial role in the design of radio frequency (RF) structures. Deep neural network (DNN) approaches have demonstrated their capability to simulate microwave structure behaviors. Nonetheless, the quality and utility of the model are constrained by the availability of data and computational capabilities. These inherent disadvantages hinder the extensive application of DNN in microwave structure behavior prediction. Transfer learning has recently been produced as a method offering improved accuracy and speed for predicting microwave circuit behavior. This paper proposes a novel transfer learning-based model to expedite the prediction process for a sequence of frequency samples. Through experimental validation, it is illustrated that the proposed methodology outperforms the conventional DNN techniques for microwave structure behavior prediction by effectively reducing the required data and shortening the training time. The proposed model also facilitates the fine-tuning of hyperparameters and reduces the simulator computing load.

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Class-E Power Amplifiers Incorporating Fingerprint Augmentation With Combinatorial Security Primitives for Machine-Learning-Based Authentication in 65 nm CMOS

Cited by 9 publications

References 27 publications

A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS

A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS

RF Signal Feature Extraction in Integrated Sensing and Communication

A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction

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