This work introduces a novel design theory for a single-input hybrid Doherty power amplifier (PA) inspired by the design space existing within the previously reported dual-input Doherty–Chireix (outphasing) continuum. Unlike the conventional $\lambda/4$ Doherty PA inverter which only performs the correct load modulation at its center frequency, the hybrid Doherty PA (HD $\omega$ -PA) combiner network achieves a wideband load modulation using the frequency dependence of the electrical length of the output combiner lines versus frequency for sliding the PA mode of operation. A modified theory is presented in this work to allow for a single-input PA implementation. In this new design, the outphasing angle is only changing with frequency and not the input power. A transmission line phase shifter is used to provide the correct frequency-dependent input phase offset ensuring the correct wideband load modulation performed by the output combiner. A novel methodology is also proposed to select the optimal input phase offset to reduce the variation in the saturation power versus frequency and minimize the circuit size. A proof-of-concept demonstrator PA circuit is designed to operate from 2.5 to 3.3 GHz. When the fabricated PA is excited by a 20-MHz long-term evolution (LTE) modulated signal with 6-dB peak-to-average-power ratio (PAPR), an average efficiency of 45%–59% and adjacent channel leakage ratio (ACLR) less than $-$ 50 dBc are achieved after digital predistortion (DPD) across the entire band. When the fabricated PA is excited by a 5G-like 50-MHz orthogonal frequency-division multiplexing (OFDM) signal with 10-dB PAPR, an average efficiency of 33%–41% and ACLR less than $-$ 44 dBc are achieved after DPD across the entire band.

One means by which the security of Internet-of-Things (IoT)-enabled devices may be augmented is through radio-frequency fingerprinting-based authentication methods. As variability in CMOS processes increases with technology scaling, the hardware imperfections that form RF fingerprints can be controlled with small reconfigurable elements, enabling the feasibility of RF fingerprinting as a low overhead security measure for device authentication. To achieve rapid RF identification, we present an inherently secure RF power amplifier and a convolutional neural network-based machine learning classifier through an exploration of combinatorial randomness and self-aware detection mechanisms. By selecting different subsets of thinly sliced power amplifier elements, combinations of random process variations are exploited and updated to form a large search space of distinct RF fingerprints and improve fingerprint prominence. The rich features enabled by augmented device primitives are updated in a time-varying manner to strengthen built-in hardware security. Measurement results demonstrate the effectiveness of this approach at generating distinguishable RF fingerprints across a significant number of configurations.

This paper proposes a new primitive that allows soft robots to be physically controlled in a completely non-line-of-sight context using wireless energy - a process we call wireless actuation. Soft robots, which are composed entirely of soft materials and exclude any rigid components, are highly flexible platforms that can change their shape. This paper considers a specific class of soft robots composed of liquid-crystal elastomers (LCE) that are entirely electronics-free and engineered to change shape when heated to 60 °C. Traditionally, such robotic systems must be in line-of-sight of a light source, such as infrared to be moved, or require an external power supply for Joule heating and often take several tens of seconds to heat. We present WASER, a novel RF-based heating platform that allows electronics-free robots to be actuated rapidly (within a few seconds) and potentially in non-line-of-sight. WASER achieves this through innovations in both wireless systems and material science. On the wireless front, WASER develops a new blind beamforming solution that directs high-power wireless energy at fine spatial granularity without electronics on the robot to provide feedback. On the material science front, WASER exhibits heat-responsive shape-morphing and energy-harvesting material functionalities that allow for rapid wireless heating. We implement and evaluate WASER and demonstrate diverse shape-morphing capabilities.

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

Real-time in-sensor spike sorting is a forefront requirement in the development of brainmachine interfaces (BMIs). This work presents the characterization, design, and efficient implementation on a field-programmable gate array (FPGA) of a novel approach to neural spike sorting intended for implantable devices based on convolutional neural networks (CNNs). While the temporal features, the shape of the spike signals, could be highly mitigated from the ambient noise, the proposed classifier effectively extracts spatial features from the multi-channel neural signal to maintain high accuracy on the noisy data. The proposed classifier mechanism was tested on real data that is recorded from multi-channel electrodes, containing 27 neural units, and the classifier achieves 93.1% accuracy despite high temporal noise in the signal. For hardware synthesis, the CNN weights are quantized to reduce the model storage requirement by 93% compared to its floating point-precision version, and the model achieves an accuracy of 86.1%.

The growth of the IoT requires more comprehensive security measures than ever. RF fingerprinting (RFF) utilizes features in the signals and waveforms from transmitters’ physical-layer imperfections to classify and authenticate devices. To prevent attacks from impersonators, combinatorial randomness is exploited to augment the RF fingerprints with a high-efficiency PA for IoT applications. By enabling different subsets of thinly sliced PA elements, the transmitter can be reconfigured with 220 subsets that exhibit distinctive RF fingerprints for signal analysis at the edge. In this work, a combinatorial-randomness-based PA was implemented in a BLE system. The BLE packets’ in-phase and quadrature samples transmitted from each configuration are collected with different SNRs to emulate the environmental changes in communication channels. A lightweight convolutional neural network (CNN) classifier demonstrates the possibility of accurate and fast inference of unique features in the IoT environment, which our approach exploits to enable on-chip time-varying RF fingerprints.