Search Results (218)

Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based on a sparse Bayesian learning (SBL) algorithm to identify a reduced set of regressors capable of an efficient and accurate prediction. The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. The performance of this proposed FD-SBL with a validation NMSE of $-47$ dB for a signal of 30 MHz bandwidth is comparable to $-46.6$ dB of the previously proposed implementation of the TD-SBL. In terms of execution time, the TD-SBL fails due to excessive processing time and numerical problems for a 100 MHz bandwidth signal, whereas the FD-SBL yields an adequate validation NMSE of −38.6 dB. Full article

This study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam’s geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester’s dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring—specifically, non-linear thickness shaping—as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations. Full article

In recent years, ultrafast bursts with high power have been applied in many significant fields. However, the peak power of the pulse train generated by fiber lasers is limited by fiber characteristics from nonlinear effects, which can only be at the level of milliwatt. In this research, the pulse frequency is reduced by an AOM pulse picker-integrated modulator. With M2 and pulse width guaranteed, the frequency of the reduced pulse train is amplified by a solid-state three-stage Nd:YVO₄ amplifier system. Finally, the peak power of the pulse train is increased. The final output pulse repetition rate of the experiment is 1 MHz with a pulse width of 8.09 picoseconds and a peak power of up to 3.7 MW. Full article

Neural networks are increasingly attractive for digital predistortion applications due to their demonstrated superior performance. This is mainly attributed to their ability to capture the intrinsic traits of nonlinear systems. This paper presents a novel hybrid predistorter labeled as the look-up table assisted bidirectional long-short term memory (BiLSTM) neural network (LUT-A-BiNN) that combines a neural network cascaded with a look-up table in a manner that both sub-models complement each other. The main motivation in using this two-box arrangement is to eliminate the highly nonlinear static distortions of the PA with the look-up table, allowing the neural network to focus on the compensation of the dynamic distortions. The proposed predistorter is experimentally validated using 5G test signals. The results demonstrate the ability of the proposed predistorter to achieve a 5 dB enhancement in the adjacent channel leakage ratio when compared to its single-box counterpart (BiLSTM neural network predistorter) while maintaining the signal-agnostic performance of the BiLSTM predistorter. Full article

Given the critical role of electrical cabinets in the post-earthquake recovery and emergency response of nuclear power plants (NPPs), a comprehensive assessment of their seismic performance is essential to ensure operational safety. This study analyzed seismic behavior by fabricating an electrical cabinet model based on the dynamic characteristics and field surveys of equipment installed in a Korean-type NPP. A shaking table test with simultaneous tri-axial excitation was conducted, incrementally increasing the seismic motion until damage was observed. A numerical model was then developed based on the experimental results, followed by a seismic response analysis and comparison of results. The findings verified that assuming fixed anchorage conditions in the numerical model may significantly overestimate seismic performance, as it fails to account for the nonlinear behavior of the anchorage system, as well as the superposition between global and local modes caused by cabinet rocking and impact under strong seismic loading. Furthermore, damage and impact at the anchorage amplified acceleration responses, significantly affecting the high-frequency range and the vertical behavior, leading to substantial amplification of the in-cabinet response spectrum. Full article

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article

Bio-inspired joints play a pivotal role in legged robots, directly determining their motion capabilities and overall system performance. While shape memory alloy (SMA) actuators present superior power density and silent operation compared to conventional electromechanical drives, their inherent nonlinear hysteresis and restricted strain capacity (typically less than 5%) limit actuation range and control precision. This study proposes a bio-inspired joint integrating an antagonistic actuator configuration and differential dual-diameter pulley collaboration, achieving amplified joint stroke (±60°) and bidirectional active controllability. Leveraging a comprehensive experimental platform, precise reference input tracking is realized through adaptive fuzzy control. Furthermore, an SMA-driven bio-inspired leg is developed based on this joint, along with a motion retargeting framework to map human motions onto the robotic leg. Human gait tracking experiments conducted on the leg platform validate its motion performance and explore practical applications of SMA in robotics. Full article

This paper presents a highly linear two-stage InGaP/GaAs power amplifier integrated circuit (PAIC) using a dynamic predistorter for 5G small-cell applications. The proposed predistorter, based on a diode-connected transistor, utilizes a supply voltage to accurately control the linearization characteristics by adjusting its dc current. It is connected in parallel with an inter-stage of the two-stage PAIC through a series configuration of a resistor and an inductor, and features a shunt capacitor at the base of the transistor. These passive components have been optimized to enhance the linearization performance by managing the RF signal’s coupling to the diode. Using these optimized components, the AM−AM and AM−PM nonlinearities arising from the nonlinear resistance and capacitance in the diode can be effectively used to significantly flatten the AM−AM and AM−PM characteristics of the PAIC. The proposed predistorter was applied to the 2.6 GHz two-stage InGaP/GaAs HBT PAIC. The IC was tested using a 5 × 5 mm² module package based on a four-layer laminate. The load network was implemented off-chip on the laminate. By employing a continuous-wave (CW) signal, the AM−AM and AM−PM characteristics at 2.55–2.65 GHz were improved by approximately 0.05 dB and 3°, respectively. When utilizing the new radio (NR) signal, based on OFDM cyclic prefix (CP) with a signal bandwidth of 100 MHz and a peak-to-average power ratio (PAPR) of 9.7 dB, the power-added efficiency (PAE) reached at least 11.8%, and the average output power was no less than 24 dBm, achieving an adjacent channel leakage power ratio (ACLR) of −40.0 dBc. Full article

With the continuous expansion of communication bandwidth, accurately modeling the non-linear characteristics of power amplifiers has become increasingly challenging, directly affecting the performance of digital pre-distortion (DPD) technology. The high peak-to-average power ratio and complex modulation schemes of wideband signals further exacerbate the difficulty of DPD implementation, necessitating more efficient algorithms. To address these challenges, this paper proposes a wideband DPD algorithm based on edge signal correction. By acquiring signals near the center frequency and comparing them with equally band-limited feedback signals, the algorithm effectively reduces the required processing bandwidth. The incorporation of cross-terms for model calibration enhances the model fitting accuracy, leading to significant improvement in pre-distortion performance. Simulation results demonstrate that compared with traditional DPD algorithms, the proposed method reduces the error vector magnitude (EVM) from 1.112% to 0.512%. Experimental validation shows an average improvement of 11.75 dBm in adjacent channel power at a 2 MHz frequency offset compared to conventional memory polynomial DPD. These improvements provide a novel solution for power amplifier linearization in wideband communication systems. Full article

Active magnetic bearings (AMBs), pivotal in high-speed rotating machinery for their frictionless operation and precise control, demand power amplifiers with exceptional dynamic performance and minimal current ripple. However, conventional amplifier designs often overlook eddy current effects, a critical oversight given the high-frequency switching inherent to pulse-width modulation (PWM). These induced eddy currents distort output waveforms, amplify ripple, and degrade system bandwidth. This paper bridges this critical gap by proposing a comprehensive methodology to model, quantify, and mitigate eddy current impacts on three-level half-bridge power amplifiers. A novel mutual inductance-embedded circuit model was developed, integrating winding–eddy current interactions under PWM operations, while a discretized transfer function framework dissects frequency-dependent ripple amplification and phase hysteresis. A voltage selection criterion was analytically derived to suppress nonlinear distortions, ensuring stable operation in high-precision applications. A Simulink simulation model was established to verify the accuracy of the theoretical model. Experimental validation demonstrated a 212% surge in steady-state ripple (48 mA to 150 mA at 4 A DC bias) under a 20 kHz PWM operation, aligning with theoretical predictions. Dynamic load tests (400 Hz) showed a 6.28% current amplitude reduction at 80 V DC bus voltage compared to 40 V, highlighting bandwidth degradation. This research provides a paradigm for optimizing AMB power electronics, enhancing precision in next-generation high-speed systems. Full article

This paper presents a prototype open-loop pipelined stage in a 45 nm CMOS process for supporting 1.8 GS/s and 10-bit design specifications of pipelined ADCs. In order to alleviate the severe non-linearity expressed by open-loop MDACs, an innovative current-mode harmonic compensation is proposed to provide input related third harmonic terms to cancel non-linearity. In addition, an effective double-sampling scheme is optimized by modifying compensation timing and input of a residual amplifier so that the pipelined stage can be immune to memory effect and improve power efficiency. The memory effect compensation scheme can provide a 21 dB improvement on output SNDR of the double-sampling pipelined stage. The simulation results illustrate that the open-loop pipelined ADC stage achieves an output SNDR of at least 52 dB with 840 mV input amplitude and 240 fF load while consuming only 11.24 mW. Full article

A polarization-maintaining all-fiber laser source based on a nonlinear amplifying loop mirror with broadband operation (64 nm) around 1920 nm is demonstrated. The oscillator can generate 66 pJ up-chirped dissipative soliton pulses at a repetition rate of 22.8 MHz with a high polarization extinction ratio of 17 dB. By adding a polarization controller to the polarization-maintaining dispersion-compensating fiber, the filter behavior can be adjusted allowing for the tuning of the emission to a center wavelength of 1878 nm, 1907 nm, and 1926 nm. Using an all-polarization-maintaining single-mode fiber amplifier with anomalous dispersion, the pulses are amplified to 0.9 nJ and compressed to a near Fourier-limited pulse duration of 170 fs with a peak power of 4.3 kW. Such all-fiber-based sources are attractive due to their compact size, high beam quality, and good environment stability. Full article

Recently, the concept of cell-free massive multiple-input multiple-output (CF-mMIMO) has been implemented in low-Earth-orbit (LEO) constellations to enhance energy efficiency. However, signal distortion caused by nonlinear power amplifiers (NPAs) significantly degrades the performance of LEO satellite communication (SATCOM) links. In this paper, we propose a serving cluster scheme and a hybrid precoding framework for CF-mMIMO-assisted LEO SATCOM systems, aiming to suppress the impact of NPAs while reducing onboard hardware complexity and power consumption. Specifically, we first develop a user-centric access point clustering strategy that incorporates elevation constraints and channel fading. Then, we formulate a precoding optimization problem to maximize energy efficiency by treating the product of tightly coupled analog and digital matrices as a single fully digital precoder. The proposed distortion-aware precoding design is achieved by integrating the weighted minimum mean square error (WMMSE) approach with a gradient descent method enhanced by Nesterov’s accelerated momentum. Furthermore, an efficient hybrid precoding method based on alternating minimization is proposed to address the matrix decomposition challenge. Simulation results validate the effectiveness of our proposed user-centric serving clustering algorithm and the WMMSE-based precoding algorithm in CF-mMIMO-assisted LEO SATCOM systems. Full article

Digital predistortion (DPD) is essential for improving the efficiency and linearity of power amplifiers (PAs), particularly in radio frequency communication systems. We propose a wavelet decomposition prediction (WDP) framework that better adapts to the highly nonlinear characteristics of PAs. In this framework, the input data are first decomposed using wavelet transformation, allowing for a more effective representation of nonlinear features. Next, a nonlinear modeling process is conducted on the PA to capture its distortion characteristics. Once the nonlinear model is trained, it is frozen to preserve its learned features. Based on this frozen nonlinear model, DPD is then applied to the PA to compensate for nonlinear distortions. Experimental results demonstrate the effectiveness of our proposed method, achieving the best ACPR and EVM performance on the OpenDPD dataset. Full article