Hardware Imperfections Research Articles

Universal logic with encoded spin qubits in silicon

Quantum computation features known examples of hardware acceleration for certain problems, but is challenging to realize because of its susceptibility to small errors from noise or imperfect control. The principles of fault tolerance may enable computational acceleration with imperfect hardware, but they place strict requirements on the character and correlation of errors1. For many qubit technologies2–21, some challenges to achieving fault tolerance can be traced to correlated errors arising from the need to control qubits by injecting microwave energy matching qubit resonances. Here we demonstrate an alternative approach to quantum computation that uses energy-degenerate encoded qubit states controlled by nearest-neighbour contact interactions that partially swap the spin states of electrons with those of their neighbours. Calibrated sequences of such partial swaps, implemented using only voltage pulses, allow universal quantum control while bypassing microwave-associated correlated error sources1,22–28. We use an array of six 28Si/SiGe quantum dots, built using a platform that is capable of extending in two dimensions following processes used in conventional microelectronics29. We quantify the operational fidelity of universal control of two encoded qubits using interleaved randomized benchmarking30, finding a fidelity of 96.3% ± 0.7% for encoded controlled NOT operations and 99.3% ± 0.5% for encoded SWAP. The quantum coherence offered by enriched silicon5–9,16,18,20,22,27,29,31–37, the all-electrical and low-crosstalk-control of partial swap operations1,22–28 and the configurable insensitivity of our encoding to certain error sources28,33,34,38 all combine to offer a strong pathway towards scalable fault tolerance and computational advantage.

Radar specific emitter identification based on open-selective kernel residual network

Radar specific emitter identification (SEI) plays important roles in spectrum resource management and Internet of Things (IoT) security. This term refers to the identification of different individual devices of the same type by analyzing the unintentional modulation features caused by hardware imperfections (i.e., fingerprint features). Increasingly many researchers are using deep learning (DL)-based methods for SEI. However, DL-based methods still have many problems, including model generalization, recognition efficiency, and adaptability in open electromagnetic environments. This paper proposes a one-dimensional convolutional neural network (1D CNN)—called a selective kernel residual network (SKResNet)—capable of autonomously adjusting the receptive field, to improve the feature extraction capability. The transient part is cut efficiently from a radar signal sequence as the input of SKResNet using a universal preprocessing method. In addition, considering that most existing methods obtain incorrect results while identifying new emitters without any prior knowledge, SKResNet was improved using OpenMax—an open-set recognition method—to develop Open-SKResNet. As this network could identify unknown emitters effectively, generating reliable classification results of known emitters simultaneously, it could be of great importance and have broad application prospects.

A Reference-Free Calibration for Ultrafast Spatiotemporally Encoded 2-D NMR Spectroscopy

Spatiotemporal encoding (SPEN) is a state-of-the-art nuclear magnetic resonance (NMR) technique, utilized to acquire 2-D or even multidimensional ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -D) NMR spectra and/or images in a single (or a few) scans. Benefitting from special encoding and decoding schemes, SPEN can thus accelerate the classical 2-D NMR experiment by orders of magnitude. SPEN’s decoding in particular is executed under the effects of oscillating magnetic field gradients; inevitable hardware imperfections may then give rise to inconsistencies between odd and even SPEN data lines, making only half (odd or even) of the acquired data available for processing. This halves the spectral bandwidth available in the direct dimension and decreases the spectral sensitivity, leading to the emergence of peak folding artifacts and/or a decline of the spectral quality. Past research has been carried out to correct these distortions induced by gradient imperfections, but requires an extra reference scan collected under identical experimental conditions. This work presents a detailed theoretical analysis of the impacts of various imperfections on the 2-D SPEN NMR spectrum and proposes a three-step reference-free calibration algorithm in accordance with theoretical analysis. Experimental data on single-shot 2-D correlation spectroscopy (COSY) and heteronuclear single-quantum correlation (HSQC) NMR spectra collected using SPEN were performed and processed, which demonstrate the feasibility and applicability of the proposed reference-free algorithm.

Error Probability of a Coherent M-ary PSK FSO System Influenced by Phase Noise

In this paper, we aim to develop an analytical framework for design and analysis of new generation mobile networks fronthaul/backhaul links based on the application of free-space optical (FSO) technology. Taking the receiver hardware imperfections into account, we present an efficient analytical approach in analyzing average symbol error probability (SEP) of the coherent FSO system employing M-ary phase-shift keying (PSK). Optical signal transmission is influenced by pointing errors and atmospheric turbulence. The signal intensity fluctuations caused by atmospheric turbulence are modeled by general Málaga (M) distribution, which takes into account the effect of multiple scattered components. We estimate the range of the signal-to-noise ratio at which the SEP floor appears, as well as the value of this non-removable error floor. The results illustrate that the effect of imperfect phase error compensation on the SEP is more critical under weaker turbulence conditions and for higher order modulation formats. Based on the analytical tools presented here, it is possible to estimate tolerable value of standard deviation of phase noise for the given value of SEP. This value of standard deviation is an important parameter in designing the phase-locked loop filter in the receiver.

Adaptive RF Fingerprints Fusion via Dual Attention Convolutions

In recent years, with the rise of intelligent hardware technology, the Internet of Things (IoT) has achieved rapid development and has been widely used in smart cities, smart power grids, industrial Internet, and other fields. The resulting security risks of IoT have attracted more and more attention. Being different from the traditional authentication that is based on MAC or security certificate, RF fingerprinting technology extracts fingerprints from the emissions of wireless transmitters. These fingerprints root in the hardware imperfection of the transmitting circuits and can be used for wireless device identification and authentication. RF fingerprinting can effectively enhance the security of the wireless network and has become a research focus in the field of IoT. However, how to learn and fuse multiple RF fingerprints is still an urgent problem to be solved. Inspired by the attention mechanism in the field of computer vision, an adaptive RF fingerprints fusion network (ARFNet) is proposed in this article, which is built on our dual attention convolution (DAConv) layer. This neural network can extract and adaptively fuse multiple RF fingerprints in a data-driven manner to obtain more discriminant features. In addition, a data augmentation method is also designed to effectively enhance the network’s robustness to channel variation and SNR variation. Extensive experimental results show that the proposed ARFNet combined with data augmentation can achieve 99.5% recognition accuracy on five USRP X310, and achieve 95.7% recognition accuracy on 56 ADS-B devices. Our source code has been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangweifeng1218/Adaptive_RF_Fingerprinting</uri> .

Concentrate on Hardware Imperfection via Aligning Reconstructed States

Specific emitter identification (SEI) is to authenticate communication devices via analyzing unintentional modulation (UIM) from hardware imperfection. A great challenge existing in current methods is the interference of intentional modulation (IM) and channel noise. To address this issue, this letter firstly analyzes how IM, UIM, and additive noise (AN) affect reconstructed states, which describes the dynamic characteristics of communication devices. The findings indicate that IM and UIM bring a scaling transform, while AN corresponds to a translation transform. Based on theoretical analysis, a novel SEI method concentrating on UIM is proposed. An advanced PointNet architecture is designed with stacked spatial transformer networks (STN), namely PointNet-Alignment Point by Point (PointNet-APBP). PointNet-APBP aligns reconstructed states into a canonical space and extracts spatial interaction as fingerprint features. Simulated and real-world experiments validate that the proposed method can mitigate the effects of IM as well as AN, concentrate feature extractors on UIM, and in turn achieve higher accuracy and stronger robustness.

Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.

A Novel Linear Precoder Design for Reliable UL/DL Detection in TDD Cellular Networks

The ever-growing demands on wireless connectivity, especially with the emergence of various data-intensive low-latency applications, require novel multiplexing solutions capable of reliably supporting high rates at low latency. Non-orthogonal time division duplex (TDD) coupled with multiuser detection can meet these emerging needs, provided that accurate channel state information is available. This paper proposes a new pilot-free TDD frame structure that allows designing highly effective multiuser decoders and precoders for uplink and downlink multicell systems, in an unsupervised manner. The key idea is that each user repeats and permutes its uplink data using a pre-assigned permutation code. Invoking canonical correlation analysis (CCA) at the serving BS on the two deinterleaved uplink blocks yields high quality CCA-based beamformers capable of both recovering the uplink and precoding the downlink user signals in a way that effectively mitigates interference. The paper includes a pilotless synchronization framework that leverages CCA to recover the timing and frequency offsets in an asynchronous multiuser MIMO setup, without using pilots. Simulations are used to study the performance of the proposed approach on a large-scale network with multiple users and cells, while laboratory experiments with a small-scale network of software radios are used to demonstrate that the approach works well in practice under common hardware imperfections.

On the impact of IQI on cooperative NOMA with direct links in the presence of imperfect CSI

Cooperative non-orthogonal multiple access (CNOMA) is considered as a promising technique to improve network coverage, reliability and transmission for future wireless communication networks. Meanwhile, transceivers can suffer from a number of hardware imperfections that will significantly reduce their performance, such as phase noise and in-phase/quadrature-phase imbalance (IQI). In this paper, we investigate the effect of in-phase and quadrature-phase imbalance (IQI) on CNOMA with direct links in the presence of imperfect channel state information (ICSI). The outage probability (OP) and throughput expressions are derived to evaluate the performance behaviors of the CNOMA with direct links under the IQI and ICSI imperfections. Theoretical analyzes are verified by Monte Carlo simulation. The effect of the IQI on the CNOMA with the direct links has been studied with different parameters (image rejection ratio (IRR), power allocation) and compared with conventional NOMA to clearly observe the degrading effects of imperfections on the systems. The simulation results demonstrate that the IQI and ICSI have a negative impact on the outage and throughput performance.

A Novel Extreme-Learning-Machine Aided Receiver Design for THz-SM With Hardware Imperfections

Terahertz (THz) communication is promising as it can enable ultra-wide-band and ultra-high-rate for various emerging communication services. In this letter, we propose to exploit the extreme learning machine (ELM) network based regressor for simple and low-complexity joint channel estimation (CE) and signal detection (SD) for THz-band spatial modulation (THz-SM) communications impaired by hardware imperfections. Computer simulations show the performance superiority of the proposed joint CE/SD scheme when compared with the state-of-the-art schemes, and other machine learning-based ones, including the support vector machine (SVM), deep neural network (DNN) and some variants of ELM. Specifically, we show that its bit error rate (BER) performance approaches to that of the recently derived maximal likelihood (ML) SD. In addition, the robustness of the proposed scheme is validated by considering two types of background impulsive noises.

Performance Analysis and Optimization for RIS-Assisted Multi-User Massive MIMO Systems With Imperfect Hardware

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The paper derives approximate expressions for the ergodic achievable rate in closed forms under Rician fading channels. For the cases of infinite numbers of antennas and infinite numbers of reflecting elements, asymptotic data rates are derived to provide new design insights. The derived power scaling laws indicate that while guaranteeing a required system performance, the transmit power of the users can be scaled down at most by the factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{M}$</tex-math></inline-formula> when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> goes infinite, or by the factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{MN}$</tex-math></inline-formula> when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> go infinite, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> is the number of antennas and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of the reflecting units. Furthermore, an optimization algorithm is proposed based on the genetic algorithm to solve the phase shift optimization problem with the aim of maximizing the sum rate of the system. Additionally, the optimization problem with discrete phase shifts is considered. Finally, numerical results are provided to validate the correctness of the analytical results.

Distance Measurement Using mmWave Radar: Micron Accuracy at Medium Range

This work provides a proof-of-concept for a linear-position sensor using an ultrawideband (UWB) frequency-modulated continuous wave (FMCW) radar system operating at 126–182 GHz. It is the first work to show environmental compensated and calibration-free distance measurements with micron accuracy at medium range using millimeter-wave (mmWave) radar technology. We addressed hardware imperfections, parameter estimation, and the free-space path, i.e., refractive index and near-field effects. The proposed signal processing chain is robust to interference and of low computational cost. Experiments reveal a systematic error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm 1~\mu \text{m}$ </tex-math></inline-formula> over 4.8 m (0.8–5.6 m), and a random error at a minimum of 30 nm, providing very high sensitivity.

Low RF-Complexity Digital Transmit Beamforming for Large-Scale Millimeter Wave MIMO Systems

Digital beamforming (DBF) with full Radio-Frequency (RF) complexity requires a separate RF chain per transmit antenna, which is not only difficult to realize for large-scale MIMO systems but also vulnerable to hardware imperfection. In this paper, we show that DBF architectures don’t necessarily require the number of RF chains to be the same as the number of transmit antennas. We propose a low RF-complexity transmit DBF architecture that enables to use a small number of RF chains to serve a large number of transmit antennas. In our architecture, the transmit antennas are divided into groups. All antennas in the same group share the same RF chain in a time-multiplexed manner to preserve the signal from baseband till the antenna aperture. A novel antenna grouping algorithm is proposed to dynamically group the transmit antennas in a way that each antenna group forms a close-to-rank-one channel matrix with the receive antennas. Under ideal hardware conditions, we show that our DBF architecture can achieve nearly the same performance as that of the conventional full RF-complexity DBF in terms of bandwidth and spectral efficiency. However, when hardware imperfection is considered, our architecture outperforms full RF-complexity DBF in both spectral and energy efficiency because it is more robust to inter RF-chain cross-talk effects due to the reduction on the number of required RF chains.

Outage Performance Analysis of RIS-Assisted UAV Wireless Systems Under Disorientation and Misalignment

In this paper, we analyze the performance of a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) wireless system that is affected by mixture-gamma small-scale fading, stochastic disorientation and misalignment, as well as transceivers hardware imperfections. First, we statistically characterize the end-to-end channel for both cases, i.e., in the absence as well as in the presence of disorientation and misalignment, by extracting closed-form formulas for the probability density function (PDF) and the cumulative distribution function (CDF). Building on the aforementioned expressions, we extract novel closed-form expressions for the outage probability (OP) in the absence and the presence of disorientation and misalignment as well as hardware imperfections. In addition, high signal-to-noise ratio OP approximations are derived, leading to the extraction of the diversity order. Finally, an OP floor due to disorientation and misalignment is presented.

Analytical Evaluation of Power-Amplifier-Based Charging Methodology and Energy Efficiency Optimization Framework for Aerial Base Stations

In this article, a radio-frequency (RF) power-amplifier (PA) system configuration for the on-board energy conversion is proposed for unmanned aerial vehicle base station’s (UAV-BS’s) battery charging. First, the PA system utilizes the internal RF source for energy conversion so that perpetual battery charging is possible without external power sources. Therefore, a continuous battery charging of the UAV-BS is possible while providing wireless network coverage by hovering over the target location. In addition, the proposed PA system offers a higher RF power control resolution than the existing PA works, which is desirable for precise power control of the UAV-BS. The objective of simultaneous RF power control and battery charging by the PA system is achieved by activating one Wilkinson power divider (WPD) with a specific power splitting ratio in the power divider bank. The simulations show that the dynamic activation of ten distinct unequal split WPDs of the proposed PA system improves the RF power control resolution by $157$ and $396{\,}\%$ compared to the existing PAs. Additionally, the simulations show that the PA’s power added efficiency fluctuation is reduced by $54$ and $82{\,}\%$ compared to the existing PAs. In addition, it will be shown that the proposed on-board battery charging increases the hovering time by $7.4{\,}\%$. Furthermore, an energy-efficiency optimization framework for UAV-BS’s is proposed considering real-world communication system’s hardware imperfections, and the proposed result suggests that UAV-BS’s flight time may decrease by $50{\,}\%$ depending upon the type of UAV-BS RF components.

A method for delay estimation between channels of Analog to Information Converters

Analog to Information Converters (AIC) implements signal acquisition in Compressed Sensing. AIC performance is affected by noise and hardware imperfections. In parallel Random Modulator Pre-Integrator (RMPI) architectures, delay among channels reduces the performance; thus, delay estimation and compensation methods are necessary. In previous works, delay estimation compares a reference signal with a reconstructed signal from the AIC output; however, such methods produce different results according to the recovery method. To avoid this problem, we present a method based on a controlled delay signal to estimate channel delay without the necessity of signal recovery. The obtained results show the technique's feasibility and the possibility of its use as a built-in calibration process for AIC.

A practical radio frequency fingerprinting scheme for mobile phones identification

Radio frequency fingerprint (RFF) has attracted a remarkable surge of attention to wireless transmitter identification due to the inimitable hardware imperfection. However, most RFF-based schemes devised in controlled environments are unable to reach the claimed performance in real-world scenarios. This paper proposes a practical RFF identification method for mobile phones in Global System for Mobile (GSM) system. Specifically, the instantaneous amplitude of near-transient part in normal burst (NB) is regarded as RFF that extracted from up-link communications. In addition, an anomaly filtering and stacking (AFS) method is introduced to obtain stable RFF in Time Division Multiple Access (TDMA) system. Lastly, the impacts of frequency point and sending power variations on RFF are fully dissected in comparison experiments. Experiments on 10 mobile phones show that the proposed RFF scheme yields 99.17% True Acceptance Rate (TAR) in real wireless environments. Experiments also show that the varying transmission power decreases the accuracy of RFF identification.

Drastically reduced sensor hardware for solvable chaos‐based sonar

An acoustic ranging demonstration is presented in a reflective environment using solvable chaos with novel and practical hardware advances. Previously, solvable chaos was optimally detected using ideal alignment, random codes and an 11 element microphone array for environments free of multipath reflections. In this work, ranging experiments were performed with drastically reduced sensor hardware and measurements were extended to include non-ideal effects such as multipath and angle misalignment from reduced line of sight (LOS). Importantly, this new scheme not only reduces the size and power of these systems by over 83%, a novel triggering technique allows for triggering on targeted bit sequences and produces sidelobe levels that are independent of oscillator dynamics. Ranging results from a closed, reflective cavity obtained using a 10 kHz solvable chaotic oscillator are reported. Measured sidelobe levels were typically between −1.80 dB and −15.85 dB. The resulting demonstration shows successful ranging despite drastically reduced hardware and environmental imperfections.

Quantum Optimal Control: Practical Aspects and Diverse Methods

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise. Over the last 2 decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.

Estimation and Compensation of IQ Imbalance for OTSM Systems

Recently, orthogonal time sequency multiplexing (OTSM) waveform was proposed using Walsh Hadamard transform (WHT) for doubly-dispersive channels at a lower modulation/demodulation complexity than orthogonal time frequency space (OTFS) waveform. At high frequencies, hardware imperfections such as in-phase and quadrature (IQ) imbalance is prominent at the transceiver. In this letter, the input-output (I/O) relation in the delay-sequency domain is derived under these impairments at the receiver. The impairment parameters are estimated in the delay-time domain and are compensated in the time domain using proposed dual pilot approach. The normalized mean square error (NMSE) of the estimated imbalance parameters is evaluated and it is observed that NMSE reduces with increase in SNR and with reduction in mobility. The results are evaluated with OTFS system and it is observed that the NMSE performance of OTSM is similar to OTFS under high pilot power and high SNR at no excess pilot power. The bit error rate performance of the compensated system is evaluated and its performance is close to the ideal system.