Search

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy, wind-thermal-bundled power transmission by line-commutated converter based high-voltage direct current (LCC-HVDC) systems has been widely developed. The dynamic interaction mechanisms among permanent magnet synchronous generators (PMSGs), synchronous generators (SGs), and LCC-HVDC system become complex. To deal with this issue, a path analysis method (PAM) is proposed to study the dynamic interaction mechanism, and the damping reconstruction is used to analyze the damping characteristic of the system. First, based on the modular modeling, linearized models for the PMSG subsystem, the LCC-HVDC subsystem, and the SG subsystem are established. Second, based on the closed-loop transfer function dia-gram of the system, the disturbance transfer path and coupling relationship among subsystems are analyzed by the PAM, and the damping characteristic analysis of the SG-dominated oscillation mode is studied based on the damping reconstruction. Compared with the PAM, the small-signal model of the system is obtained and eigenvalue analysis results are presented. Then, the effect of the control parameters on the damping characteristic is analyzed and the conclusions are verified by time-domain simulations. Finally, the penalty functions of the oscillation modes and decay modes are taken as the objective function, and an optimization strategy based on the Monte Carlo method is proposed to solve the parameter optimization problem. Numerical simulation results are presented to validate the effectiveness of the proposed strategy.

Supercavitating vehicles have received significant attention in military applications due to their high underwater speed. This paper aims to establish analytic models of the encounter probability between a supercavitating vehicle and its target in two modes: straight-running mode (SRM) and turning-straight-running mode (TSRM). Mathematical models for the supercavitating vehicle to encounter the target in SRM and TSRM are proposed. An improved particle swarm optimization (PSO) algorithm based on local best topology with a penalty function is introduced to plan the TSRM path of the supercavitating vehicle. The particle swarm position is updated by using Cauchy and Gaussian distributions. The judgement indexes of the encounter probability analytic models in SRM and TSRM are determined through statistical analysis. Based on the law of error propagation, the analytic models of judgement index variances in the two modes are derived by using the implicit function differential method, and the integral intervals of their probability density functions are obtained according to the relative motion between the supercavitating vehicle and the target. Monte Carlo simulations and hypothesis testing are utilized to verify the accuracy and feasibility of the encounter probability analytic models in SRM and TSRM.

In this article, the estimation of random channel gain is studied for a single-input single-output (SISO) visible light communication (VLC) system. Five different estimators, namely maximum likelihood (ML), least square (LS), maximum posteriori probability (MAP), linear minimum mean square error (LMMSE), and minimum mean square error (MMSE), are proposed. The performances of these estimators are compared with the derived Bayesian Cramér–Rao lower bound (BCRLB), which can be used as a benchmark to evaluate the efficiency of the unbiased estimators. The presented analytical results, corroborated with Monte Carlo simulations, indicate that the MMSE estimator provides the best results. Additionally, the increasing number of pilot symbols as well as the ascending transmitted power improve the system performance. On the other hand, the noise variance has a negative effect on the channel estimation in terms of mean square error (MSE), and thus, it can dramatically reduce the performance of the estimators. [ABSTRACT FROM AUTHOR]

The feasibility of underwater polarization navigation technology in low visibility waters, especially in large depths, has always been questioned. To explore the influence of depth on underwater polarization navigation technology, we study the ocean polarized light fields at different depths in this paper. We first establish the model of the coupled atmosphere-surface-underwater optical transmission, which can simulate the underwater polarization patterns at different depths. Then underwater detection equipment with ROV and polarization camera is constructed and we use it for underwater experiments. Finally, we research the effects of other environmental factors through experiment and simulation. Our study provides theoretical references for the application of underwater polarization navigation. Based on previous research work, this paper builds a relatively complete underwater polarization transmission model, which improves the polarization transmission theory. Underwater experiment results further prove the feasibility of underwater polarization navigation technology at deep depths. Due to the robustness of polarized light, polarization navigation technology still has great application potential in low visibility underwater.

In this article, an automatic vehicle parallel parking algorithm, consisting of path planning, controller design, and state estimation is developed. The path is planned using clothoid sequences and a straight line, which avoids stopping the car to reorient the wheels. The control inputs, including speed and steering angle, are a function of traveled distance. This method enables the car to park from different initial poses, achieving reduced parking time and the ability to park in one or two maneuvers, in smaller than standard places. An evolutionary optimization algorithm is used to calculate the best speed parameter according to the defined criteria. The proposed technique utilizes the Unscented Kalman Filter (UKF) to estimate the traveled distance, resulting in a smaller error compared to the conventional Extended Kalman Filter (EKF). The research aims to introduce an optimal automatic parking algorithm to improve the existing methods in terms of parking duration, the required space size for parking in the maximum of two maneuvers, and path continuity. Finally, the fidelity and improved performance of the proposed method are assessed in various probable conditions using the powerful Monte Carlo simulations.

Optical wireless data transmission from an autonomous underwater vehicle (AUV) to a sea-surface buoy has become a viable solution to support the increasing needs for many applications in the field of marine technology. Despite having unique characteristics and advantages, underwater wireless optical communication remains a challenging field due to the difficulties associated with the harsh marine environments and severe attenuation caused by the bubbles and particulates. This study aims to evaluate the uplink UWOC system in the presence of particles and bubbles under various wind speeds. To calculate the link distances and signal loss between a transmitter and a receiver of the UWOC system, simulations were performed with the inputs of the water optical properties obtained from the Bay of Bengal and Southern Ocean waters and bubble properties derived from the Hall-Novarini (HN) model. Consequently, the normalized received power were calculated for the different receiver configurations and oceanic conditions without and with bubbles. Our results showed that the received power decreased with the increasing wind speeds and smaller bubble populations. The angular and spatial distributions of the received beam increased for smaller bubble populations under high wind speeds. When compared to the clean bubbles, the received power slightly decreased for the non-absorbing coated bubbles with different film thicknesses (0.01 ∼ 1.0 μm) and increased for the absorbing coated bubbles with higher thicknesses (≥1 μm). These results will significantly help the system designer to develop and optimize an UWOC system under different bubble populations and wind speed conditions.

In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses greater than 0 and minimum responses less than 0 are selected and regarded as the candidate samples; then, a U-based learning function is developed in which multiple samples of the interval are considered instead of one particular sample. To terminate the proposed method, a hybrid convergence criterion is proposed. Finally, an improved optimization strategy based on the DIRECT algorithm is developed for the Monte Carlo simulation conducted for the HRA. The performance of the proposed method is demonstrated by four numerical cases. The results illustrate that the proposed method is accurate and efficient for HRA. [ABSTRACT FROM AUTHOR]

Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV’s mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEMs) of realistic terrain for reliability analysis. Subsequently, we propose a reliability-prediction method and derive the corresponding metric to evaluate the system’s ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte Carlo simulations show that in mountainous environments, the proposed reliability-prediction method could achieve a prediction accuracy that is at least 36.8 $\%$ higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability-enhancement method improves the service reliability of the proposed system by 23 $\%$ and 29 $\%$ , respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have demonstrated their potential in a variety of domains ranging from computer vision (CV) to natural language processing. Among various NNs, two-dimensional (2-D) and three-dimensional (3-D) convolutional NNs (CNNs) have been widely adopted for a broad spectrum of applications, such as image classification and video recognition, due to their excellent capabilities in extracting 2-D and 3-D features. However, standard 2-D and 3-D CNNs are not able to capture their model uncertainty which is crucial for many safety-critical applications, including healthcare and autonomous driving. In contrast, Bayesian CNNs (BayesCNNs), as a variant of CNNs, have demonstrated their ability to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BayesCNNs have not been widely used in industrial practice due to their compute requirements stemming from sampling and subsequent forward passes through the whole network multiple times. As a result, these requirements significantly increase the amount of computation and memory consumption in comparison to standard CNNs. This article proposes a novel field-programmable gate array (FPGA)-based hardware architecture to accelerate both 2-D and 3-D BayesCNNs based on Monte Carlo dropout (MCD). Compared with other state-of-the-art accelerators for BayesCNNs, the proposed design can achieve up to four times higher energy efficiency and nine times better compute efficiency. An automatic framework capable of supporting partial Bayesian inference is proposed to explore the tradeoff between algorithm and hardware performance. Extensive experiments are conducted to demonstrate that our framework can effectively find the optimal implementations in the design space. [ABSTRACT FROM AUTHOR]

Regularized logistic regression is a popular classification tool that can be employed to accurately model the binary nature of the memory cell fail mechanisms for purposes of the yield analysis of memory designs. The iterative reweighted least squares (IRLS) method has been employed along with the least angle regression (LARS) to efficiently solve the $L_{1}$ regularized logistic regression problem. In this brief, we propose an efficient $L_{1}$ regularized logistic regression methodology. At the core lies a Group LARS-based approach that benefits from Group LARS inherent ability to handle groups of variables and exploits the natural evolution of the solution to speed up the search for the critical features of the classifier. Thus, it tracks Newton’s step direction from one round of the solution to the next and employs weighted directions to efficiently solve for the underlying $L_{1}$ constrained iterative least squares problem. We apply the methodology in the context of an importance sampling-based yield analysis framework targeting rare fail probability estimation. We study the yield of 14-nm FinFET SRAM designs with programmable and resonant boosting. Our results demonstrate up to $14\times $ – $20\times $ speedup for the Group LARS compared to the pure LARS-based approach, and we report 98.7% accuracy and 0.12 $\sigma $ average error compared to pure circuit-simulations approach for the resulting classifier. [ABSTRACT FROM AUTHOR]

The effect of negative bias temperature instability (NBTI) varies significantly according to given workloads. Finding a feasible worst case workload is difficult due to logical correlation within the logic circuit under consideration. In this article, we propose an NBTI-aware subset simulation (SS) framework that efficiently and accurately finds the failure probability covering various input duty cycles determined by different workloads. In addition, the proposed method is incorporated with the NBTI mitigation technique to facilitate workload-aware mitigation. Through numerical experiments using benchmark circuits, the proposed method achieves up to 36 times speedup compared to a naive Monte Carlo method. The NBTI mitigation based on SS demonstrates $1.78\times $ better mitigation for multiple input duty cycles compared to the conventional method. [ABSTRACT FROM AUTHOR]

Multiobjective model predictive control (Mo-MPC), owing to its simplicity and adaptability to comply with diverse constraints, has gained popularity in the control of direct matrix converters (DMCs). However, the MPC strategy makes the converter operate at an uncontrolled and variable switching frequency, which may inhibit high power density applications due to increased power losses. This article proposes a method for controlling the switching frequency of the converter and simultaneously regulating the common mode voltage in the DMC fed system while maintaining sinusoidal load and source currents at near unity input power factor. Furthermore, in order to streamline the control while simultaneously dealing with these objectives, a suitable normalization technique is implemented for scaling the errors in each objective. This method is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests. [ABSTRACT FROM AUTHOR]

Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Gas-insulated equipment such as GIS (Gas-Insulated Substation) and GIL (Gas-Insulated transmission lines) faces critical insulation failure challenges caused by particles inside. However, the particle traps, as key devices available to inhibit the movement of particles, lack theoretical basis and quantitative methodology for trap design especially with high voltage ratings. In this paper, a particle motion excitation and observation platform is established to physically simulate the actual online operating conditions of HVAC/HVDC GIS/GIL and study the capture mechanism of particle traps. Experimental results illustrate that, the collision motion style is a principal factor to affect the particle capturing capability, and the capture behavior of AC and DC particle traps can be classified into three separate modes, depending on variation of the collision process which can be characterized by random probability and the lognormal distribution rebound angle. A particle trajectory simulation model incorporating motion randomness is thereby presented to realize quantitative analysis and further optimize the trap parameters. A hazard index and minimum collision times are defined and calculated to quantitatively evaluate the particle collision effect on trapping capability. Further, a novel trap design topology is proposed to enhance the anti-motion efficiency of the particle traps. Targeted at high objective capture probability, the particle trap parameters are optimized to reduce the hazard index and improve the minimum collision times by the Monte Carlo scheme. The obtained results indicated that, the capture probability of AC traps can reach 86.5% at a gap spacing of 5 mm, while that of DC traps can reach 74.1% with an 8 mm-width slot and a 50-degree inclination angle. Verification of the novel article trap is implemented on a test rig, which gives an increased capture probability by 30% compared with the traditional trap design. The proposed new trap topology and corresponding methodology present quantitative guidance for particle traps optimization and render application potential for different voltage levels. [ABSTRACT FROM AUTHOR]

This letter analyzes the velocity-field relationship of AlGaN/GaN HFETs via Monte Carlo simulation. This method is based on complete scattering mechanisms such as polarized coulomb field scattering (PCF scattering), polar optical phonon scattering, acoustic deformation potential scattering, inter-valley phonon scattering, and piezoelectric scattering. It is found that the gate bias voltage affects the PCF scattering intensity and the PCF scattering intensity influences the drift velocity under a high electric field. A decrease in peak steady-state drift velocity is observed for our fabricated AlGaN/GaN HFETs from ${1.335}\times {10}^{{5}}$ m/s at $\text{V}_{\text {gs}} =$ 0 V to ${1.23}\times {10}^{{5}}$ m/s at $\text{V}_{\text {gs}} =-3$ V as the gate voltage becomes more negative. When combined with the velocity-field relationship obtained from Monte Carlo simulations, the calculated current-voltage characteristics of AlGaN/GaN HFET devices are in good agreement with our experiment results. The electron saturation drift velocity deviation of AlGaN/GaN HFETs from the Monte Carlo simulation of the GaN material is attributed to the presence of PCF scattering in AlGaN/GaN HFETs. In the study, we provide strong support for the accurate modeling of AlGaN/GaN HFETs. [ABSTRACT FROM AUTHOR]

An almost all-digital time-to-digital converter (TDC) possessing subpicosecond resolutions, scalable dynamic ranges, high linearity, high noise immunity, and moderate conversion rates can be achieved by a random sampling-and-averaging (RSA) approach with the self-antithetic variance reduction (SAVR) technique for time-correlated single-photon counting (TCSPC) quantum measurements. This article presents detailed theoretical analysis and behavior-model verifications of the SAVR technique to effectively enhance the conversion rate of an asynchronous RSA-based TDC by more than 62 times with 7% power overhead. In addition, the proposed performance estimation methodology for SAVR can greatly improve the computation efficiency during the system-level design and reduce the read-out circuit complexity in the silicon-photonics RSA-based TCSPC realization.

Due to the randomness of load and renewable energy generation (REG), microgrids face multiple uncertainties. These uncertainties lead to the uncertainty of microgrid operation and bring more challenges to the economic evaluation of microgrids. In this paper, an economic evaluation method for determining microgrid revenue distribution is proposed. Considering the dual uncertainties of source-load and forecast, and temporal autocorrelation of time series, the probabilistic model of uncertainties is established by multivariate kernel density estimation (KDE). Then the random scenarios including forecasting values are generated and used in optimal dispatch calculation for the detailed production simulation. The probabilistic revenue is derived with a method based on Monte Carlo method. Finally, a case study is carried out based on the real data of an industrial park. The results demonstrate the necessity and effectiveness of the probabilistic revenue analysis proposed in this paper. This method can reveal the actual values of each component of a microgrid (e.g., device or algorithm) in specific scenes and provides more insights into investment decisions.

Many inverse problems in machine learning, system identification, and image processing include nuisance parameters, which are important for the recovering of other parameters. Separable nonlinear optimization problems fall into this category. The special separable structure in these problems has inspired several efficient optimization strategies. A well-known method is the variable projection (VP) that projects out a subset of the estimated parameters, resulting in a reduced problem that includes fewer parameters. The expectation maximization (EM) is another separated method that provides a powerful framework for the estimation of nuisance parameters. The relationships between EM and VP were ignored in previous studies, though they deal with a part of parameters in a similar way. In this article, we explore the internal relationships and differences between VP and EM. Unlike the algorithms that separate the parameters directly, the hierarchical identification algorithm decomposes a complex model into several linked submodels and identifies the corresponding parameters. Therefore, this article also studies the difference and connection between the hierarchical algorithm and the parameter-separated algorithms like VP and EM. In the numerical simulation part, Monte Carlo experiments are performed to further compare the performance of different algorithms. The results show that the VP algorithm usually converges faster than the other two algorithms and is more robust to the initial point of the parameters. [ABSTRACT FROM AUTHOR]

Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system’s development and verification activities (VAs) are executed as the development progresses. Such an approach appears to give inferior results as the selection of the VAs does not leverage information gained through the system’s development process. In contrast, a set-based design (SBD) approach to verification, where VAs are dynamically selected as the system’s development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate an exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stochastic robust real-time power dispatch model considering wind uncertainty. In this dispatch model that incorporates automatic power generation control, system constraints are constructed with affinely adjustable robust optimization to enhance the system security, while the objective concerning the expected value of generation cost is optimized to improve the cost efficiency. The optimized objective is consistent with stochastic optimization, while the formulation for the objective is different. A Nataf conversion-based three-point estimate (NC-TPE) is adopted, which uses an approximating function to formulate the expected generation cost with deterministic estimate points. Compared to traditional stochastic sampling methods, the deterministic assessment method NC-TPE achieves an accurate formulation with more stable and efficient computation performance. The dispatch model is derived as a problem with nonconvex objective and bilinear constraints using the dual theory. To address the highly nonconvex problem, the objective and bilinear constraints are first converted to difference-of-convexity functions, and then a tailored difference-of-convexity optimization is employed to convexify the nonconvex functions, such that the original problem can be solved with an iterative quadratically constrained program. Numerical simulations demonstrate the economic superiority and robustness of the proposed model, as well as the effectiveness of the convexified solution. [ABSTRACT FROM AUTHOR]

The integration of uncertain photovoltaics (PVs) and flexible loads leads to uncertainties in the power system dynamic simulation results. Furthermore, geographically close PV farms are correlated and may exhibit nonlinear correlations. This article proposes a copula-based sparse polynomial chaos expansion (PCE) framework for quantifying the impacts of uncertain dynamic PVs and loads on power system dynamic simulations and stability. The dynamics include both PV and load stochasticity and those governed by differential and algebraic equations. The copula statistics are utilized to accurately characterize the dependence structure of PVs and further used to develop the copula-PCE for quantifying the impacts of uncertain PVs and loads. A probabilistic TSI is also developed to assess the uncertainties from PVs and loads on the system stability. To address the cases, where both stable and unstable conditions coexist, a preprocessing step via sample classification is proposed. The effects of different dependence structures of PVs and different numbers of uncertain sources are investigated. Comparison results with other methods on the modified IEEE 39- and 118-bus systems, including the Monte Carlo method, Latin hypercube sampling, and traditional PCE without consideration of uncertain input correlations show that the proposed method is able to accurately quantify the uncertain dynamic simulations and transient system stability while being computationally efficient. [ABSTRACT FROM AUTHOR]

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of typical kernels, e.g., Gaussian kernels, arc-cosine kernels, are fully symmetric, we leverage a numerical integration technique, deterministic fully symmetric interpolatory rules, to efficiently compute quadrature nodes and associated weights for kernel approximation. Thanks to the full symmetric property, the applied interpolatory rules are able to reduce the number of needed nodes while retaining a high approximation accuracy. Further, we randomize the above deterministic rules by the classical Monte-Carlo sampling and control variates techniques with two merits: 1) The proposed stochastic rules make the dimension of the feature mapping flexibly varying, such that we can control the discrepancy between the original and approximate kernels by tuning the dimnension. 2) Our stochastic rules have nice statistical properties of unbiasedness and variance reduction. In addition, we elucidate the relationship between our deterministic/stochastic interpolatory rules and current typical quadrature based rules for kernel approximation, thereby unifying these methods under our framework. Experimental results on several benchmark datasets show that our methods compare favorably with other representative kernel approximation based methods. [ABSTRACT FROM AUTHOR]

To obtain the transient current law of typical coaxial cable irradiated by intense pulsed gamma rays, the irradiation effect experiment of intense pulsed gamma rays on typical coaxial cable was carried out on the “Qiangguang-1” accelerator, and the relationship between the amplitude of pulsed current in the center conductor of coaxial cable and the instantaneous gamma dose rate was measured. The simulation model of irradiation effect of intense pulsed gamma rays on the coaxial cable is presented. By using the Monte Carlo particle transport method, the electron charge deposition rate in the center conductor and the outer shielding conductor of different types of coaxial cables under intense pulsed gamma irradiation is calculated. The calculated results are basically consistent with the experimental results. When the gamma ray energy is greater than 0.6 MeV, the electron charge deposition rate in the center conductor of coaxial cable first increases, then decreases, and then increases with the increase of gamma ray energy. When the gamma ray energy is greater than 0.1 MeV, the number of electrons deposited in the center conductor of the cable per unit length increases with the increase of gamma ray energy. The number of electron charges deposited in the outer shielding conductor layer is greater than that deposited in the center conductor for typical coaxial cable. The gamma radiation sensitivity of the coaxial cable increases at the same gamma ray intensity when the cable is obliquely irradiated. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging strategy to improve the energy efficiency of many error-tolerant applications. To design an approximate circuit automatically, many approximate logic synthesis (ALS) methods have been proposed, among which many are greedy. To improve the synthesis quality of these greedy methods, one key is to calculate the errors of all candidate approximate transformations accurately. However, the traditional simulation-based method is time consuming. Instead, many existing methods just perform quick but inaccurate error estimation. In this work, to improve both the accuracy and runtime of error estimation, we propose VECBEE, a versatile efficiency–accuracy configurable batch error estimation method for greedy ALS. It is based on Monte Carlo simulation and an efficient technique to capture whether a signal change due to an introduced approximation will be propagated to each primary output. VECBEE is generally applicable to any statistical error measurement, such as error rate and average error magnitude, and any graph-based circuit representation. It allows a flexible tradeoff between the error estimation accuracy and the runtime, while even the fully accurate version is much faster than the traditional simulation-based method. We apply VECBEE to two representative greedy ALS methods and demonstrate its effectiveness in generating better approximate circuits. The code of VECBEE is made open source. [ABSTRACT FROM AUTHOR]

The design of a blind receiver for unsynchronized bursty communications is a challenging task, especially when the data burst duration is very short and the over-sampling ratio (OSR) at the receiver is a non-integer value. In this article, the problem of blind symbol-rate estimation for bursty communications is studied. A cyclostationary method with bias removal (CS-BR) is proposed to improve the accuracy of the “OSR peak” detection in cyclostationary method when the length of signal burst is very short. Factors which affect the accuracy of the proposed blind symbol-rate estimation are thoroughly analyzed. Both field measurement data simulations and Monte Carlo simulations show that by using our proposed CS-BR method, the accuracy of blind symbol-rate estimation can be significantly improved, and the root-mean-square-error of the estimated OSR can be reduced to as small as $10^{-4}$ even if each short data burst consists of only tens of symbols. [ABSTRACT FROM AUTHOR]

This paper derives the asymptotic distribution of the normalized $k$ -th maximum order statistics of a sequence of non-central chi-square random variables with non-identical non-centrality parameters. We demonstrate the utility of these results in characterizing the signal-to-noise ratio in three different applications in wireless communication systems where the statistics of the $k$ -th maximum channel power over Rician fading links are of interest. Furthermore, we derive simple expressions for the asymptotic outage probability, average throughput, achievable throughput, and average bit error probability. The proposed results are validated via extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In optical fiber communication, due to the random variation of the environment, the state of polarization (SOP) fluctuates randomly with time leading to distortion and performance degradation. The memory-less SOP fluctuations can be regarded as a two-by-two random unitary matrix. In this paper, for what we believe to be the first time, the capacity of the polarization drift channel under an average power constraint with imperfect channel knowledge is characterized. An achievable information rate (AIR) is derived when imperfect channel knowledge is available and is shown to be highly dependent on the channel estimation technique. It is also shown that a tighter lower bound can be achieved when a unitary estimation of the channel is available. However, the conventional estimation algorithms do not guarantee a unitary channel estimation. Therefore, by considering the unitary constraint of the channel, a data-aided channel estimator based on the Kabsch algorithm is proposed, and its performance is numerically evaluated in terms of AIR. Monte Carlo simulations show that Kabsch outperforms the least-square error algorithm. In particular, with complex, Gaussian inputs and eight pilot symbols per block, Kabsch improves the AIR by 0.20 to 0.30 bits/symbol throughout the range of studied signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

In this paper, we consider variable-length coding over the memoryless binary asymmetric channel (BAC) with full noiseless feedback, including the binary symmetric channel (BSC) as a special case. In 2012, Naghshvar et al. introduced a coding scheme, which we refer to as the small-enough-difference (SED) coding scheme. For symmetric binary-input channels, the deterministic variable-length feedback (VLF) code constructed with the SED coding scheme asymptotically achieves both capacity and Burnashev’s optimal error exponent. Building on the work of Naghshvar et al., this paper extends the SED coding scheme to the BAC and develops a non-asymptotic VLF achievability bound that is shown to achieve both capacity and the optimal error exponent. For the specific case of the BSC, we develop an additional non-asymptotic VLF achievability bound using a two-phase analysis that leverages both a submartingale synthesis and a Markov chain time of first passage analysis. Numerical evaluations show that both new VLF achievability bounds outperform Polyanskiy’s achievability bound for variable-length stop-feedback codes. [ABSTRACT FROM AUTHOR]

This article proposes a physics-based control-oriented model and observer design for the state-of-charge (SoC) estimation of lithium-ion cells for applications involving high magnitude fluctuating current profiles. The physics-based single-particle model (SPM) provides enhanced accuracy due to the inclusion of electrolyte dynamics and addresses the issue of nonobservability associated with it. The computationally efficient physics-based model is utilized to design a robust observer-based SoC estimator in the framework of linear matrix inequality to guarantee fast convergence despite parametric uncertainty in the state and output equations, and unknown initial conditions. The observer performance is validated using FTP75 and US06 dynamic tests at different temperatures, and the results are compared with the standard unscented Kalman filter (UKF). The mean SoC estimation error and the integral square error of the estimated SoC for the proposed observer are at least one order of magnitude smaller than that of UKF. Furthermore, robustness to ±30% parametric uncertainty, measurement noise, and unknown initial conditions is demonstrated through Monte Carlo simulations at different temperatures. [ABSTRACT FROM AUTHOR]

A cost-effective and accurate method for predicting over-the-air (OTA) data throughput of multiple-input and multiple-output (MIMO) wireless devices in the antenna design stage is proposed. The method incorporates passive characteristics of MIMO antennas, realistic channel model, and conductive measurement results in a computer simulation framework. With extracted relation between received power and signal-to-noise ratio (SNR) of a wireless device at antenna ports by a conductive test and measured passive characteristics of MIMO antennas, OTA throughput versus received power in a realistic channel model is obtained by the Monte Carlo simulation of Shannon’s law. The unique features of this method include: no channel emulator equipment is required; a wide variety of precoding schemes can be applied; passive characteristics of antennas are fully incorporated; and frequency-selective fading channel can be adopted. The method has been intensively validated by comparing the calculated OTA throughputs with those measured by a multiprobe anechoic chamber method under spatial channel model extension (SCME) in three aspects: spatial correlation, radiation efficiency, and mutual coupling, showing as low as 0.2–0.3 dB discrepancy in power level in most cases. The method can be used to evaluate a MIMO array antenna design of a wireless device with respect to OTA performance. [ABSTRACT FROM AUTHOR]

This work presents a simple grouping method for efficient power pattern tolerance analysis of antennas/radomes using the Monte Carlo method. The discrete elements, as antenna array elements or antenna/radome discretized meshes, are grouped into a limited number of clusters to reduce the number of independent random variables. Three grouping strategies based on Cartesian coordinates, polar coordinates, and random case are presented to group the discrete elements into eight types. A method combining two sets of results with pattern main-lobe-region and sidelobe-region optimal grouping numbers is proposed to improve the tolerance analysis precision. A 60 $\times $ 60 uniformly excited rectangular array antenna with excitation errors and its variants with amplitude taper or circular aperture, and a spherical radome with more than 1100 discrete meshes are considered. The results show that the proposed method can yield desirable electromagnetic (EM) performance variation ranges using 103–2 $\times \,\,10^{3}$ random samplings. Through grouping, the obtained EM performance interval percentages with respect to the interval analysis results are improved from 3%–6% to 47%–88% for the antenna cases and from 1%–2% to 19%–30% for the radome cases, respectively. [ABSTRACT FROM AUTHOR]

Off-policy reinforcement learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current off-policy policy gradient methods either suffer from high bias or high variance, delivering often unreliable estimates. The price of inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited, and a very high sample cost hinders straightforward application. In this paper, we propose a nonparametric Bellman equation, which can be solved in closed form. The solution is differentiable w.r.t the policy parameters and gives access to an estimation of the policy gradient. In this way, we avoid the high variance of importance sampling approaches, and the high bias of semi-gradient methods. We empirically analyze the quality of our gradient estimate against state-of-the-art methods, and show that it outperforms the baselines in terms of sample efficiency on classical control tasks. [ABSTRACT FROM AUTHOR]

This article obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated signal-to-noise ratio (SNR) metrics are defined and their interrelationships analyzed to show that the accuracy of IMCs is fundamentally limited by the compute SNR (${\mathrm {SNR}}_{a}$) of its analog core, and that activation, weight, and output (ADC) precision needs to be assigned appropriately for the final output SNR (${\mathrm {SNR}}_{T}$) to approach ${\mathrm {SNR}}_{a}$. The minimum precision criterion (MPC) is proposed to minimize the analog-to-digital converter (ADC) precision and hence its overhead. Three in-memory compute models—charge summing (QS), current summing (IS), and charge redistribution (QR)—are shown to underlie most known IMCs. Noise, energy, and delay expressions for the compute models are developed and employed to derive expressions for the SNR, ADC precision, energy, and latency of IMCs. The compute SNR expressions are validated via Monte Carlo simulations in a 65 nm CMOS process. For a 512 row SRAM array, it is shown that: 1) IMCs have an upper bound on their maximum achievable ${\mathrm {SNR}}_{a}$ due to constraints on energy, area and voltage swing, and this upper bound reduces with technology scaling for QS-based architectures; 2) MPC enables ${\mathrm {SNR}}_{T}$ to approach ${\mathrm {SNR}}_{a}$ to be realized with minimal ADC precision; and 3) QS-based (QR-based) architectures are preferred for low (high) compute SNR scenarios. [ABSTRACT FROM AUTHOR]

To utilize the full potential of the direct matrix converter (DMC) and improve the output waveform quality, a three-level output strategy for conventional 3 × N (N≥3) DMCs is presented, where three-level output voltages are achieved without any changes in the topology. In this developed strategy, the DMC is treated as a two-stage converter that consists of a fictitious rectifier and a neutral-point clamped (NPC) three-level inverter. By applying a phase-shifted carrier modulation strategy to the rectifier and a modified carrier-based double-signal pulsewidth modulation strategy to the NPC inverter, sinusoidal input and output, reduced switching losses, and controllable input power factors are achieved in conventional 3 × N DMCs. The developed three-level output strategy improves output waveform quality while sacrificing input performance to some extent. Finally, the effectiveness of the developed strategy is verified by both simulations and experimental results. [ABSTRACT FROM AUTHOR]

This paper presented an assessment of lightning energy absorption by dedicated surge arresters (SADT), which are provided to protect the distribution transformers on 10kV overhead distribution lines (OHLs). A hybrid Partial Element Equivalent Circuit-Multi-conductor Transmission Line (PEEC-MTL) code was applied to evaluate lightning surges on SADT. This method has the capability of modeling both short and long wires in OHL systems and is verified numerically and experimentally. A Monte Carlo procedure was adopted to assess the fault rate (FR) of SADT. It is found that the direct lightning strokes to the phase wire are the primary threat to the SADT, compared with the strokes to the poles as well as indirect strokes to the ground or structure nearby. Several protection schemes are identified, and their performance is evaluated from the aspects of lightning protection and economic cost. The full shielding wire (SW) scheme performs the best, but with the most expensive protection cost. The lightning energy absorption by SADT can be suppressed significantly by installing extra SAs on the adjacent poles. The scheme of two or four extra SAs together with a partial SW can achieve a balanced performance of protection effect and economic cost for the OHL. The SW for a section of the OHL alone is not enough to the lightning protection for SADT. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the time domain statistical simulation of field-to-wire coupling model with uncertain wire position in traverse direction. The parameters of governing equations of field-to-wire coupling model about the wire position are expanded to the augmented governing equations by using the generalized polynomial chaos technique with normalized orthogonal basis functions. And the coefficients of the polynomial chaos expansions are solved by combining with the stochastic Galerkin method. However, the solution of augmented governing equations is a complex problem. Therefore, a new finite-difference time-domain (FDTD) algorithm based on the implicit Wendroff difference format is proposed to solve the governing equations, and the general iterative equations are given in this paper. The proposed methods are verified by a field-to-wire coupling model. The effectiveness and advantages of the new FDTD algorithm and the uncertainty analysis method based on generalized polynomial chaos technique are verified, which are achieved by comparing with the conventional leapfrog fashion FDTD algorithm and the Monte Carlo technique, respectively. [ABSTRACT FROM AUTHOR]

In this work, two heuristic techniques used to reduce the number of simulations of lightning-induced voltages, which are required to assess the lightning performance of distribution lines according to the Monte Carlo methodology, are presented. The first uses the Rusck Formula to discard the events that do not cause damage to the line. The second is based on a multi-nonlinear regression of the previously simulated events. Both techniques were applied to estimate the flashover rate of straight and complex lines above flat ground with finite conductivities. As a result, between 80 % to 90 % of the total number of simulations were eliminated with a reduced error. [ABSTRACT FROM AUTHOR]

Uncertainties in power system studies are typically managed by Monte Carlo methods or by applying some worst case assumptions. As the number ofuncertainties increases, there is a need for methods that can estimate statistical parameters from a limited number of calculations. This paper utilizes a method called the Unscented Transform together with Cornish-Fisher expansion to estimate the 2%-value of switching overvoltages using only about one tenth the number of calculations as a Monte Carlo method, with similar accuracy. The uncertainties considered are the energizing instants of the three phases. The method is illustrated through EMT simulations in PSCAD and it is shown to provide a good approximation of the 2%-value in most of the studied cases. [ABSTRACT FROM AUTHOR]

A fast and accurate deep learning (DL) based ASM-HEMT I-V model parameter extraction is presented for the first time. DL-based extraction starts with 120k training data-sets comprising of 374 million I-V data points. Training data-sets are generated through Monte Carlo simulations. The trained DL-model is demonstrated to successfully model 114 GaN HEMTs from a typical GaN fabrication process. The predicted parameters show an excellent fit for the I-V data. In addition, the root-mean-square(RMS) error incurred for key electrical parameters such as pinch-off voltage, linear condition current and the maximum current is 2.2%, 17.6%, and 2.4% respectively. The proposed approach is verified for multiple GaN HEMTs of different sizes. The developed technique can provide a very fast means for parameter extraction with a reasonable accuracy. [ABSTRACT FROM AUTHOR]

Rendering a translucent material involves integrating the product of the transmittance-weighted irradiance and the BSSRDF over the surface of it. In previous methods, this spatial integral was computed by creating a dense distribution of discrete points over the surface or by importance-sampling based on the BSSRDF. Both of these approaches necessitate specifying the number of samples, which affects both the quality and the computation time for rendering. An insufficient number of samples leads to noise and artifacts in the rendered image and an excessive number results in a prohibitively long rendering time. In this article, we propose an error estimation method for translucent materials in a many-light rendering framework. Our adaptive sampling can automatically determine the number of samples so that the estimated relative error of each pixel intensity is less than a user-specified threshold. We also propose an efficient method to generate the sampling points that make large contributions to the pixel intensity taking into account the BSSRDF. This enables us to use a simple uniform sampling, instead of costly importance sampling based on the BSSRDF. The experimental results show that our method can accurately estimate the error. In addition, in comparison with the previous methods, our sampling method achieves better estimation accuracy in equal-time. [ABSTRACT FROM AUTHOR]

Backscattering by objects is a relevant phenomenon in millimeter-wave (mm-wave) propagation. Accordingly, computationally efficient models characterizing this process are now available in the literature. However, these models are restricted to single-surface contributions. In this article, we propose a multiple-scattering model for calculating backscattered fields due to consecutive interactions with several smooth electrically-large rectangular surfaces. The model is fundamentally based on the cascading of single-scattering events. In addition, the model is later extended to capture single-scattering blockage between backscattering events. The model is implemented using a Fresnel-integral-based method and validated by means of electromagnetic (EM) simulations and measurements. Furthermore, insight on the effects of the model parameters—number of surfaces, frequency, surface size, surface height-to-width ratio, surface displacement, and antenna-to-surface and surface-to-surface distances—is provided. Due to its computational efficiency, the proposed model can be suitable for system- and link-level simulations of wireless systems, particularly when Monte Carlo simulations are applied. [ABSTRACT FROM AUTHOR]

Penetration of renewable energy resources (RERs) in the power grid continues to increase as we strive toward a greener environment for the future. While they have many advantages, most RERs possess little or no rotational kinetic energy, thereby threatening the frequency stability of future power grids. Energy storage systems (ESSs) can be used to mitigate this problem, as they are capable of providing virtual inertia to the system. This paper proposes a novel analytical approach for sizing ESSs to provide inertial support to the grid and maintain frequency stability in presence of RERs. This method analytically estimates the total inertia of the system using probability distributions of the outage states of conventional generators and RERs. RERs are modeled as multi-state units, considering the variability of their output power due to changes in weather conditions and also the forced outages of their equipment. The ESS is utilized to provide virtual inertia to the grid in the form of active power once the estimated system inertia falls below the minimum required during a disturbance. The efficacy of the proposed approach is tested on the IEEE 39 bus system and the results obtained by the analytical approach are validated using Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Gallium nitride (GaN) high electron mobility transistors (HEMTs) are currently being used for RF applications due to the intrinsically high saturation velocity and high mobility of GaN compared to both Silicon and SiC. However, GaN HEMTs suffer from a variety of issues, such as a lack of E-Mode operation and non-linearity, which impacts their widespread adoption in the RF and low-voltage switching devices. Recent advances in material processing, high aspect ratio epitaxial growth, and etching methods has led to an increased interest in 3-D GaN nanostructures such as AlGaN/GaN MIS FinFET wherein a layer of Al2O3 surrounds the AlGaN/GaN fin. Theoretical calculations of transport properties of AlGaN/GaN FinFETs are scarce compared to those of their planar HEMT counterparts. This work employs for the first time self-consistent solution of the coupled 1-D Boltzmann – 2-D Schrödinger – 3-D Poisson problem to yield the channel electrostatics and the transport characteristics of AlGaN/GaN MIS FinFETs. The low field electron mobility in the quasi-1-D region is calculated, using 1-D ensemble Monte Carlo method, as a function of temperature and fin width. Acoustic, piezoelectric and polar optical phonon scattering, and interface roughness scattering at the AlGaN/GaN interface, are incorporated in the theoretical model. Our simulations suggest that E-mode FinFETs can be achieved in ultranarrow fin channels. As suggested experimentally, we confirm via simulation experiments that strain relaxation increases the sheet resistance of the channel. [ABSTRACT FROM AUTHOR]

Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient explorations of architectures. To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics. During the search phase, as different action sequences lead to regions with different performance, the search efficiency can be significantly improved by biasing towards the good regions. On three NAS tasks, empirical results demonstrate that LaNAS is at least an order more sample efficient than baseline methods including evolutionary algorithms, Bayesian optimizations, and random search. When applied in practice, both one-shot and regular LaNAS consistently outperform existing results. Particularly, LaNAS achieves 99.0 percent accuracy on CIFAR-10 and 80.8 percent top1 accuracy at 600 MFLOPS on ImageNet in only 800 samples, significantly outperforming AmoebaNet with $33\times$ 33 × fewer samples. Our code is publicly available at https://github.com/facebookresearch/LaMCTS. [ABSTRACT FROM AUTHOR]

The magnetization stability of a YBCO superconductor was numerically studied using the Monte Carlo method. The magnetization-reversal loops of samples with various pinning configurations (periodic and random) were calculated as well as minor loops caused by field inhomogeneities. Hysteresis energy losses were estimated via areas bounded by the minor loops at different temperatures. Samples providing a balance between the critical current, pinning properties, and energy losses, were identified. Among the considered distributions, the homogeneous (periodic) defect configurations showed the highest efficiency. [ABSTRACT FROM AUTHOR]

As an efficient and interference-resistant coding technique, multiple description coding (MDC) is proposed with the aim to solve the transmission unreliability problem caused by packet errors, loss or blocking delays. The integration of MDC with non-orthogonal multiple access (NOMA) scheme (MDC-NOMA) can effectively boost the robustness and throughput of the underlying system. Additionally, space-time block coding (STBC) can further enhance the system reliability with increased diversity gain, as well as reduced decoding complexity. In this paper, we propose a novel framework referred to as MDC-NOMA-STBC, in which we apply Alamouti STBC integrated with MDC and NOMA to implement more reliable transmission. Specifically, NOMA signals are constructed by superimposing the descriptions from different users, then transmitted by the base station (BS) using Alamouti STBC. In order to substantiate the performance of the proposed framework, first, we derive closed-form expressions of both the outage probability and ergodic rate for each user in a two-antenna BS scenario. Second, for multi-antenna BS, we investigate different antenna selection strategies to further enhance the outage performance under the considered framework, whose analytical expression is provided wherever possible. Third, Monte Carlo simulation results are presented to validate our theoretical framework and to corroborate the superiority of the proposed MDC-NOMA-STBC over state-of-the-art MDC-NOMA scheme. Moreover, under realistic contexts with image transmission, it is confirmed that MDC-NOMA-STBC outperforms its counterpart MDC-NOMA in terms of the peak signal-to-noise ratio and the bit error rate. [ABSTRACT FROM AUTHOR]

Practical microwave design is most often carried out in the nominal sense. Yet, in some cases, performance degradation due to uncertainties may lead to the system failing to meet the prescribed specifications. Reliable uncertainty quantification (UQ) is, therefore, important yet intricate from numerical standpoint, especially when the circuit at hand is to be evaluated using electromagnetic (EM) simulation tools. Tolerance-aware design (e.g., yield improvement) is even more challenging. This article introduces a methodology for low-cost surrogate-based yield optimization of passive microwave components. The novelty of the proposed approach, and, at the same time, its major acceleration factor is to span the metamodel domain with the selected principal vectors, characterized by significant response variability within operating frequency bands of the component under design. This results in a volumewise constriction of the domain (thereby lower cost of the surrogate model setup) without restricting its size along the relevant directions of the parameter space. Consequently, our technique is a one-shot approach for yield optimization that does not require neither domain relocation nor surrogate reconstruction. Our methodology is demonstrated using two microstrip components and favorably compared to benchmark metamodeling techniques in terms of the computational cost of the yield maximization procedure. The average cost is only 130 EM simulations of the respective circuit, versus the average of 800 and over 360 analyses for the benchmark procedures. At the same time, its reliability is verified by means of EM-based Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

This correspondence considers the performance evaluation of precoded orthogonal frequency division multiplexing (P-OFDM) based on Walsh-Hadamard Transform (WHT) in the presence of symbol timing offset (STO). The signal-to-interference plus noise ratio (SINR) is evaluated for flat and frequency-selective fading channels (FSC), where closed-form analytical expressions are derived for the flat fading case. In FSC, the SINR is obtained using Monte Carlo simulation due to the analysis intractability. The bit error rate (BER) performance of OFDM and P-OFDM based on WHT, Haar, and Zadoff-Chu matrix transforms are considered to demonstrate the STO impact on the BER using various precoding transforms. The obtained results show that the impact of STO on P-OFDM is substantially different from conventional OFDM. In particular, it is found that STO affects the SINR of each subcarrier differently, where the SINR of certain subcarriers is severely degraded while others are immune. Furthermore, the diversity gain advantage of the precoding process may vanish for large STOs. [ABSTRACT FROM AUTHOR]