Computer‐aided engineering challenges for wireless transceivers

This work explores potential errors arising in the application of modern computer‐aided engineering (CAE) software to the simulation of RF‐communication system hardware. A hardware set comprising a 915‐MHz transmitter and a corresponding 915‐MHz receiver is analyzed by comparing system‐level simulations from commercially available software to various linear and nonlinear performance measurements. It is shown that after the determination of the subsystem computer‐aided design parameters using the measured component behavior, good predictions of in‐band performance can be achieved for all analog parameters examined. Discrepancies, in some cases significant ones, are revealed in the out of band response predictions of the output frequency spectral content caused by system nonlinearities. Ideal filter and mixer models, in particular, are shown to be inadequate for high accuracy system simulation work. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 86–100, 2003.     

Construction of behavioral models for microwave devices from time domain large‐signal measurements to speed up high‐level design simulations

Behavioral models for microwave devices from time domain large‐signal measurements are developed. For the presented examples, the model is defined by representing the terminal currents as a function of the terminal voltages and their derivatives. When using these models as building blocks of higher level designs, the simulation speed is significantly improved. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 54–61, 2003.     

Straightforward modeling of dynamic I–V characteristics for microwave FETs

This work presents a straightforward approach aimed at modeling the dynamic I–V characteristics of microwave active solid‐state devices. The drain‐source current generator represents the most significant source of nonlinearity in a transistor and, therefore, its correct modeling is fundamental to predict accurately the current and voltage waveforms under large‐signal operation. The proposed approach relies on using a small set of low‐frequency time‐domain waveform measurements combined with numerical optimization‐based estimation of the nonlinear model parameters. The procedure is applied to a gallium nitride HEMT and silicon FinFET. The effectiveness of the modeling procedure in terms of prediction accuracy and generalization capability is demonstrated by validation of the extracted models under operating conditions different than the ones used for the parameters estimation. Good agreement between measurements and model simulations is achieved for both technologies and in both low‐ and high‐frequency range. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:109–116, 2014.     

Modeling power and intermodulation behavior of microwave transistors with unified small‐signal/large‐signal neural network models

This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large‐signal model of a power MESFET device, modeling the nonlinear relationship of drain‐source current I_ds as well as gate and drain charge Q_g and Q_d with respect to intrinsic voltages V_gs and V_ds over the whole operational bias region. The neural models have been implemented into a user‐defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load‐pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first‐order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276–284, 2003.     

Time‐domain measurement and modeling techniques for wideband communication components and systems

The measurement and characterization of RF and microwave components and systems has been dominated by a tone‐based frequency‐domain paradigm because of its simplicity and accuracy. This review article describes new and accurate time‐domain techniques that are superior for application to wideband and nonlinear contexts. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 5–31, 2003.     

Multi‐targets localization and elliptical circumnavigation by multi‐agents using bearing‐only measurements in two‐dimensional space

In order to enable the multi‐agents to elliptically circumnavigate the multi‐targets in more complex environment, we propose a geometric center estimator and an elliptical circumnavigation controller in two‐dimensional space by only employing bearing measurements without knowing the target's position and velocity. The stability of the algorithm is proved for both stationary targets and dynamic targets. Finally, a series of numerical simulations is presented to verify the correctness of the algorithm both in ideal networks and in networks with communication delays.     

Measurements of team workload: A time pressure and scenario complexity study for maritime operation tasks

Workload is a critical factor influencing team performance in complex systems. There remains no consensus on the selection of team workload measures. Through an experiment based on simulated maritime operation tasks, team workload was manipulated by time pressure and scenario complexity to investigate the responses of three categories (subjective, performance-based, and physiological) of potential team workload measurements. The results show that time pressure had significant effects on all the three categories of measurements, especially on subjective indicators, miss rate, completion rate, operation accuracy, and eye movements. Significant main effects of scenario complexity on subjective team workload and the selected team performance measurements were also found, such as response latent time, miss rate, completion rate, and operation accuracy. Physiological measurements may not be sensitive for measuring team workload when scenario complexity serves as a major influencing factor of team workload.     

Neuro‐space mapping modeling approach for trapping and self‐heating effects on GaAs and GaN devices

In recent years, neural networks have been successfully applied for modeling the nonlinear microwave devices as GaAs and GaN MESFETs/HEMTs. Many modeling approaches have been developed for small and large signal applications. In this contribution, a neuro‐space mapping approach is proposed for modeling the trapping and the self‐heating effects on GaAs and GaN devices. The Angelov empirical model is used as the coarse model, which can be adjusted using DC and Pulsed I/V measurements at different static bias points. The proposed approach is tested for the MGF1923 GaAs MESFET and for an AlGaN/GaN HEMT. DC and transient simulation results are compared to DC and Pulsed I/V measurements. Good results are obtained for the DC and dynamics I/V characteristics at different static bias points.     

A variance‐constrained approach to event‐triggered distributed extended Kalman filtering with multiple fading measurements

In this paper, a distributed extended Kalman filtering problem is studied for discrete‐time nonlinear systems with multiple fading measurements. To alleviate the network communication burden, the event‐triggered communication scheme is employed in both sensor‐to‐estimator channel and estimator‐to‐estimator channel. As such, the data transmission is executed only when the predefined event occurs. In addition, a set of independent random variables with known statistical properties is defined to represent the phenomenon of multiple fading measurements. The variance‐constrained approach is adopted to derive an upper bound for the estimation error covariance in consideration of the event‐triggered mechanism and truncated error by linearization. The filter gain for each node is then designed to minimize such an upper bound by recursively solving two Raccati‐like difference equations. By virtue of the stochastic stability theory, a sufficient condition is provided to guarantee the boundedness of the estimation error. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed filtering algorithm.     

A de‐embedding method with matrix rectification and influences of residual errors on model parameters extraction of InP HEMTs

As the cutoff frequency of InP HEMTs enters the terahertz band, high frequency measurement and modeling techniques in hundreds of gigahertz become urgent needs for further millimeter monolithic integrated circuits design. We proposed a new de‐embedding method linking device measurements and modeling based on full EM simulation data acquired from HFSS and advanced design system (ADS). The simulation results for passive dummy structures are well consistent with experiments, and the de‐embedding method is proved very effective for a resistive passive device with high distributed embedding surroundings in frequency range below 40 GHz. Based on these experimental facts, the EM simulations were extended up to 300 GHz and corresponding de‐embedding deviation was further investigated. Results show that the proposed de‐embedding method has very high accuracy in the whole frequency region with a maximum S‐parameters deviation of only 2.58%. However, further analysis proves that the small residual errors still significantly affect extracted small signal model parameters of InP HEMTs especially for transit time τ. Thus, further improvements on de‐embedding accuracy or careful considerations of more error functions in modeling process are necessary for obtaining physically meaningful model parameters.     

基于OMNeT＋＋的岸海短波通信网建模研究及分析

针对短波通信组网中的岸海短波通信网的特殊问题，通过网络仿真软件OMNeT＋＋对岸海通信网进行仿真建模研究与分析。通过OMNeT＋＋实现岸海短波通信网的建模，并研究分析通信网中的数据包接受成功和信道利用率等网络性能指标。基于OMNeT＋＋的岸海短波通信网建模有效地解决了岸海短波通信网建模仿真研究问题，通过进一步研究分析改善网络中模块建模的准确性问题，可以达到更高精确度的仿真。     

Recursive state estimation for discrete‐time nonlinear systems with event‐triggered data transmission,norm‐bounded uncertainties and multiple missing measurements

In this paper, we consider the recursive state estimation problem for a class of discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements. The phenomenon of event‐triggered communication mechanism occurs only when the specified event‐triggering condition is violated, which leads to a reduction in the number of excessive signal transmissions in a network. A sequence of independent Bernoulli random variables is employed to model the multiple measurements missing in the transmission. The norm‐bounded uncertainties that could be considered as external disturbances which lie in a bounded set. The purpose of the addressed filtering problem is to obtain an optimal robust recursive filter in the minimum‐variance sense such that with the simultaneous presence of event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements; the filtering error is minimized at each sampling time. By solving two Riccati‐like difference equations, the filter gain is calculated recursively. Based on the stochastic analysis theory, it is proved that the estimation error is bounded under certain conditions. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

Application of deterministic and stochastic Petri-Nets for performance modeling of NoC architectures

The design of appropriate communication architectures for complex Systems-on-Chip (SoC) is a challenging task. One promising alternative to solve these problems are Networks-on-Chip (NoCs). Recently, the application of deterministic and stochastic Petri-Nets (DSPNs) to model on-chip communication has been proven to be an attractive method to evaluate and explore different communication aspects. In this contribution the modeling of basic NoC communication scenarios featuring different processor cores, network topologies and communication schemes is presented. In order to provide a testbed for the verification of modeling results a state-of-the-art FPGA-platform has been utilized. This platform allows to instantiate a soft-core processor network which can be adapted in terms of communication network topologies and communication schemes. It will be shown that DSPN modeling yields good communication performance prediction results at low modeling effort. Different DSPN modeling aspects in terms of accuracy and computational effort are discussed.     

基于串行通信不完全数据采集的融合估计算法

针对监控系统采用串行电路作为数据传输方式具有一定延迟的弊端,提出一种基于异步串行数据融合的新算法。首先将随机接收自多传感器系统多采集目标的数据统一映射到融合中心的坐标系中,在融合周期内,状态更新时采用距前一融合时刻最近的观测值作为反馈,状态和观测预计时采用扩展卡尔曼滤波,从而获得基于全局的融合估计值和误差。仿真结果表明该算法估计误差更低,且随着串行系统数目的增加估计误差的增加幅度有明显降低。     

Finite-time consensus of multiple second-order dynamic agents without velocity measurements

This article considers the finite-time consensus of multiple second-order dynamic agents without velocity measurements. A feasible protocol under which each agent can only obtain the measurements of its position relative to its neighbours is proposed. By applying the graph theory, Lyapunov theory and the homogeneous domination method, some sufficient conditions for finite-time consensus of second-order multi-agent systems are established under the different kinds of communication topologies. Some examples are presented to illustrate the effectiveness of the theoretical results.     

Robust distributed H∞ filtering over an uncertain sensor network with multiple fading measurements and varying sensor delays

In this paper, the problem of robust distributed H_∞ filtering is investigated for state‐delayed discrete‐time linear systems over a sensor network with multiple fading measurements, random time‐varying communication delays, and norm‐bounded uncertainties in all matrices of the system. The diagonal matrices, whose elements are individual independent random variables, are utilized to describe the multiple fading measurements. Furthermore, the Bernoulli‐distributed white sequences are introduced to model the random occurrence of time‐varying communication delays. In the proposed filtering approach, the stability of the estimation error system is first shown by the Lyapunov stability theory and the H_∞ performance is then achieved using a linear matrix inequality method. Finally, two numerical examples are given to show the effectiveness and performance of the proposed approach.     

Does network quality matter? A field study of mobile user satisfaction

Mobile quality of experience and user satisfaction are growing research topics. However, the relationship between a user’s satisfaction with network quality and the networks real performance in the field remains unexplored.This paper is the first to study both network and non-network predictors of user satisfaction in the wild. We report findings from a large sample (2224 users over 12 months) combining both questionnaires and network measurements. We found that minimum download goodput and device type predict satisfaction with network availability. Whereas for network speed, only download factors predicted satisfaction. We observe that users integrate over many measurements and exhibit a known peak-end effect in their ratings. These results can inform modeling efforts in quality of experience and user satisfaction.     

Fractionally Delayed Kalman Filter

The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.