Equivalent Complex Baseband Model for Digital Transmitters Based on 1-bit Quadrature Pulse Encoding

In modern distributed energy systems (DES), focus is shifting from the conventional centralized approach towards distributed architectures. However, modeling and analysis of these systems is more complex, as it involves the interface of multiple energy sources with many different type of loads through power electronics converters. The integration of power electronics converters allows distributed renewable energy sources to become part of modern electronics power distribution systems (EPDS). It will also facilitate the ongoing research towards DC-based DES which is mostly composed of commercial DC-DC converters whose internal structure and parameters are unknown. For the system level analysis, the behavioral modeling technique is the only choice. Since most power electronics converters are non-linear systems and linear models can’t model their dynamics to a desired level of accuracy, hence non-linear modeling is required for accurate modeling. The non-linear modeling approach presented here aims to develop behavioral models that can predict the response of the system over the entire operating range. In this work, either a lookup table or a polytopic structure-based modeling technique is used. The technique is further applied to cascade and parallel connected converters, being two DES scenarios. First the procedure is verified via application to switching models in a simulation and then validated for commercial converters via experiments. The results show that the developed behavioral models accurately predict both the transient and steady state response.

This study aims to determine the effectiveness of behavioral counseling modeling techniques to improve the character able to independent characters through lesson. This research is a quasi research (Quasi Experiment). The purpose of this study was to determine the effectiveness of modeling techniques in behavioral counseling models for able to independent of UPW B grade X students. The process of collecting data in this study used observation sheets, interview sheets, diaries and character able to independent questionnaires. The results of this study experimental group treated with modeling techniques indicator 1 produces tcount = 3.57> ttable = 1.94, From this study effective modeling techniques to improve the character able to independent

This paper describes behavioral models for mixed-mode and multi-level simulation of phase-locked 100ps(PLLs). PLLs are a difficult class of systems to evaluate using conventional circuit simulators because of the mixed analog digital signals involved, and extensively long runtimes required to capture the performance. Behavioral modeling techniques and a mixed-signal simulator, the AT&T Bell Laboratories ADAMS simulator, are used to overcome these limitations. An all analog PLL is simulated at the behavioral level to measure the lock-in characteristics as well as the tracking range. A high-speed digital PLL is simulated at the behavioral level, as well as at a multi-level using device-level models for the phase detector, to measure the lock-in time and detect false locking.

Data compilation methods, whether generated by remotely deployed sensors collecting physically observable measurements or collaborative communications, such as blogs and online survey forms, are amassing extremely large repositories of information. Compounding the overwhelming volume of historical and streaming information, is the quick turn-around required to analyze and report the essence of this information. For example, health care facilities must evaluate data collected by emergency rooms within the context of the individual patient's profile, the trend (over time and social agency) within the larger patient community, the payer's historically-generated statistics, and the provider's subjective, but expert, knowledge of the situation; and pattern recognition must be accomplished in the few hours prior to a treatment decision. Similarly, national security policy makers and action planners have a diversity of information, all with degrees of uncertainty about accuracy (vice precision), to be explored in a short time (hours to days) to support decisions with long-term international consequences. While models and simulations of actor behavior have a credible history of performance in specialized applications, these efforts typically require time-consuming human examination. Moreover, rarely do researchers apply statistical learning and modeling techniques understanding the outputs of large-scale models to go back and improve the simulation/models from such analysis. This panel discussion will highlight some of the recent successes in knowledge discovery and behavioral modeling techniques, as well as the research challenges remaining.

Summary1. Hodgson et al. [Journal of Applied Ecology46 (2009) 964] argue that connectivity is complex and uncertain, that it can be improved incidentally by increasing habitat extent, and that connectivity conservation is unlikely to be effective under climate change.2. We believe that they have overlooked recent research on dispersal behaviour and structural connectivity, which has improved our understanding of functional connectivity and revealed that it will not necessarily increase with habitat extent.3. New modelling techniques including least‐cost path models incorporate this more detailed understanding of connectivity into conservation planning, facilitating the true aim of connectivity conservation – to ensure appropriate interactions between habitat extent, quality and connectivity.4. Synthesis and applications. Advances in behavioural research and modelling techniques allow us to manage structural connectivity with as much certainty as we manage extent and quality of habitat. Successful landscape conservation to address both current threats and future climate change must manage these three elements in concert.

The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

In this paper, we propose an auto-companding technique for the analog behavioral modeling via wavelet collocation method. The companding function is automatically constructed according to the singularities of the input-ouput function of the circuit block. Such a general-purpose technique can be applied for the automatic modeling of arbitrary building block of arbitrary input-output function. Moreover, compared with the published modeling approaches, this method works more efficiently in reducing both the modeling error and the number of basis functions in wavelet expansion.

A Doherty amplifier was investigated using behavioral amplifier modeling techniques. A measurement system for simultaneous wide bandwidth (>100 MHz) and large dynamic range (-70 dBc ACLR) sampled measurements that are needed for the behavioral modeling is presented. Both the measurement system and the performance of the Doherty amplifier are described. The findings are that the well known and widely used parallel Hammerstein model, also denoted the memory polynomial model, is well suitable also for modeling and predistortion of Doherty amplifiers, and that a Doherty amplifier can be designed with only minor memory effects. This is seen in the modeling error, which is reduced by 13 dB, compared with a constant gain, using a memoryless polynomial model and only 6 dB further by adding memory to the model.

Earlier in Strategic Highway Research Program 2 (SHRP 2) research, Reliability Project L11: Evaluating Alternative Operations Strategies to Improve Travel Time Reliability presented a novel approach to establishing an economic value for travel time reliability. The approach is based on the real options theory from the financial sector, in which an individual purchases an insurance premium to protect against an undesirable outcome. The travel analogy is when a traveler knows the normal travel time to a destination but chooses an earlier departure time to reduce the risk of a late arrival. This extra travel time has a monetary value and represents the insurance premium that a traveler is willing to pay. The method is data driven, using actual local historical travel times as the basis for establishing a value of reliability, whereas previous methods have used behavioral modeling techniques. The Maryland State Highway Administration currently has a project development and programming process to address short-term congestion relief that includes the value of travel time reliability based on consolidated past research and methodologies. This project’s purpose was to further develop and pilot the viability of the options-theoretic approach in Maryland, establish a localized range of reliability values based on state travel time data, compare the new values to those already in use, apply the newly established values in the project development and analysis process, and conduct an assessment of the findings and results. A travel time data-driven methodology was constructed to produce a range of local values for reliability, and the sensitivity of this range of values was examined using an actual short-term congestion relief project to better understand application effects. While the data-driven method shows promise, additional validation is required on the underlying theories, method, and test application results. Suggestions for further research are presented. In addition, the applicability of incorporating the value of reliability into long-range project development, prioritization, and selection was explored as a proof of concept. Although the Maryland State Highway Administration is the basis for this pilot study, the underlying theory, principles, and data-driven method, as well as the further research suggestions, could be informative to other agencies and jurisdictions that have an interest in considering incorporating travel time reliability into their project development and programming decision-making process.

Memristive behavior has been clearly addressed through growth and shrinkage of thin filaments in metal-oxide junctions. Capacitance change has also been observed, raising the possibility of using them as memcapacitors. Therefore, this paper proves that metal-oxide junctions can behave as a memcapacitor element by analyzing its characteristics and modeling its memristive and memcapacitive behaviors. We develop two behavioral modeling techniques: charge-dependent memcapacitor model and voltage-dependent memcapacitor model. A new physical model for metal-oxide junctions is presented based on conducting filaments variations, and its effect on device capacitance and resistance. In this model, we apply the exponential nature of growth and shrinkage of thin filaments and use Simmons' tunneling equation to calculate the tunneling current. Simulation results show how the variations of practical device parameters can change the device behavior. They clarify the basic conditions for building a memcapacitor device with negligible change in resistance.

An accurate, efficient, and improved Light Gradient Boosting Machine (LightGBM) based Small‐Signal Behavioral Modeling (SSBM) techniques are investigated and presented in this paper for Gallium Nitride High Electron Mobility Transistors (GaN HEMTs). GaN HEMTs grown on SiC, Si and diamond substrates of geometries 2 × 50 , 10 × 200 , and 4 × 125 , respectively are used in this study. A versatile set of LightGBM's hyperparameters including learning and tree specific parameters are meticulously optimized using a modern and vigorous optimization algorithm namely Osprey Optimization Algorithm (OOA) with the objective to accomplish superior model performance. The developed OOA‐LightGBM based models are validated for a wide array of operating conditions including for frequency values within a broad spectrum of 0.25 to 120 GHz, 0.1 to 26 GHz, and 0.1 to 40 GHz for GaN‐on‐SiC, GaN‐on‐Si, and GaN‐on‐Diamond HEMTs, respectively. The proposed SSBM techniques have demonstrated remarkable prediction ability and are impressively efficient for all the GaN HEMTs devices tested in this work.

Hysteresis nonlinearity modeling and linearization approach for Envelope Tracking Power Amplifiers in wireless systems

A statistically based modeling technique is developed for characterizing in-band and out-of-band intermodulation and cross-modulation distortions in multichannel amplifier environments. The model is based on a new multiple envelope memoryless behavioral model that captures the black-box characteristics of multichannel amplifiers. A power amplifier with a two code-division multiple-access channel signal is characterized experimentally and verifies the approach.

The introduction of next generation mobile systems and alternative applications demands significant improvement in efficiency and linearity of the power amplifier. It is essential to understand the limitations inherent to the power transistor, and improve it for emerging applications. The package containing the power transistor is built up of various components; MOS capacitors, bond wire arrays and ESD protection circuitry common in commercial power transistors in addition to the semiconductor transistor die. The objectives of this work are to develop multiphysics modelling components which are needed for CAD-based design optimisation. An electrothermal bond wire array model is proposed which combines the electrical and thermal characteristics at high-frequency, reproducing the non-uniform distribution seen in measurements. The model returns the temperature distribution over 2000-times faster, in a fraction of a second, compared to an equivalent 3D multiphysics simulation which takes at least 23 minutes, making it well suited to circuit modelling for CAD-design and optimisation. Fundamentally, the foundations of the power transistor is the semiconductor technology that the package holds. Extraction of compact transistor models is time consuming, requiring a multitude of measurement steps and specialised equipment. Behavioural modelling techniques simplify the measurement extraction procedure, while conserving the accuracy and convergence needed for circuit modelling. A primary limiting factor of the X-parameter behavioural model is that it lacks thermal memory effects that are prevalent in most applicable transistor technologies. This work extends the extraction of X-parameters to include dynamic thermal memory effects. Thermal hysteresis is demonstrated and shown to introduce spectral asymmetry during simulations using two-tone and QAM-16 modulation stimulus. Furthermore, distributed multiphysics modelling techniques co-simulate electromagnetics, thermodynamics and semiconductor device physics - the coupled solution providing enhanced insight into the performance and limitations of the transistor compared to lumped modelling approaches. A distributed electrothermal model was developed which enables significant design improvements by optimisation of the thermal and electrical layouts. This is demonstrated at 28~GHz for a GaN pHEMT transistor where a 12% increase in linear output power and 54% increase in PAE is achieved relative to the peak efficiency of the transistor (24%). The multiphysics modelling components developed in this work aim to improve power transistors for next generation mobile systems and applications requiring the efficient application of high-power microwaves.

This paper describes procedures to build custom‐tailored behavioural models of cellular neural networks (CNNs), and acompanion tool to run these models. The main property of the CNNs is the emerging behaviour, i.e. new phenomena arise from the interactions of thousands of identical cells. The existence of these phenomena need is to be checked during the design phase, which requires a full network simulation and therefore constitutes a very time‐consuming step of circuit verification. To solve this task as a modelling problem, we introduce a new behavioural model optimization technique. Starting from a user‐defined set of block models, the proposed framework produces an optimized selection which is used to build up a full‐chip model. The optimization goal is the minimization of the simulation CPU time and the maximization of the time domain precision. A dedicated environment has been developed for efficient numerical simulation; this environment is briefly described in the paper. Two case studies are also presented to demonstrate the effectivity of the technique. Copyright © 2002 John Wiley & Sons, Ltd.