Use of OTA System Performance Metrics in the Design & Optimization of CATRs for 5G Testing

2019 ◽  
Author(s):  
S.F. Gregson ◽  
C.G. Parini
Keyword(s):  
Design Optimization ◽  
System Performance ◽  
Performance Metrics

Effect of CSI Imperfection on Cognitive Underlay Transmission over Nakagami-m Fading Channel

2015 ◽  
Vol 1 ◽  
pp. 59
Author(s):  
Nguyen Hong Giang ◽  
Vo Nguyen Quoc Bao ◽  
Hung Nguyen-Le
Keyword(s):  
Fading Channels ◽  
System Performance ◽  
Performance Metrics ◽  
Maximal Ratio Combining ◽  
Relay Nodes ◽  
Channel State ◽  
Primary Network ◽  
Secondary Network ◽  
System Designs ◽  
Approximate Expressions

This paper analyzes the performance of a cognitive underlay system over Nakagami-m fading channels, where maximal ratio combining (MRC) is employed at secondary destination and relay nodes. Under the condition of imperfect channel state information (CSI) of interfering channels, system performance metrics for the primary network and for the secondary network are formulated into exact and approximate expressions, which can be served as theoretical guidelines for system designs. To verify the performance analysis, several analytical and simulated results of the system performance are provided under various system and channel settings.

On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

Database Benchmarks

2011 ◽  
pp. 950-954
Author(s):  
Jérôme Darmont
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
System Performance ◽  
Performance Metrics ◽  
Actual Behavior ◽  
Future Trends ◽  
Measurement Tools ◽  
Object Relational ◽  
Model Set ◽  
System Performance Evaluation

Performance measurement tools are very important, both for designers and users of Database Management Systems (DBMSs). Performance evaluation is useful to designers to determine elements of architecture, and, more generally, to validate or refute hypotheses regarding the actual behavior of a DBMS. Thus, performance evaluation is an essential component in the development process of well-designed and efficient systems. Users may also employ performance evaluation, either to compare the efficiency of different technologies before selecting a DBMS, or to tune a system. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. It consists of performing a series of tests on a given DBMS to estimate its performance in a given setting. Typically, a benchmark is constituted of two main elements: a database model (conceptual schema and extension), and a workload model (set of read and write operations) to apply on this database, following a predefined protocol. Most benchmarks also include a set of simple or composite performance metrics such as response time, throughput, number of input/output, disk or memory usage, and so forth. The aim of this article is to present an overview of the major families of state-of-the-art database benchmarks, namely, relational benchmarks, object and object-relational benchmarks, XML benchmarks, and decision-support benchmarks; and to discuss the issues, tradeoffs, and future trends in database benchmarking. We particularly focus on XML and decision-support benchmarks, which are currently the most innovative tools that are developed in this area.

scholarly journals Performance Sensitivity to the High-Order Statistics of Time Interval Variables in Cellular Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Felipe A. Cruz-Pérez ◽  
Genaro Hernandez-Valdez ◽  
Andrés Rico-Páez ◽  
Sandra L. Castellanos-López ◽  
José R. Miranda-Tello ◽  
...  
Keyword(s):  
Cellular Networks ◽  
System Performance ◽  
Performance Metrics ◽  
Numerical Results ◽  
Time Interval ◽  
Forced Termination ◽  
Mobile Cellular ◽  
Interval Variables ◽  
Mobile Cellular Networks ◽  
Forced Termination Probability

Cell dwell time (DT) and unencumbered interruption time (IT) are fundamental time interval variables in the teletraffic analysis for the performance evaluation of mobile cellular networks. Although a diverse set of general distributions has been proposed to model these time interval variables, the effect of their moments higher than the expected value on system performance has not been reported in the literature. In this paper, sensitivity of teletraffic performance metrics of mobile cellular networks to the first three standardized moments of both DT and IT is investigated in a comprehensive manner. Mathematical analysis is developed considering that both DT and IT are phase-type distributed random variables. This work includes substantial numerical results for quantifying the dependence of system level performance metrics to the values of the first three standardized moments of both DT and IT. For instance, for a high mobility scenario where DT is modeled by a hyper-Erlang distribution, we found that call forced termination probability decreases around 60% as the coefficient of variation (CoV) and skewness of DT simultaneously change from 1 to 20 and from 60 to 2, respectively. Also, numerical results confirm that as link unreliability increases the forced termination probability increases while both new call blocking and handoff failure probabilities decrease. Numerical results also indicate that for low values of skewness, performance metrics are highly sensitive to changes in the CoV of either the IT or DT. In general, it is observed that system performance is more sensitive to the statistics of the IT than to those of the DT. Such understanding of teletraffic engineering issues is vital for planning, designing, dimensioning, and optimizing mobile cellular networks.

scholarly journals Intelligent Approach to Robust Design Optimization of a Rotor System due to Its Support Stiffness Uncertainty

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bensheng Xu ◽  
Chaoping Zang ◽  
Genbei Zhang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
System Performance ◽  
Polynomial Chaos ◽  
Low Pressure ◽  
Polynomial Chaos Expansion ◽  
Rotor System ◽  
Robust Design Optimization ◽  
Chaos Expansion ◽  
Vibration Responses

In this paper, an intelligent robust design approach combined with different techniques such as polynomial chaos expansion (PCE), radial basis function (RBF) neural network, and evolutionary algorithms is presented with a focus on the optimization of the dynamic response of a rotor system considering support stiffness uncertainty. In the proposed method, the PCE method instead of the traditional Monte Carlo uncertainty analysis is applied to analyze the uncertain propagation of system performance. The RBF network is introduced to establish the approximate models of the objective and constraint functions. Taking the low-pressure rotor of a gas turbine with support stiffness uncertainty as an example, the optimization model is established with the mean and variance of unbalanced response of the rotor system at different operating speeds as the objective function, and the maximum unbalance response is less than the upper limit as the constraint function. The polynomial chaos expansion is generated to facilitate a rapid analysis of robustness in the presence of support stiffness uncertainties that is defined in terms of tolerance with good accuracy. The optimal Hypercubus are used as experimental plans for building RBF approximation models of the objective and constraint functions. Finally, the robust solutions are obtained with the multiobject optimization algorithm NSGA-II. Monte Caro simulation analysis demonstrates that the qualified rate of maximum vibration responses of the low-pressure rotor system can be increased from 83.6% to over 99%. This approach to robust design optimization is shown to lead to designs that significantly decrease vibration responses of the rotor system and improved system performance with reduced sensitivity to support stiffness uncertainty.

Expected-Improvement-Based Methods for Adaptive Sampling in Multi-Objective Optimization Problems

2016 ◽  
Author(s):  
Jesper Kristensen ◽  
You Ling ◽  
Isaac Asher ◽  
Liping Wang
Keyword(s):  
Convex Hull ◽  
Design Optimization ◽  
Adaptive Sampling ◽  
Pareto Front ◽  
Performance Metrics ◽  
Sampling Methods ◽  
Optimization Problems ◽  
Expected Improvement ◽  
Multi Objective Optimization ◽  
Multi Objective

Adaptive sampling methods have been used to build accurate meta-models across large design spaces from which engineers can explore data trends, investigate optimal designs, study the sensitivity of objectives on the modeling design features, etc. For global design optimization applications, adaptive sampling methods need to be extended to sample more efficiently near the optimal domains of the design space (i.e., the Pareto front/frontier in multi-objective optimization). Expected Improvement (EI) methods have been shown to be efficient to solve design optimization problems using meta-models by incorporating prediction uncertainty. In this paper, a set of state-of-the-art methods (hypervolume EI method and centroid EI method) are presented and implemented for selecting sampling points for multi-objective optimizations. The classical hypervolume EI method uses hyperrectangles to represent the Pareto front, which shows undesirable behavior at the tails of the Pareto front. This issue is addressed utilizing the concepts from physical programming to shape the Pareto front. The modified hypervolume EI method can be extended to increase local Pareto front accuracy in any area identified by an engineer, and this method can be applied to Pareto frontiers of any shape. A novel hypervolume EI method is also developed that does not rely on the assumption of hyperrectangles, but instead assumes the Pareto frontier can be represented by a convex hull. The method exploits fast methods for convex hull construction and numerical integration, and results in a Pareto front shape that is desired in many practical applications. Various performance metrics are defined in order to quantitatively compare and discuss all methods applied to a particular 2D optimization problem from the literature. The modified hypervolume EI methods lead to dramatic resource savings while improving the predictive capabilities near the optimal objective values.

Detection system performance metrics with scene content dependencies

2005 ◽  
Author(s):  
Timothy D. Ross ◽  
William E. Pierson ◽  
Edmund G. Zelnio ◽  
Kevin L. Priddy
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Detection System ◽  
Scene Content

scholarly journals Performance Evaluation of UAV-Based NOMA Networks with Hardware Impairment

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 94
Author(s):  
Chung Ho Duc ◽  
Sang Quang Nguyen ◽  
Chi-Bao Le ◽  
Ngo Tan Vu Khanh
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Ergodic Capacity ◽  
High Signal ◽  
Performance Loss ◽  
Hardware Impairments ◽  
Asymptotic Expressions ◽  
Aerial Vehicle ◽  
Hardware Impairment

In this paper, we evaluate the outage performance of a non-orthogonal multiple access (NOMA)-enabled unmanned aerial vehicle (UAV) where two users on the ground are simultaneously served by a UAV for a spectral efficiency purpose. In practice, hardware impairments at the transceiver cause distortion noise, which results in the performance loss of wireless systems. As a consequence, hardware impairment is an unavoidable factor in the system design process. Hence, we take into account the effects of hardware impairment (HI) on the performance of the proposed system. In this setting, to evaluate the system performance, the closed-form expressions of the outage probability of two NOMA users and the ergodic capacity are derived as well as their asymptotic expressions for a high signal-to-noise ratio (SNR). Finally, based on Monte-Carlo simulations, we verify the analytical expressions and investigate the effects on the main system parameters, i.e., the transmit SNR and level of HI, on the system performance metrics. The results show that the performance for the near NOMA user is better than of that for the far NOMA user in the case of perfect hardware; however, in the case of hardware impairment, an inversion happens at a high transmit power of the UAV in terms of the ergodic capacity.

Unified Applicable Propulsion System Performance Metrics

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Oliver Schmitz ◽  
Mirko Hornung
Keyword(s):  
System Performance ◽  
Exergy Analysis ◽  
Performance Metrics ◽  
Aircraft Design ◽  
Electrical Energy ◽  
Propulsion System ◽  
Combustion Engines ◽  
Propulsion Systems ◽  
Figures Of Merit ◽  
Parallel Hybrid

Progress in the development of electrical storage and conversion technology progressively attains focus in aerospace motive power research. Novel propulsion system concepts based on hybrid or even entirely electrical energy sources are seriously considered for aircraft design. To this point, unified figures of merit are required in order to allow for consistent comparative investigations of existing combustion engines and future electrically-based propulsion systems. Firstly, this paper identifies the shortcomings of conventional performance metrics used for nonthermal electrical conversion processes and then approaches exergy-based loss methods as means of metrics extensions. Subsequently, energy source-independent figures of merit based on exergy analysis are derived and embedded into the well-known performance definitions. Finally, the unified metrics are demonstrated through application to a conventional turbofan, a parallel-hybrid turbofan, a novel integrated-hybrid turbofan concept, and an entirely electrical fan concept.

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