Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation

Variable gravity research facility

Spin and despin requirements; sequence of activities required to assemble the Variable Gravity Research Facility (VGRF); power systems technology; life support; thermal control systems; emergencies; communication systems; space station applications; experimental activities; computer modeling and simulation of tether vibration; cost analysis; configuration of the crew compartments; and tether lengths and rotation speeds are discussed

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Applying Mean-field Approximation to Continuous Time Markov Chains

The mean-field analysis technique is used to perform analysis of a systems with a large number of components to determine the emergent deterministic behaviour and how this behaviour modifies when its parameters are perturbed. The computer science performance modelling and analysis community has found the mean-field method useful for modelling large-scale computer and communication networks. Applying mean-field analysis from the computer science perspective requires the following major steps: (1) describing how the agents populations evolve by means of a system of differential equations, (2) finding the emergent deterministic behaviour of the system by solving such differential equations, and (3) analysing properties of this behaviour either by relying on simulation or by using logics. Depending on the system under analysis, performing these steps may become challenging. Often, modifications of the general idea are needed. In this tutorial we consider illustrating examples to discuss how the mean-field method is used in different application areas. Starting from the application of the classical technique, moving to cases where additional steps have to be used, such as systems with local communication. Finally we illustrate the application of the simulation and uid model checking analysis techniques

Some issues related to simulation of the tracking and communications computer network

The Communications Performance and Integration branch of the Tracking and Communications Division has an ongoing involvement in the simulation of its flight hardware for Space Station Freedom. Specifically, the communication process between central processor(s) and orbital replaceable units (ORU's) is simulated with varying degrees of fidelity. The results of investigations into three aspects of this simulation effort are given. The most general area involves the use of computer assisted software engineering (CASE) tools for this particular simulation. The second area of interest is simulation methods for systems of mixed hardware and software. The final area investigated is the application of simulation methods to one of the proposed computer network protocols for space station, specifically IEEE 802.4

DSIM: A distributed simulator

Discrete event-driven simulation makes it possible to model a computer system in detail. However, such simulation models can require a significant time to execute. This is especially true when modeling large parallel or distributed systems containing many processors and a complex communication network. One solution is to distribute the simulation over several processors. If enough parallelism is achieved, large simulation models can be efficiently executed. This study proposes a distributed simulator called DSIM which can run on various architectures. A simulated test environment is used to verify and characterize the performance of DSIM. The results of the experiments indicate that speedup is application-dependent and, in DSIM's case, is also dependent on how the simulation model is distributed among the processors. Furthermore, the experiments reveal that the communication overhead of ethernet-based distributed systems makes it difficult to achieve reasonable speedup unless the simulation model is computation bound

Combining high performance simulation, data acquisition, and graphics display computers

Issues involved in the continuing development of an advanced simulation complex are discussed. This approach provides the capability to perform the majority of tests on advanced systems, non-destructively. The controlled test environments can be replicated to examine the response of the systems under test to alternative treatments of the system control design, or test the function and qualification of specific hardware. Field tests verify that the elements simulated in the laboratories are sufficient. The digital computer is hosted by a Digital Equipment Corp. MicroVAX computer with an Aptec Computer Systems Model 24 I/O computer performing the communication function. An Applied Dynamics International AD100 performs the high speed simulation computing and an Evans and Sutherland PS350 performs on-line graphics display. A Scientific Computer Systems SCS40 acts as a high performance FORTRAN program processor to support the complex, by generating numerous large files from programs coded in FORTRAN that are required for the real time processing. Four programming languages are involved in the process, FORTRAN, ADSIM, ADRIO, and STAPLE. FORTRAN is employed on the MicroVAX host to initialize and terminate the simulation runs on the system. The generation of the data files on the SCS40 also is performed with FORTRAN programs. ADSIM and ADIRO are used to program the processing elements of the AD100 and its IOCP processor. STAPLE is used to program the Aptec DIP and DIA processors