Simulations of continuous-time systems are frequently used by designers of signal processing and communication systems. Windowed finite-impulse response models are often used in these simulations to model continuous-time linear filters. We investigate the performance of some common windows with respect to waveform fidelity, which is a primary goal in waveform simulation, and we also obtain the form of optimum windows for this criterion. Our results indicate that the rectangular window is generally a practical and reasonably good choice for waveform simulation.

Phase noise can significantly degrade the performance of a block-coded digital communication system. This degradation can be mitigated by interleaving. The bit error rate (BER) of a block-coded system is estimated, taking into account the correlatedness of phase noise, as a function of interleaver depth. In principle, simulation can rigorously solve this problem, but the computer run time is prohibitive. A simplified system model that leads to an analytical formulation amenable to computer solution is presented. Numerical difficulties arise when a standard approach is used for long block codes with relatively high error-correcting capability and moderately small channel BER. An alternative formulation that is numerically much more stable is developed.>

Importance sampling can significantly reduce simulation run time in estimating the bit error probability of digital systems if a suitable procedure is used. Various aspects of optimum and suboptimum biasing procedures are considered. It is shown that the strict optimum does not lead to a realistic procedure. An examination is made of the suboptimal variations that can be implemented reasonably; in one variation, it is found that for a linear system, the biasing scheme should have a time-varying mean proportional to the impulse response. Aspects of implementation are also discussed.>

Importance sampling is a technique that can significantly reduce computer run-time in the estimation of bit error rate (BER). However, in the conventional implementation (CIS), the improvement reduces markedly for systems with long memory. An approach to recover the full improvement for such systems has been previously suggested, and is called 'efficient' important sampling (EIS). A report is presented on an extensive series of simulation-based experiments with CIS and EIS, both to compare theoretical predictions to experimental observations, as well as to gain insight into the conditions of applicability, especially for EIS.>

The assessment of bit error rate (BER) performance of a digital communication system via computer simulation has traditionally been done using the Monte Carlo method. For very low BER, this method requires excessive computer time. This time can be substantially reduced by using extrapolation based on importance sampling (IS). In applying IS to a complex system, many considerations must be addressed, chief among which is the reliability (variance) of the estimator as a function of the system particulars. We discuss a number of these considerations and, specifically, derive a number of expressions for the variance. We find that the variance improvement may be severely limited by the dimensionality (or memory) of the system. We describe a means for circumventing this limitation through the definition of a statistically equivalent impulse response. For a linear system, this amounts to the ordinary impulse response. The simulation can be structured to estimate the equivalent impulse response using statistical regression. This new approach has been implemented and found to yield significant runtime improvement over conventional importance sampling for linear systems of large dimensionality. We believe this technique will work also for mildly nonlinear systems, as might be encountered in typical satellite Communications.

This correspondence deals with the coding gain for harddecisioned codes used over realistic channels. A simulation-based procedure is described, by which the performance (BER) curve of a coded system can be straightforwardly determined. The coding gain for real, i.e., "degraded" channels is discussed, vis-a-vis that for idealized channels. A formula for asymptotic coding gain for the latter type of channel is extended to channels with degradation. Finally, the coding gain for multiple-hop systems is briefly discussed.

Computer simulation is often used to estimate the bit error rate (BER) performance of digital communication systems. There are a number of distinct techniques in the simulation context that can be used to construct this estimate. A tutorial exposition of such techniques is provided, with particular reference to five specific methods which can be implemented in a simulation. These methods range from the traditional Monte Carlo trials to assumption of definite forms for the noise statistics. An attempt is made to show how these methods are related, and the specific assumptions that are invoked in order to apply them.

With the increasing demands being placed upon the geostationary orbit/spectrum resource, there is a need to consider the impact of increasing the intersatellite interference allowance. By increasing the intererence allowance, more satellites can be placed along the geostationary orbit. However, as a result, an individual sateilite's capacity will be reduced. This paper examines the tradeoff between intersatellite interference and total orbit capacity for both digital and analog modulation methods.

Importance sampling is one of the more useful techniques for reducing the number of samples required in Monte Carlo simulation. In the communication context, it has been applied and discussed in conjunction with single-hop digital systems. The purpose of this paper is to extend the method of application of importance sampling to multihop digital communication systems, with particular reference to the two-hop, or satellite, case. The situation is examined both for the case when the receiver is nonlinear and for when it is linear.

We study the statistical distribution of carrier-to-interference ratios when mutually interfering geostationary satellites are considered to be randomly positioned within their station-keeping tolerance bands. Results are obtained by approximate analysis and by Monte Carlo methods, from which one can ascertain "outage" probabilities as a function of the relevant parameters. For certain such probabilities and a carrier-to-interference ratio requirement, we also show the increased satellite spacing necessitated by imperfect station-keeping.

The geostationary orbit can accommodate only a finite number of satellites using any given frequency band. Technological development has made certain space bands very popular and has given rise to potential crowding. This article discusses the interplay of technical, operational and administrative factors in relation to orbit use.

The application of the generalized theory of extreme values is discussed. When the hypothesis of the theory is not strictly satisfied, as is inevitable in practice, we deal with an approximation that must be carefully used to yield accurate results. A numerical example provides some insight into this approximation problem.

The relationship between intersatellite spacing, error probability, and carrier-to-thermal noise ratio is studied for the case where the satellites transmit multilevel coherent phase-shift-keyed signals. Numerical results based on computer evaluation of exact expressions are presented for 2-, 4-, 8-, and 16-phase systems.