Developments in the Theory and Application of Importance Sampling

Hahn, Peter; Jeruchim, Michel C.

doi:10.1109/tcom.1987.1096838

Cited by 83 publications

(27 citation statements)

References 4 publications

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“…Placing sensors close to the center, therefore, can amount to wasting limited resources on what are predictably "non-events". This brings us to the subject of importance sampling [7].…”

Section: System Modelmentioning

confidence: 99%

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Distributed measurements for estimating and updating cellular system performance

Xiao

Greenstein

Mandayam

et al. 2008

IEEE Trans. Commun.

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Abstract-We investigate the use of distributed measurements for estimating and updating the performance of a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if a mobile receiver there has inadequate signal-to-noise ratio (SNR-based outage) or, using another criterion, inadequate signal-to-interference ratio (SIRbased outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to improve measurement efficiency (i.e., minimizing the required number of measurement sensors) while accurately estimating the outage probability and mapping the coverage holes. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is on the performance prediction accuracy of the sensor network, rather than on cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of Po can be accurately estimated using ∼ 10/Po power-measuring sensors distributed in a random uniform way over the area with basesensor distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based outage estimation for both indoor and outdoor environments.

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“…Placing sensors close to the center, therefore, can amount to wasting limited resources on what are predictably "non-events". This brings us to the subject of importance sampling [7].…”

Section: System Modelmentioning

confidence: 99%

“…The average and the variance are taken over different random selections of the sensor positions and the shadow fading realizations. This is the essence of importance sampling [7].…”

Section: A Is Concept and Analysismentioning

confidence: 99%

Distributed measurements for estimating and updating cellular system performance

Xiao

Greenstein

Mandayam

et al. 2008

IEEE Trans. Commun.

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“…To this end, we assume that a number of statistically independent simulations of the system are carried out. The independence assumption is standard in analyses of MC and IS techniques [5], [6], and is partly justified by analytical simplicity. Note that this assumption does not prevent application to systems with memory, as will be discussed in Section VI.…”

Section: Motivation and Statement Of The Estimator Biasmentioning

confidence: 99%

“…The normalized bias for the estimator (4) is obtained from (3) as (5) It is also shown in Appendix A that for , is a negative, decreasing function of , with (6) and that increases with . As a consequence of (6), for and for .…”

Section: Motivation and Statement Of The Estimator Biasmentioning

confidence: 99%

A simple sequential stopping rule for Monte Carlo Simulation

Mendo¹,

Hernando

2006

IEEE Trans. Commun.

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Abstract-In this paper, a sequential stopping rule for the estimation of a probability by means of Monte Carlo simulation is analyzed. It is shown that the proposed estimator is almost unbiased, and guarantees a given relative precision irrespective of . Under very mild conditions, the method also guarantees a certain confidence level for a given relative estimation error, provided that does not exceed a maximum value. An extension to importance sampling is discussed.

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“…With the aid of Figure 9, optimum γ can be obtained for a given simulation. More details can be found in [10].…”

Section: M P O R Ta N C E S a Mpling Es Timation Of Bit Error Probamentioning

confidence: 99%

Modelling, Performance Analysis and Design of WPAN Systems

Armada

Rodríguez

Jiménez

et al. 2006

Wireless Pers Commun

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Abstract. This paper covers the main issues that must be solved in order to design and analyse the performance of Wireless Personal Area Networks (WPANs) with the aid of simulation tools. We review state-of-the-art channel models to account for small-and large-scale propagation conditions in waveform-and system-level simulations. When dealing with waveform simulations, we also look at Montecarlo and importance sampling techniques that allow efficien estimation of error probabilities. Additionally, discrete channel models are introduced to efficientl link both waveform-and system-level approaches and we point out some methods to obtain the model parameters that are suitable for the wireless environment. The discussion of these techniques is complemented with two application examples that show the use of the different simulation levels for system design and performance study. With the paradigm of Bluetooth piconets we illustrate the usefulness of discrete channel models and we consider the design of an OFDM-based WPAN system to exemplify simulation from a waveform-level point of view.

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Developments in the Theory and Application of Importance Sampling

Cited by 83 publications

References 4 publications

Distributed measurements for estimating and updating cellular system performance

Distributed measurements for estimating and updating cellular system performance

A simple sequential stopping rule for Monte Carlo Simulation

Modelling, Performance Analysis and Design of WPAN Systems

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