J. Keith Townsend

Learn More
Importance sampling (IS) is recognized as a potentially powerful method for reducing simulation run times when estimating the probabilities of rare events in communication systems using Monte Carlo simulation. Of special interest is the probability of buffer overflow in networks of queues. When simulating networks of queues, regenerative techniques make the(More)
Importance sampling is recognized as a potentially powerful method for reducing simulation runtimes when estimating the bit error rate (BER) of communications systems using Monte Carlo simulation. Analytically, minimizing the variance of the importance sampling (IS) estimator with respect to the biasing parameters has typically yielded solutions for systems(More)
Rare event simulation is an important area of simulation theory, producing algorithms that can significantly reduce the simulation time when analyzing problems that involve rare events. However, existing rare event simulation techniques are rather restrictive, i.e., applicable only to systems with modest complexity. In this paper, we first develop a Markov(More)
Performance constraints in communication systems and networks require the error or fault probabilities to be very low. Analytical and numerical models are often too restrictive and Monte Carlo simulation is computationally prohibitive for low probabilities. Importance sampling (IS) has been used to estimate rare event probabilities but is increasingly(More)