We derive an exact closed-form solution for the reliability of an ideal M-branch MMSE (minimum mean-squared error) diversity combiner operating in a Rayleigh-fading channel with N interferers, each having some specified average power. The reliability is defined as the probability, taken over fading of the desired and interfering signals, that the combiner's output signal-to-interference ratio (SINR) is greater than some specified threshold. This kind of metric is important in evaluating the potential capacity improvements of using diversity combining and adaptive array processing in interference-limited wireless systems. Our result is remarkably simple, fast, straightforward to compute, and numerically stable. We show a set of special cases, which relate to standard results and reveal valuable insights into how this type of array processing operates in interference-limited environments. We also present a set of numerical examples, which show that our calculated reliabilities agree with estimates from Monte Carlo simulation.

Importance sampling (IS) is a simulation technique which aims to reduce the variance (or other cost function) of a given simulation estimator. In communication systems, this usually, but not always, means attempting to reduce the variance of the bit error rate (BER) estimator. By reducing the variance, IS estimators can achieve a given precision from shorter simulation runs; hence the term "quick simulation." The idea behind IS is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. If these "important" values are emphasized by sampling more frequently, then the estimator variance can be reduced. Hence, the basic methodology in IS is to choose a distribution which encourages the important values. This use of a "biased" distribution will, of course, result in a biased estimator if applied directly in the simulation. However, there is a simple procedure whereby the simulation outputs are weighted to correct for the use of the biased distribution, and this ensures that the new IS estimator is unbiased. Hence, the "art" of designing quick simulations via IS is entirely dependent on the choice of biased distribution. Over the last 50 years, IS techniques have flourished, but it is only in the last decade that coherent design methods have emerged. The outcome of these developments is that at the expense of increasing technical content, modern techniques can offer substantial run-time saving for a very broad range of problems. We present a comprehensive history and survey of IS methods. In addition, we offer a guide to the strengths and weaknesses of the techniques, and hence indicate which techniques are suitable for various types of communications systems. We stress that simple approaches can still yield useful savings, and so the simulation practitioner as well as the technical researcher should consider IS as a possible simulation tool.