Adaptive Importance Sampling for Performance Evaluation and Parameter Optimization of Communication Systems

We present new adaptive importance sampling techniques based on stochastic Newton recursions. Their applicability to the performance evaluation of communication systems is studied. Besides bit-error rate (BER) estimation, the techniques are used for system parameter optimization. Two system models that are analytically tractable are employed to demonstrate the validity of the techniques. As an application to situations that are analytically intractable and numerically intensive, the influence of crosstalk in a wavelength-division multiplexing (WDM) crossconnect is assessed. In order to consider a realistic system model, optimal setting of thresholds in the detector is carried out while estimating error rate performances. Resulting BER estimates indicate that the tolerable crosstalk levels are significantly higher than predicted in the literature. This finding has a strong impact on the design of WDM networks. Power penalties induced by the addition of channels can also be accurately predicted in short run-time

Crosstalk Statistics via Collocation Method

A probabilistic model for the evaluation of transmission lines crosstalk is proposed. The geometrical parameters are assumed to be unknown and the exact solution is decomposed into two functions, one depending solely on the random parameters and the other on the frequency. The stochastic collocation method is used to estimate the crosstalk statistical moments. The results are obtained from a limited number of carefully-chosen values of the random geometrical parameters. The estimated statistical moments are then used to build the probability density function of the crosstalk parameters. A Monte Carlo validation demonstrates the accuracy and efficiency of the advocated method.\ud \u