A simulation algorithm design strategy based on the combination of event simulation, conditional importance sampling, and asymptotically optimal biasing of Gaussian noise inputs is discussed. The utility of this approach is illustrated by presenting numerical results for a satellite channel model that includes uplink and downlink noise sources, a travelling-wave tube amplifier (TWTA) nonlinearity, and intersymbol interference (ISI) from both uplink and downlink filtering. An overview and comparison of various simulation design strategies and some results on the optimization of general mean translation and variance scaling biasing schemes for nonlinear systems are presented.>

An improved importance sampling technique for the efficient simulation of digital communication systems is proposed. Evaluation of low-probability error events by direct use of classical Monte Carlo (MC) simulation techniques usually involves a very large number of runs. Importance-sampling techniques make the low-probability events occur more frequently. The technique proposed here is based on optimized translations of the original probability densities. The only approximation needed in the optimizations is that of replacing the Q function by the simpler exponential expression. Detailed analytical evaluations of the estimation variances of the classical MC and the conventional and improved importance-sampling approaches for systems with memories and signals are presented and compared, showing the superior performance of the latter. Detailed numerical and simulation results are given.>