Improved importance sampling technique for efficient simulation of digital communication systems

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