Importance sampling for the random phase Gaussian channel

Importance sampling (IS) is developed as a variance reduction technique for Monte Carlo simulation of data communications over random phase additive white Gaussian noise channels. The binary problem (with known performance) is examined initially to determine parameter values and estimate the performance gain of IS. These results can then be applied to intractable m-ary signaling problems through composite IS. An example compares the performance of linear, square-law, and optimum receivers for binary block coded data.