Simulation of digital communication systems using a stochastically optimised importance sampling technique

Importance sampling (IS) techniques can substantially accelerate bit error rate (BER) estimation using Monte Carlo (MC) simulation provided that the IS parameters specifying the simulation probability density function (PDF) are carefully chosen. We present an IS optimization algorithm based on stochastic gradient techniques. The formulation of the stochastic gradient descent (SGD) algorithm presented in this paper is quite general and system-independent, and its applicability is not restricted to a specific PDF or biasing scheme. The generality and effectiveness of the SGD algorithm are demonstrated by applying it to the problem of efficiently simulating a communication system with diversity combining, slow nonselective Rayleigh fading channel and noncoherent envelope detection