Importance sampling is a modified Monte Carlo technique which can substantially reduce computer run time of a simulation. The basic principle is to bias the simulation distribution in order to increase the relative frequency of the 'important' events. Proper weighting of the simulation data results in an unbiased Monte Carlo estimator which often requires orders of magnitude fewer simulation runs than ordinary Monte Carlo to obtain the same specified precision. This paper presents an efficient importance sampling technique for estimating the distribution of computation for sequential decoders that employ the stack algorithm. This method uses a stationary biasing of noise inputs that alters the drift of the node metric process in an ensemble average sense. The biasing is applied only up to a certain point in time; the point where the correct path node metric minimum occurs.
|Number of pages||12|
|Journal||ATR. Australian telecommunication research|
|Publication status||Published - 1 Jan 1991|