Efficient importance sampling simulations for digital communication systems
Abstract
Importance sampling is a modified Monte Carlo simulation technique which can dramatically reduce the computational cost of the Monte Carlo method. A complete development is presented for its use in the estimation of bit error rates $P\sb b$ for digital communication systems with small Gaussian noise inputs. Emphasis is on the optimal mean-translation Gaussian simulation density function design and the event simulation method as applied to systems which employ quasi-regular trellis codes. These codes include the convolutional codes and many TCM (Ungerboeck) codes. Euclidean distance information of a code is utilized to facilitate the simulation. Also, the conditional importance sampling technique is presented which can handle many non-Gaussian system inputs. Theories as well as numerical examples are given. In particular, we study the simulations of an uncoded MSK and a trellis-coded 8-PSK transmissions over a general bandlimited nonlinear satellite channel model. Our algorithms are shown to be very efficient at low $P\sb b$ compared to the ordinary Monte Carlo method. Many techniques we have developed are applicable to other system simulations as building blocks for their particular system configurations and channels.
Degree
Ph.D.
Advisors
Sadowsky, Purdue University.
Subject Area
Electrical engineering|Systems design|Operations research
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