By Topic

Efficient simulation of orthogonal frequency division multiplexing systems using importance sampling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
P. Wijesinghe ; Sch. of Eng., Univ. of Western Sydney, Penrith South, NSW, Australia ; U. Gunawardana ; R. Liyanapathirana

This study presents the importance sampling (IS) analysis for the efficient simulation of orthogonal frequency division multiplexing (OFDM) systems. Importance sampling is an efficient rare-event simulation technique that aims at reducing the simulation runtime by reducing the variance of the estimator. The efficiency of IS highly relies on the proper selection of the biased simulation density function. This paper considers the problem of biasing the simulation density function of OFDM systems for achieving higher variance reduction gains and computational efficiencies. In particular, the authors show how to bias the time domain noise density function and how to correct the biased error count at the receiver in the frequency domain of an OFDM system operating over multipath fading channels with minimum mean square error equalisation. Further, the authors obtain the optimum biasing parameters for variance scaling method and mean translation method applied to bias the noise density function. The presented results can easily be generalised to OFDM systems operating over additive white Gaussian noise (AWGN) channels. The simulation results demonstrate that extensive gains in estimator variance reduction can be achieved through the proposed analysis rather than by using conventional Monte-Carlo simulations.

Published in:

IET Communications  (Volume:5 ,  Issue: 3 )