The two main goals of this research are to study the implementation aspects of space-time turbo trellis codes (ST Turbo TC) and to develop efficient simulation methods for space-time and turbo coded systems using the importance sampling (IS) technique. The design of ST Turbo TC for improving the bandwidth efficiency and the reliability of wireless communication networks, which is based on the turbo structure, has been proposed in the literature. To achieve memory savings and reduce the decoding delay, this thesis proposes a simplified ST Turbo TC decoder using a sliding window (SW) technique. Different window sizes are employed and investigated. Through computer simulation, the optimum window sizes are determined for various system configurations. The effect of finite word length representation on the performance of ST Turbo TC is then studied. Simulation results show that ST Turbo TC is feasible for finite word length representation without significant degradation in the frame error rate performance. The optimum word length configurations are defined for all quantities external and internal to the ST Turbo TC decoder. For complex communication systems such as space-time codes and turbo codes, computer simulation is in fact the useful approach to obtain the estimated performance. To overcome the lengthy run-time requirements of the conventional Monte-Carlo (MC) method, this thesis introduces importance sampling simulation methods that accurately estimate the performances of turbo codes and space-time codes including orthogonal space-time block codes (OSTBC) and concatenated OSTBC. It is demonstrated that the proposed methods require much smaller sample sizes to achieve the same accuracy required by a conventional MC estimator. / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:ADTP/204257 |
| Date | January 2007 |
| Creators | Nguyen, Kim Chi, University of Western Sydney, College of Health and Science, School of Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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