Modern communications has become far more than point-to-point calling and wireless communications is part of every-day life. Driven by ever growing demand for high data rate communication, multiple-access techniques are of interest for allowing multiple users to share limited resources, such as frequency, time and space. Commercially introduced in 1995, Code-Division Multiple-Access (CDMA) quickly became one of the world's fastest-growing wireless technologies. However, CDMA is subject to some limiting factors, such as multiple-access interference (MAI), which dramatically affects the capacity of the wireless system and degrades performance. Fortunately, these effects can be alleviated by applying advanced signal processing techniques such as multiuser detection (MUD), which potentially provides a large increase in system capacity, enhances spectral efficiency, and relaxes requirements for power control. / Further improvements of MUD can be obtained through joint multiuser detection/decoding. However this is a very complex approach. Inspired by Turbo codes and iterative decoding, Turbo-MUD and iterative multiuser decoding have been proposed. The main objective of this research is to analyse the existing iterative techniques applied to Turbo multiuser decoding for coded CDMA systems and propose new decoder structures to improve the system performance. / In this thesis, we observe that many of the iterative multiuser decoding algorithms in the literature are focused on exchanging information obtained within the most current iteration. However, if correlations over iterations are low, then in principle the bit error rate (BER) performance can be improved by combining signal estimates over iterations. Inspired by this idea, iterative maximal ratio combining (MRC) is proposed in this thesis for application to iterative decoding structures. With this approach all previous estimates are recursively weighted and combined to refine the current signal estimates. The derivation of the corresponding weighting factors is based on the statistics of the decoder outputs over iterations, which leads to maximizing the resultant signal-to-noise ratio (SNR) for each current signal estimate. It is shown that the recursive MRC scheme can be widely applied to many existing iterative structures and provide significantly improved system performance with acceptable computational complexity. In addition, the analytic and numerical results illustrate that the resulting performance gain from the application of MRC is inversely proportional to the correlation of the decoder estimates across iterations. The more correlated the signal estimates over consecutive iterations are, the slower system convergence will be, if MRC is employed over all iterations. MRC over only a few initial iterations where correlation across those iterations is low provides faster convergence. A truncated MRC is suggested, which provides better performance while maintaining low computational complexity. Simulation results based on monte carlo averaging demonstrate that the system performance for the proposed techniques is better than many existing algorithms in the literature. / Thesis (MA(Telecommunications))--University of South Australia, 2005.
| Identifer | oai:union.ndltd.org:ADTP/267510 |
| Creators | Lin, Tao. |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | copyright under review |
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