This thesis is intended to provide several new bounding techniques for channel codes over quasi-static fading channels (QSFC). This type of channel has drawn more and more attention recently with the demanding need for higher capacity and more reliable wireless communication systems. Although there have been some published results on analyzing the performance of channel codes over QSFCs, most of them produced quite loose performance upper bounds. In this thesis, the general Gallager bounding approach which provides convergent upper bounds of coded systems over QSFCs is addressed first. It is shown that previous Gallager bounds employing trivial low SNR bounds tended to be quite loose. Then improved low instantaneous SNR bounds are derived for two classes of convolutional codes including turbo codes. Consequently, they are combined with the classical Union-Chernoff bound to produce new performance upper bounds for simple convolutional and turbo codes over single-input single-output (SISO) QSFCs. The new bound provides a much improved alternative to characterizing the performance of channel codes over QSFCs over the existing ones. Next the new bounding approach is extended to cases of serially concatenated space-time block codes, which show equivalence with SISO QSFCs. Tighter performance bounds are derived for this coding scheme for two specific cases: first a convolutional code, and later a turbo code. Finally, the more challenging cases of multiple-input multiple-output (MIMO) QSFCs are investigated. Several performance upper bounds are derived for the bit error probability of different cases of space-time trellis codes (STTC) over QSFCs using a new and tight low SNR bound. Also included in this work is an algorithm for computing the unusual information eigenvalue spectrum of STTCs.
| Identifer | oai:union.ndltd.org:TEXASAandM/oai:repository.tamu.edu:1969.1/1650 |
| Date | 01 April 2005 |
| Creators | Hu, Jingyu |
| Contributors | Miller, Scott L., Georghiades, Costas N., Chang, Kai, Liu, Jyh C. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis, text |
| Format | 901138 bytes, electronic, application/pdf, born digital |
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