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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1304

Title: Iterative joint detection and decoding of LDPC-Coded V-BLAST systems
Authors: Tsai, Meng-Ying (Brady)

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Keywords: Bell Labs Layered Space-Time (BLAST)
Soft-input soft-output detectors
Low-density parity check (LDPC) codes
Iterative decoding
Joint detection and decoding
Issue Date: 2008
Series/Report no.: Canadian theses
Abstract: Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalization over the entire observation space is intractable; and the current literature is unable to guide us towards the best way to obtain the suboptimal soft information. In this thesis, several existing soft-input soft-output (SISO) detectors, including minimum mean-square error-successive interference cancellation (MMSE-SIC), list sphere decoding (LSD), and Fincke-Pohst maximum-a-posteriori (FPMAP), are examined. Prior research has demonstrated that LSD and FPMAP outperform soft-equalization methods (i.e., MMSE-SIC); however, it is unclear which of the two scheme is superior in terms of performance-complexity trade-off. A comparison is conducted to resolve the matter. In addition, an improved scheme is proposed to modify LSD and FPMAP, providing error performance improvement and a reduction in computational complexity simultaneously. Although list-type detectors such as LSD and FPMAP provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. A new detection scheme is proposed to address the above issues with fixed detection complexity, making the scheme suitable for practical implementation.
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-07-08 19:29:17.66
URI: http://hdl.handle.net/1974/1304
Appears in Collections:Queen's Theses & Dissertations

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