The objective of the proposed research is to devise high-performance and low-complexity signal-detection algorithms for communication systems over fading channels. They include channel equalization to combat intersymbol interference
(ISI) and multiple input multiple output (MIMO) signal detection to deal with multiple access interference (MAI) from other transmit antennas. As the demand for higher data-rate and more efficiency wireless communications increases, signal detection becomes more challenging.
We propose novel transmission and iterative signal-detection techniques based on energy spreading transform (EST). Different from the existing iterative methods based on the turbo
principle, the proposed schemes are independent of channel coding. EST is an orthonormal that spreads a symbol energy over the symbol
block in time and frequency for channel equalization; space and time for MIMO signal detection with flat fading channels; and
space, time, and frequency for MIMO signal detection with frequency-selective fading channels. Due to the spreading, EST obtains diversity in the available domains for the specific application and increases the reliability of the feedback signal. Moreover, it enables iterative signal detection that has near
interference-free performance only at the complexity of linear detectors.
Either a hard or soft decision can be fed back to the interference-cancellation stage at the subsequent iteration. The soft-decision scheme prevents error propagation of the hard-decision scheme for a low SNR and improves the performance.
We analyze the performance of the proposed techniques. Analytical and simulation results show that these schemes perform very close
to the interference-free systems.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7509 |
| Date | 10 November 2005 |
| Creators | Hwang, Taewon |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 878865 bytes, application/pdf |
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