Channel Estimation for OFDM Systems With Transmitter Diversity

Orthogonal Frequency-Division Multiplexing (OFDM) is now regarded as a feasible alternative to the conventional single carrier modulation techniques for high data rate communication systems, mainly because of its inherent equalisation simplicity. Transmitter diversity can effectively combat multipath channel impairments due to the dispersive wireless channel that can cause deep fades in some subchannels. The combination of the two techniques, OFDM and transmitter diversity, can further enhance the data rates in a frequency-selective fading environment. However, this enhancement requires accurate and computationally efficient channel state information when coherent detection is involved. A good choice for high accuracy channel estimation is the linear minimum mean-squared error (LMMSE) technique, but it requires a large number of processing operations. In this thesis, a deep and thorough study is carried out, based on the mathematical analysis and simulations in MATLAB, to find new and effective channel estimation methods for OFDM in a transmit diversity environment. As a result, three novel LMMSE based channel estimation algorithms are evaluated: real time LMMSE, LMMSE by significant weight catching (SWC) and low complexity LMMSE with power delay profile approximation as uniform. The new techniques and their combinations can significantly reduce the full LMMSE processor complexity, by 50% or more, when the estimation accuracy loss remains within 1-2 dB over a wide range of channel delay spreads and signal-to-noise ratios (SNR). To further enhance the channel estimator performance, pilot symbol structures are investigated and methods for statistical parameter estimation in real time are also presented.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Victoria University

Year dc:date.issued

2005

Author dc:creator

• Tolochko, Igor Aleksandrovich

• School of Communication and the Arts
• 280000 Information, Computing and Communication Sciences

Language dc:language

en