The increasing demand for high data rate transmission over broadband
radio channels has imposed significant challenges in wireless
communications. Accurate channel estimation has a major impact on
the whole system performance. Specifically, reliable estimate of the
channel state information (CSI) is more challenging for orthogonal
frequency division multiplexing (OFDM) systems in doubly selective
fading channels than for the slower fading channels over which OFDM
has been deployed traditionally. With the help of a basis expansion
model (BEM), a novel multivariate autoregressive (AR) process is
developed to model the time evolution of the fast fading channel.
Relying on pilot symbol aided modulation (PSAM), a novel Kalman
smoothing algorithm based on a second-order dynamic model is
exploited, where the mean square error (MSE) of the channel
estimator is near to that of the optimal Wiener filter. To further
improve the performance of channel estimation, a novel
low-complexity iterative joint channel estimation and symbol
detection procedure is developed for fast fading channels with a
small number of pilots and low pilot power to achieve the bit error
rate (BER) performance close to when the CSI is known perfectly. The
new channel estimation symbol detection technique is robust to
variations of the radio channel from the design values and
applicable to multiple modulation and coding types. By use of the
extrinsic information transfer (EXIT) chart, we investigate the
convergence behavior of the new algorithm and analyze the
modulation, pilot density, and error correction code selection for
good system performance for a given power level. The algorithms
developed in this thesis improve the performance of the whole system
requiring only low ratios of pilot to data for excellent performance
in fast fading channels. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3413 |
| Date | 18 July 2011 |
| Creators | Wan, Ping |
| Contributors | McGuire, Michael Liam, Dong, Xiaodai |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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