Blind estimation of unknown channel parameters and data symbol detection
represent major open problems in non-cooperative communication systems such as
automatic modulation classification (AMC). This thesis focuses on estimating the
symbol rate and detecting the data symbols. A blind oversampling-based signal
detector under the circumstance of unknown symbol period is proposed. The thesis
consists of two parts: a symbol rate estimator and a symbol detector.
First, the symbol rate is estimated using the EM algorithm. In the EM algorithm,
it is difficult to obtain the closed form of the log-likelihood function and the density
function. Therefore, both functions are approximated by using the Particle Filter
(PF) technique. In addition, the symbol rate estimator based on cyclic correlation
is proposed as an initialization estimator since the EM algorithm requires initial
estimates. To take advantage of the cyclostationary property of the received signal,
there is a requirement that the sampling period should be at least four times less than
the symbol period on the receiver side.
Second, the blind data symbol detector based on the PF algorithm is designed.
Since the signal is oversampled at the receiver side, a delayed multi-sampling PF
detector is proposed to manage inter-symbol interference, which is caused by over-
sampling, and to improve the demodulation performance of the data symbols. In the
PF algorithm, the hybrid importance function is used to generate both data samples and channel model coe±cients, and the Mixture Kalman Filter (MKF) algorithm is
used to marginalize out the fading channel coe±cients. At the end, two resampling
schemes are adopted.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2729 |
| Date | 15 May 2009 |
| Creators | Park, Sang Woo |
| Contributors | Serpedin, Erchin |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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