In this dissertation, the problem of symbol timing synchronization for the following three different communication systems is studied: 1) conventional single-carriertransmissions with single antenna in both transmitter and receiver; 2) single-carriertransmissions with multiple antennas at both transmitter and receiver; and 3) orthogonal frequency division multiplexing (OFDM) based IEEE 802.11a wireless localarea networks (WLANs).For conventional single-carrier, single-antenna systems, a general feedforwardsymbol-timing estimation framework is developed based on the conditional maximumlikelihood principle. The proposed algorithm is applied to linear modulations and twocommonly used continuous phase modulations: MSK and GMSK. The performanceof the proposed estimator is analyzed analytically and via simulations.Moreover, using the newly developed general estimation framework, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-squareerror expression for this class of estimators is established and the best estimators aredetermined. Simulation results are presented to corroborate the analytical results.Moving on to single-carrier, multiple-antenna systems, we present two algorithms. The first algorithm is based on a heuristic argument and it improves theoptimum sample selection algorithm by Naguib et al. so that accurate timing estimates can be obtained even if the oversampling ratio is small. The performance ofthe proposed algorithm is analyzed both analytically and via simulations.The second algorithm is based on the maximum likelihood principle. The dataaided (DA) and non-data aided (NDA) ML symbol timing estimators and their cor-responding CCRB and MCRB in MIMO correlated ??at-fading channels are derived.It is shown that the improved algorithm developed based on the heuristic argumentis just a special case of the DA ML estimator. Simulation results under differentoperating conditions are given to assess and compare the performances of the DAand NDA ML estimators with respect to their corresponding CCRBs and MCRBs.In the last part of this dissertation, the ML timing synchronizer for IEEE 802.11aWLANs on frequency-selective fading channels is developed. The proposed algorithmis compared with four of the most representative timing synchronization algorithms,one specically designed for IEEE 802.11a WLANs and three other algorithms designed for general OFDM frame synchronization.
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