Abstract-We present the joint maximum likelihood (ML) symbol-time and carrier-frequency offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. Redundant information contained within the cyclic prefix enables this estimation without additional pilots. Simulations show that the frequency estimator may be used in a tracking mode and the time estimator in an acquisition mode.

This thesis focuses on advanced signal processing techniques for multicarrier modulation, in particular, orthogonal frequency division multiplexing (OFDM). OFDM promises a substantial increase in data rate and robustness against the frequency selectivity of multipath channels. For coherent detection, channel estimation is essential for receiver design. In this thesis, we will present a receiver design where the channel estimator exploits the sparse nature of the physical channel. We present the most popular subspace algorithm from the array processing literature, namely root-MUSIC, recent sparse identification algorithms in the form of orthogonal matching pursuit (OMP) and basis pursuit (BP), and a hybrid method called path identification (PI) algorithm which is the main contribution of this thesis. We also compare the performance of these estimators with that of the conventional estimators such as least-squares (LS) estimator and linear minimum-mean-squares estimator (LMMSE).iii

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A new approach to low-complexity channel estimation in orthogonal-frequency division multiplexing (OFDM) systems is proposed. A lowrank approximation is applied to a linear minimum mean-squared error (LMMSE) estimator that uses the frequency correlation of the channel. By using the singular-value decomposition (SVD) an optimal low-rank estimator is derived, where performance is essentially preservedeven for low computational complexities. A fixed estimator, with nominal values for channel correlation and signalto-noise ratio (SNR), is analysed. Analytical meansquared error (MSE) and symbol-error rates (SER) are presented for a 16-QAM OFDM system.

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