Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
EURASIP Journal on Wireless Communications and Networking - Special issue on synchronization in wireless communications
IEEE Transactions on Signal Processing
Blind identification of time-varying channels using multistep linear predictors
IEEE Transactions on Signal Processing
Optimal training for block transmissions over doubly selective wireless fading channels
IEEE Transactions on Signal Processing
Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems
IEEE Transactions on Signal Processing
Decision-directed estimation of MIMO time-varying Rayleigh fading channels
IEEE Transactions on Wireless Communications
Turbo processing for an OFDM-based MIMO system
IEEE Transactions on Wireless Communications
An Efficient Design of Doubly Selective Channel Estimation for OFDM Systems
IEEE Transactions on Wireless Communications
Low-Complexity Map Channel Estimation for Mobile MIMO-OFDM Systems
IEEE Transactions on Wireless Communications
Maximum-diversity transmissions over doubly selective wireless channels
IEEE Transactions on Information Theory
Orthogonal multiple access over time- and frequency-selective channels
IEEE Transactions on Information Theory
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The paper introduces a turbo (iterative) receiver design for joint channel estimation, synchronization and soft decoding in convolutional-coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. Employing the complex-exponential basis expansion model (CE-BEM) for representing doubly selective channels, a maximum likelihood (ML) objective function of carrier frequency offset (CFO) and MIMO time-varying channel responses (BEM coefficients) is formulated to develop a semi-blind ML framework for joint time-variant channel estimation and synchronization. To reduce the overhead of pilot signals without sacrificing estimation accuracy, the soft bit information from a soft-input soft-output (SISO) decoder is exploited in computing soft estimates of data symbols to be functioned as pilots for further enhancing the estimation accuracy after CFO and channel acquisition phase (initial coarse estimation) using pilots. In other words, the resulting semi-blind ML estimation scheme operates in conjunction with soft decoding process in a (iteratively) progressive manner to exploit remarkable gains of turbo processing (iterative extrinsic information exchange). Simulation results show that the proposed turbo joint channel estimation and synchronization scheme offers high estimation accuracy that approaches Cramér-Rao lower bounds (CRLBs) over a wide range of CFO values under low signal-to-noise ratio (SNR) conditions.