Iterative Receiver Design with Joint Channel Estimation and Synchronization for Coded MIMO-OFDM over Doubly Selective Channels

Abstract

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 timeand 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 semiblind 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.