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Decision-Feedback Detection of Differential Unitary Space-Time Modulation in Fast Rayleigh-Fading Channels

With a fading channel, standard ML detection of Differential Unitary Space-Time Modulation (DUST) leads to an error floor in the BER-versus-SNR curve since it is derived under the assumption that the channel remains constant during every consecutive pair of matrixsymbols. In this paper, we present decision-feedback differential detection (DFDD) of DUST, which drastically reduces the error floor, especially in fast-fading channels. DFDD is derived from multiple-symbol ML detection and is shown to be equivalent to Wiener channel prediction followed by coherent ML detection. To derive the new detectors, we make the simplifying assumption that the channel changes once per matrix-symbol (i.e., block fading) rather than once per channel use (i.e., continuous fading). However, for the special case of diagonal space-time constellations, the block-fading assumption is not required. Exact and Chernoff bound expressions for pair-wise word-error probability (PWEP) are derived. An approximate expression for BER is derived from the PWEP which is in close agreement with the simulation results. The relationships between the performance and various system parameters, e.g., DFDD length, normalized Doppler frequency are then explored via numerical examples and related to the theoretical analysis.