We develop a semi-blind adaptive space-time shift keying (STSK) based multiple-input multiple-output system using a low-complexity iterative channel estimation and data detection scheme. We first employ the minimum number of STSK training blocks, which is related to the number of transmitter antennas, to obtain a rough least square channel estimate (LSCE). Low-complexity single-stream maximum likelihood (ML) data detection is then carried out based on the initial LSCE and the detected data are utilised to refine the decision-directed LSCE. We show that a few iterations are sufficient to approach the optimal ML detection performance obtained with the aid of perfect channel state information.
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