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Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have been attracting increased attention. The so-called orthogonal space-time block codes (OSTBCs) have been of particular interest due to their good performance and low decoding complexity. In this paper, we take a systematic maximum-likelihood (ML) approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions. We derive a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data. Furthermore, we discuss the design of optimal training sequences and optimal information transfer to an outer decoder. Numerical examples demonstrate the performance of our algorithm.
Date of Publication: Feb 2003