The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have received considerable interest recently. On space-time coding, some algorithms with perfect channel state information (CSI) have been proposed. In certain fast varying situation, however, it may be difficult to estimate the channel accurately and it is natural to study the blind detection algorithm without CSI. In this paper, based on subspace, a new blind detection algorithm without CSI is proposed. Using singular value decomposition (SVD) on output signal, noise subspace and signal subspace, which keep orthogonal to each other, are obtained. By searching the intersection of the signal subspace and the limited symbol vector set, symbol detection is achieved. The simulations illustrate that the proposed algorithm significantly improves system performance by receiving more output signals relative to transmit symbols. Furthermore, the presented algorithm is robust to the fading channel that changes between two successive blocks.