Adaptive Reduced-Rank Interference Suppression for MIMO Decoding

We present an adaptive reduced-rank solution for robust decoding of spatial multiplexed multiple-input multiple-output (MIMO) wireless communications using the multi-stage Wiener filter (MSWF). The huge spectral efficiency gains promised by spatial multiplexing schemes, such as vertical Bell Labs space time (V-BLAST) and space-division multiple access (SDMA), are difficult to realize for less than ideal channel conditions. This is particularly true for military applications where extreme channel conditions such as co-channel interference and intentional jamming can be expected. We show that the MSWF, first proposed by Goldstein and Reed for sensor array application, as combined here with spatial multiplexing decoding method successive interference (SIC) leads to significant signal subspace compression or rank-reduction. As a result our solution converges much faster than full rank methods permitting shorter training length and robustness to co-channel interference and jamming. Also, significant computational reduction is realized making this method a good candidate for portable applications where computational resources are limited. We also present a novel rank-reduction stopping rule which adapts to channel condition. Bit error rate (BER) versus SNR for the correlated channel and for co-channel interference and SINR versus the MSWF rank-reduction and length of training interval are presented