Adaptive Reduced-Rank MIMO Decoder for Military Communications

We propose an adaptive reduced-rank solution for decoding spatial multiplexed multiple-input multiple-output (MIMO) wireless communications based on the multi-stage Wiener filter (MSWF). 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 military applications where extreme channel conditions such as co-channel interference and intentional jamming can be expected. We show that the MSWF combined with successive interference (SIC) for spatial demultiplexing leads to significant signal subspace compression or rank-reduction. We propose for the MSWF-SIC a novel rank-reduction stopping rule that adapts to channel correlation and interference 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. 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.