Semi-blind signal estimation for smart antennas using subspace tracking

We discuss a semi-blind algorithm for smart antennas that utilises user identifiers that are available in existing cellular systems in addition to some structural signal properties. Our algorithm estimates, first, the row span of the adequately composed data matrix. In this part we gain a factor of up to 70 in computational complexity by employing subspace tracking algorithms. After that we project the obtained basis vectors iteratively to the finite alphabet constellation. We call the projection part of our algorithm DILSF (decoupled iterative least-squares with subspace fitting). Assuming two co-channel users in a GSM-like TDMA system and a Rayleigh fading channel with finite angular spread, we obtained a BER<10^-3 with only 3 antennas, spaced 10λ apart. We also investigate the effect of the rank estimation accuracy on the performance and find the algorithm insensitive to minor rank estimation errors