Abstract
We propose a new blind minimum mean square error (MMSE)
equalization algorithm of noisy multichannel finite impulse
response (FIR) systems, that relies only on second-order
statistics. The proposed algorithm offers two important
advantages: a low computational complexity and a relative
robustness against channel order overestimation errors. Exploiting
the fact that the columns of the equalizer matrix filter belong
both to the signal subspace and to the kernel of truncated data
covariance matrix, the proposed algorithm achieves blindly a
direct estimation of the zero-delay MMSE equalizer parameters. We
develop a two-step procedure to further improve the performance
gain and control the equalization delay. An efficient fast
adaptive implementation of our equalizer, based on the projection
approximation and the shift invariance property of temporal data
covariance matrix, is proposed for reducing the computational
complexity from O(n3) to O(qnd), where q is the number of
emitted signals, n the data vector length, and d the dimension
of the signal subspace. We then derive a statistical performance
analysis to compare the equalization performance with that of the
optimal MMSE equalizer. Finally, simulation results are provided
to illustrate the effectiveness of the proposed blind equalization
algorithm.