Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE) and guarantee better performance than the traditional zero forcing (ZF) or minimum mean square error (MMSE) algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels

Fast Adaptive Blind MMSE Equalizer for Multichannel FIR Systems

<p/> <p>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 <inline-formula><graphic file="1687-6180-2006-014827-i1.gif"/></inline-formula> to <inline-formula><graphic file="1687-6180-2006-014827-i2.gif"/></inline-formula>, where <inline-formula><graphic file="1687-6180-2006-014827-i3.gif"/></inline-formula> is the number of emitted signals, <inline-formula><graphic file="1687-6180-2006-014827-i4.gif"/></inline-formula> the data vector length, and <inline-formula><graphic file="1687-6180-2006-014827-i5.gif"/></inline-formula> 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.</p