Input signal recovery from frequency-selective fading channels is a problem of great theoretical and practical importance. We present several new blind algorithms that utilize second-order statistics for direct multichannel equalization. The algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix. For a multiuser system, user signal separation can be achieved based on partial information of the composite channel response. These equalization algorithms do not rely on the precise separation of signal and noise subspaces and therefore tend to be less sensitive to channel order (or column rank) estimation errors. Equalization is directly achieved without channel identification. Furthermore, the equalizability conditions of these algorithms are discussed.

The recovery of input signals in a frequency selective fading channel is a problem of great theoretical and practical interest. We present several new blind algorithms that utilize second order statistics for multichannel equalization. The algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix. For multiuser system, user signal separation can be achieved based on partial information of the composite channel response. The equalization algorithms do not rely on the signal and noise subspace separation and therefore tend to be more robust to channel order estimation errors.

A family of new MMSE blind channel equalization algorithms based on second order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known subspace methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single or multiple user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given and simulation examples are provided to demonstrate their superior performance over some existing algorithms.

Asynchronous decorrelating detector is a linear multiuser detector in code-division multiple-access (CDMA) environment. It has relatively good performance and low-computational complexity. However, the ideal asynchronous decorrelating detector requires the observation of the entire input bit sequence. Implementation of this ideal detector based on a long bit sequence results in long delays and high computational complexity. In fact, if the observed window does not cover the entire data sequence, the classical asynchronous decorrelating detector is no longer near-far resistant. In this paper, we propose a method using initial decisions on bits for both edges of the finite observed window. These initial decisions are used to assist subsequent decisions of the whole sequence inside the observed window based on the decorrelating method. This new edge decision assisted decorrelator (EDAD) method is shown to be near-far resistant.