Multi-user generic channel equalization and sources separation for MIMO communication systems.

In this work, we study blind equalization techniques to mitigate inter-symbol interference (ISI), and blind sources separation (BSS) in multiple inputs multiple outputs (MIMO) communication systems. Mainly, we are interested in the multi-user generic blind equalizer (MU-GBE). A MU-GBE has no prior information about the transmission channels and the used constellations. To solve this challenge, a joint MU-GBE, based on a new multi-criteria cost function and automatic modulation classification (AMC) is proposed. The new multi-users multi-criteria cost function is based on the probability density fitting (PDF) and the K-nearest neighbors (KNN) algorithm is considered for the AMC stage. This approach is implemented in its linear and nonlinear versions. For the nonlinear case, we use a neural network. Numerical results, in terms of mean square error (MSE) and symbol error rate (SER) with quadrature amplitude modulation (QAM) signals show that our multi users multi-criteria GBE is effective in alleviating the ISI.