A class of asymptotically optimum iterated-decision multiuser detectors

A promising class of nonlinear multiuser detectors is introduced for CDMA systems. These “iterated-decision” multiuser detectors use optimized multipass algorithms to successively cancel multipleaccess interference (MAI) from received data and generate symbol decisions whose reliability increases monotonically with each iteration. They significantly outperform decorrelating detectors and linear minimum mean-square error (MMSE) multiuser detectors, but have the same order of computational complexity. When the ratio of the number of users to the spreading factor is below a certain threshold, iterated-decision multiuser detectors asymptotically achieve the performance of the “optimum” multiuser detector, i.e., maximum-likelihood (ML) decoding.