An iterative maximum a posteriori (MAP) estimator for multiuser detection in synchronous CDMA systems

This paper proposes an iterative algorithm for multiuser detection in synchronous CDMA communication systems based on maximum a posteriori (MAP) estimation. This approach performs better than the maximum likelihood (ML) estimator due to the use of probabilistic knowledge regarding the distribution of the received signal amplitudes. We assume that for every user the PDF of the signal level value in a bit time is the average of two Gaussian distributions centered at plus and minus the mean signal amplitude. The result is a gradient-based algorithm containing a ML term that determines the “direction” of bit estimation convergence, and a prior knowledge term that improves the accuracy of the bit estimate. Due to the prior knowledge term, the algorithm reaches a minimum closer to the true received bit values and therefore realizes more accurate bit estimates and a lower bit error rate (BER) than ML alone.