Turbo multiuser detection for nonlinearly modulated CDMA in unknown channels

This paper introduces a blind iterative multiuser detection scheme for demodulating nonlinearly modulated CDMA signals in unknown channels based on the Bayesian inference. The detector jointly estimates the channel gains, the noise variance and the transmitted symbols. It yields asymptotically to the MAP receiver (or minimum error probability on decisions) by using the Gibbs sampler, a Markov Chain Monte Carlo method for Bayesian computation. Its performance is compared with, that of the coherent parallel interference cancelation receiver and an iterative detector based on interference suppression. It is seen that the proposed Bayesian multiuser detector offers substantial performance gain over those existing methods, especially in a highly loaded system. Being a "soft-input soft-output" algorithm, the Bayesian multiuser detector is particularly well suited for turbo processing in a coded system.