Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multi-user environments we might not know the number of active users and the channel they face and assuming maximal scenarios (maximum number of users and dispersive channels) might degrade the receiver performance. In this presentation, we propose a Bayesian nonparametric prior that it is able to detect an unbounded number of users with an unbounded channel delay. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each received symbol without a preamble.