Multiuser detection in a dynamic environment: Joint user identification and parameter estimation

The problem of jointly estimating the number, the identities, and the data of active users in a dynamic multiuser environment was examined in [1]. This paper extends the results of [1] to the more general case where some unknown continuous parameters of the active-user signals must also be estimated. This problem, which cannot be solved with traditional signal processing techniques, is solved here by applying the theory of random finite sets constructed on hybrid spaces. In particular, we derive Bayes recursions that describe the evolution with time of a posteriori densities of the unknown parameters and data. Since these recursions do not admit closed-form expressions, we use numerical approximations based on Monte Carlo methods ("particle filtering"). Simulation results, referring to a CDMA system, are presented to illustrate the theory.