A Bayesian nonparametric approach for blind multiuser channel estimation
Paper i proceeding, 2015

In many modern multiuser communication systems, users are allowed to enter and leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. We address the problem of blind joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop a Bayesian nonparametric model based on the Markov Indian buffet process and an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our experimental results show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios.

multiuser communication

factorial HMM

Bayesian nonparametric

machine-to-machine

Författare

I. Valera

Max Planck-institutet

F. J. R. Ruiz

Universidad Carlos III de Madrid

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

F. Perez-Cruz

Universidad Carlos III de Madrid

Nokia Oy

2015 23rd European Signal Processing Conference, EUSIPCO 2015

2766-2770 7362888

Ämneskategorier

Signalbehandling

DOI

10.1109/EUSIPCO.2015.7362888

ISBN

978-0-9928-6263-3

Mer information

Senast uppdaterat

2018-09-06