Tackling pilot contamination in cell-free massive MIMO by Joint channel estimation and linear multi-user detection

Gholami, Roya; Cottatellucci, Laura; Slock, Dirk TM
ISIT 2021, IEEE International Symposium on Information Theory, 12-20 July 2021, Melbourne, Victoria, Australia (Virtual Conference)

In this paper we consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a much smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for joint channel estimation and data detection. Under the challenging assumption of deterministic parameters, we determine sufficient conditions and necessary conditions for semi-blind identifiability, which guarantee the non-singularity of the Fisher Information Matrix (FIM) and the existence of the Cramer-Rao bound (CRB). We propose a message passing (MP) algorithm which determines the exact channel coefficients in the case of semiblind identifiability. We show that the system is identifiable if the Karp-Sipser algorithm yields an empty core. Additionally, we propose a Bayesian semi-blind approach which results in an effective algorithm for joint channel estimation and multi-user detection. This algorithm alternates between channel estimation
and linear multi-user detection. Numerical simulations verify the analytical derivations.

DOI
Type:
Conference
City:
Melbourne
Date:
2021-07-11
Department:
Communication systems
Eurecom Ref:
6573
Copyright:
© 2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PERMALINK : https://www.eurecom.fr/publication/6573