  In this work, we consider the sum rate performance of joint processing
coordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in a
so-called distributed channel state information (D-CSI) setting. In the D-CSI
setting, the various transmitters (TXs) acquire a local, TX-dependent, estimate
of the global multi-user channel state matrix obtained via terminal feedback
and limited backhauling. The CSI noise across TXs can be independent or
correlated, so as to reflect the degree to which TXs can exchange information
over the backhaul, hence allowing to model a range of situations bridging fully
distributed and fully centralized CSI settings. In this context we aim to study
the price of CSI distributiveness in terms of sum rate at finite SNR when
compared with conventional centralized scenarios. We consider the family of
JP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our study
in the large scale antenna regime, as it is currently envisioned to be used in
real 5G deployments. It is then possible to obtain accurate approximations on
so-called deterministic equivalents of the signal to interference and noise
ratios. Guided by the obtained deterministic equivalents, we propose an
approach to derive a RZF scheme that is robust to the distributed aspect of the
CSI, whereby the key idea lies in the optimization of a TX-dependent power
level and regularization factor. Our analysis confirms the improved robustness
of the proposed scheme with respect to CSI inconsistency at different TXs, even
with moderate number of antennas and receivers (RXs).
