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In this work, we consider the sum rate performance of joint processingcoordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in aso-called distributed channel state information (D-CSI) setting. In the D-CSIsetting, the various transmitters (TXs) acquire a local, TX-dependent, estimateof the global multi-user channel state matrix obtained via terminal feedbackand limited backhauling. The CSI noise across TXs can be independent orcorrelated, so as to reflect the degree to which TXs can exchange informationover the backhaul, hence allowing to model a range of situations bridging fullydistributed and fully centralized CSI settings. In this context we aim to studythe price of CSI distributiveness in terms of sum rate at finite SNR whencompared with conventional centralized scenarios. We consider the family ofJP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our studyin the large scale antenna regime, as it is currently envisioned to be used inreal 5G deployments. It is then possible to obtain accurate approximations onso-called deterministic equivalents of the signal to interference and noiseratios. Guided by the obtained deterministic equivalents, we propose anapproach to derive a RZF scheme that is robust to the distributed aspect of theCSI, whereby the key idea lies in the optimization of a TX-dependent powerlevel and regularization factor. Our analysis confirms the improved robustnessof the proposed scheme with respect to CSI inconsistency at different TXs, evenwith moderate number of antennas and receivers (RXs).

Robust Regularized ZF in Cooperative Broadcast Channel under Distributed CSIT