Robust regularized ZF in decentralized Broadcast Channel with correlated CSI noise

Abstract

We consider in this work the Distributed Channel State Information (DCSI) Broadcast Channel (BC) setting, in which the various Transmitters (TXs) compute elements of the precoder based on their individual estimates of the global multiuser channel matrix. Previous works relative to the DCSI setting assume the estimation errors at different TXs to be uncorrelated, while we consider in contrast in this work that the CSI noises can be correlated. This generalization bridges the gap between the fully distributed and the centralized setting, and offers an avenue to analyze partially centralized networks. In addition, we generalize the regularized Zero Forcing (ZF) precoding by letting each TX use a different regularization coefficient. Building upon random matrix theory tools, we obtain a deterministic equivalent for the rate achieved in the large system limit from which we can optimize the regularization coefficients at different TXs. This extended precoding scheme in which each TX applies the optimal regularization coefficient is denoted as “DCSI Regularized ZF” and we show by numerical simulations that it allows to significantly reduce the negative impact of the distributed CSI configuration and is robust to the distribution of CSI quality level across all TXs.