Regularized ZF in Cooperative Broadcast Channels under Distributed CSIT: A Large System Analysis

TL;DR

This paper analyzes the impact of distributed channel state information on the performance of regularized zero-forcing precoding in multi-antenna broadcast channels, using large system analysis and random matrix theory.

Contribution

It provides the first large system analysis of rate performance for distributed CSI in multi-TX broadcast channels, deriving deterministic equivalents of SINR.

Findings

Deterministic equivalents of SINR for regularized ZF in D-CSI settings.

Quantification of the performance degradation due to distributed CSI.

Insights into the trade-offs of cooperation with limited CSI sharing.

Abstract

Obtaining accurate Channel State Information (CSI) at the transmitters (TX) is critical to many cooperation schemes such as Network MIMO, Interference Alignment etc. Practical CSI feedback and limited backhaul-based sharing inevitably creates degradations of CSI which are specific to each TX, giving rise to a distributed form of CSI. In the Distributed CSI (D-CSI) broadcast channel setting, the various TXs design elements of the precoder based on their individual estimates of the global multiuser channel matrix, which intuitively degrades performance when compared with the commonly used centralized CSI assumption. This paper tackles this challenging scenario and presents a first analysis of the rate performance for the distributed CSI multi-TX broadcast channel setting, in the large number of antenna regime. Using Random Matrix Theory (RMT) tools, we derive deterministic equivalents of the Signal to Interference plus Noise Ratio (SINR) for the popular regularized Zero-Forcing (ZF) precoder, allowing to unveil the price of distributedness for such cooperation methods.