A Rate Splitting Strategy for Massive MIMO with Imperfect CSIT

TL;DR

This paper introduces a Hierarchical-Rate-Splitting framework for massive MIMO systems with imperfect CSIT, improving multiuser interference management and significantly enhancing sum rate performance.

Contribution

It generalizes rate-splitting to large-scale arrays using channel statistics, proposing a novel Hierarchical-Rate-Splitting approach with optimized precoding and power allocation.

Findings

HRS outperforms baseline schemes in sum rate.

Closed-form power allocation provides system insights.

Simulation confirms significant sum rate gains.

Abstract

In a multiuser MIMO broadcast channel, the rate performance is affected by the multiuser interference when the Channel State Information at the Transmitter (CSIT) is imperfect. To tackle the detrimental effect of the multiuser interference, a Rate-Splitting (RS) approach has been proposed recently, which splits one selected user's message into a common and a private part, and superimposes the common message on top of the private messages. The common message is drawn from a public codebook and should be decoded by all users. In this paper, we generalize the idea of RS into the large-scale array regime with imperfect CSIT. By further exploiting the channel second-order statistics, we propose a novel and general framework Hierarchical-Rate-Splitting (HRS) that is particularly suited to massive MIMO systems. HRS simultaneously transmits private messages intended to each user and two kinds of common messages that can be decoded by all users and by a subset of users, respectively. We analyse the asymptotic sum rate of RS and HRS and optimize the precoders of the common messages. A closed-form power allocation is derived which provides insights into the effects of system parameters. Finally, simulation results validate the significant sum rate gain of RS and HRS over various baselines.