Channel Covariance Estimation in Massive MIMO Frequency Division Duplex Systems

TL;DR

This paper proposes a novel method for estimating downlink channel covariance in FDD Massive MIMO systems by inferring it from uplink covariance using a dictionary-based interpolation approach, eliminating the need for feedback.

Contribution

It introduces a dictionary-based interpolation technique for downlink covariance estimation in FDD Massive MIMO, reducing reliance on user feedback and improving CSI acquisition.

Findings

Interpolation methods outperform traditional feedback-based estimation

Dictionary-based approach achieves accurate covariance inference

Simulation results validate the proposed method's effectiveness

Abstract

Channel covariance is emerging as a critical ingredient of the acquisition of instantaneous channel state information (CSI) in multi-user Massive MIMO systems operating in frequency division duplex (FDD) mode. In this context, channel reciprocity does not hold, and it is generally expected that covariance information about the downlink channel must be estimated and fed back by the user equipment (UE). As an alternative CSI acquisition technique, we propose to infer the downlink covariance based on the observed uplink covariance. This inference process relies on a dictionary of uplink/downlink covariance matrices, and on interpolation in the corresponding Riemannian space; once the dictionary is known, the estimation does not rely on any form of feedback from the UE. In this article, we present several variants of the interpolation method, and benchmark them through simulations.