FDD massive MIMO channel spatial covariance conversion using projection methods

TL;DR

This paper introduces projection-based algorithms to estimate downlink channel covariance in FDD massive MIMO systems from uplink data, reducing feedback overhead and improving accuracy over existing methods.

Contribution

It proposes novel projection methods exploiting angular reciprocity to estimate downlink covariance from uplink measurements in massive MIMO systems.

Findings

Outperforms state-of-the-art solutions in accuracy.

Reduces computational complexity.

Effective for typical array geometries and duplex gaps.

Abstract

Knowledge of second-order statistics of channels (e.g. in the form of covariance matrices) is crucial for the acquisition of downlink channel state information (CSI) in massive MIMO systems operating in the frequency division duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance information via feedback of the estimated covariance matrix from the user equipment (UE), but in the massive MIMO regime this approach is infeasible because of the unacceptably high training overhead. This paper considers instead the problem of estimating the downlink channel covariance from uplink measurements. We propose two variants of an algorithm based on projection methods in an infinite-dimensional Hilbert space that exploit channel reciprocity properties in the angular domain. The proposed schemes are evaluated via Monte Carlo simulations, and they are shown to outperform current state-of-the art solutions in terms of accuracy and complexity, for typical array geometries and duplex gaps.