Dual-Polarized FDD Massive MIMO: A Comprehensive Framework

TL;DR

This paper introduces a comprehensive framework for dual-polarized FDD massive MIMO systems, addressing challenges in channel covariance estimation and multi-user training to enable efficient beamforming.

Contribution

It develops a unified three-step framework for UL-DL covariance transformation and multi-user DL training in dual-polarized FDD massive MIMO systems, which was previously challenging.

Findings

Effective UL covariance estimation from noisy pilots

Accurate DL covariance transformation from UL covariance

Enabling interference-free multi-user DL beamforming

Abstract

We propose a comprehensive scheme for realizing a massive multiple-input multiple-output (MIMO) system with dual-polarized antennas in frequency division duplexing (FDD) mode. Employing dual-polarized elements in a massive MIMO array has been common practice recently and can, in principle, double the number of spatial degrees of freedom with a less-than-proportional increase in array size. However, processing a dual-polarized channel is demanding due to the high channel dimension and the lack of Uplink-Downlink (UL-DL) channel reciprocity in FDD mode. In particular, the difficulty arises in channel covariance acquisition for both UL and DL transmissions and in common training of DL channels in a multi-user setup. To overcome these challenges, we develop a unified framework consisting of three steps: (1) a covariance estimation method to efficiently estimate the UL covariance from noisy, orthogonal UL pilots; (2) a UL-DL covariance transformation method that obtains the DL covariance from the estimated UL covariance in the previous step; (3) a multi-user common DL channel training with limited DL pilot dimension method, which enables the BS to estimate effective user DL channels and use them for interference-free DL beamforming and data transmission. We provide extensive empirical results to prove the applicability and merits of our scheme.