Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems

TL;DR

This paper introduces a tensor-based approach for efficient downlink channel estimation in dual-polarized massive MIMO systems, leveraging low-rank tensor models and decomposition algorithms to reduce training overhead and improve parameter identifiability.

Contribution

It models dual-polarized double directional channels as low-rank tensors and develops a compressed tensor decomposition method for improved channel estimation with limited training.

Findings

Tensor modeling enables effective channel parameter estimation.

Compressed tensor decomposition reduces training overhead.

Channel parameters are identifiable under mild conditions.

Abstract

The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach---since such channel can be fully characterized by several key parameters. However, most existing channel estimation work under the DD model has not yet considered DP arrays, perhaps because of the complex array manifold and the resulting difficulty in algorithm design. In this paper, we first reveal that the DD channel with DP arrays at the transmitter and receiver can be naturally modeled as a low-rank tensor, and thus the key parameters of the channel can be effectively estimated via tensor decomposition algorithms. On the theory side, we show that the DD-DP parameters are identifiable under very mild conditions, by leveraging identifiability of low-rank tensors. Furthermore, a compressed tensor decomposition algorithm is developed for alleviating the downlink training overhead. We show that, by using judiciously designed pilot structure, the channel parameters are still guaranteed to be identified via the compressed tensor decomposition formulation even when the size of the pilot sequence is much smaller than what is needed for conventional channel identification methods, such as linear least squares and matched filtering. Numerical simulations are presented to showcase the effectiveness of the proposed methods.