Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach

TL;DR

This paper presents a novel high-rank tensor method for estimating complex sub-6G MU-MIMO channels with hundreds of sub-paths, leveraging physical channel structures for improved accuracy and efficiency.

Contribution

It introduces a structured high-rank tensor approach with Vandermonde constraints, enhancing channel estimation accuracy and providing a stronger uniqueness property compared to existing methods.

Findings

Outperforms state-of-the-art tensor techniques in simulations

Supports efficient one-pass sub-path parameter estimation

Ensures compatibility with existing baseline algorithms

Abstract

This letter introduces a structured high-rank tensor approach for estimating sub-6G uplink channels in multi-user multiple-input and multiple-output (MU-MIMO) systems. To tackle the difficulty of channel estimation in sub-6G bands with hundreds of sub-paths, our approach fully exploits the physical structure of channel and establishes the link between sub-6G channel model and a high-rank four-dimensional (4D) tensor Canonical Polyadic Decomposition (CPD) with three factor matrices being Vandermonde-constrained. Accordingly, a stronger uniqueness property is derived in this work. This model supports an efficient one-pass algorithm for estimating sub-path parameters, which ensures plug-in compatibility with the widely-used baseline. Our method performs much better than the state-of-the-art tensor-based techniques on the simulations adhering to the 3GPP 5G protocols.