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

arXiv: https://arxiv.org/abs/2409.00723 · HTML: https://mlsanity.com/paper/2409.00723/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication](https://mlsanity.com/paper/2503.21242/) (2025)

## Abstract (arXiv)

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.