Beyond MMSE: Rank-1 Subspace Channel Estimator for Massive MIMO Systems
Bin Li, Ziping Wei, Shaoshi Yang, Yang Zhang, Jun Zhang, Chenglin, Zhao, Sheng Chen

TL;DR
This paper introduces a low-complexity rank-1 subspace channel estimator for massive MIMO systems that outperforms traditional MMSE estimators in accuracy while maintaining scalable computational efficiency.
Contribution
A novel rank-1 subspace estimator approximates ML estimation with lower complexity, leveraging AoA information and post-reception beamforming.
Findings
Outperforms linear MMSE estimator in accuracy.
Complexity scales linearly with the number of antennas.
Theoretical gain grows logarithmically with antenna number.
Abstract
To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts the post-reception beamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced MIMO Systems Optimization · Advanced Wireless Communication Techniques · Antenna Design and Optimization
