Structure-Aware Near-Field Radio Map Recovery via RBF-Assisted Matrix Completion
Hao Sun, Xianghao Yu, and Junting Chen

TL;DR
This paper introduces a structure-aware radio map recovery method combining RBF interpolation and matrix completion to improve near-field XL-MIMO signal modeling, achieving over 10% NMSE improvement.
Contribution
It develops a novel RBF-assisted matrix completion framework that captures near-field spatial variations and exploits low-rank structures for accurate radio map reconstruction.
Findings
Achieves over 10% NMSE improvement over standard methods.
Effectively models complex near-field wavefront variations.
Adaptive sampling enhances reconstruction accuracy.
Abstract
This paper proposes a novel structure-aware matrix completion framework assisted by radial basis function (RBF) interpolation for near-field radio map construction in extremely large multiple-input multiple-output (XL-MIMO) systems. Unlike the far-field scenario, near-field wavefronts exhibit strong dependencies on both angle and distance due to spherical wave propagation, leading to complicated variations in received signal strength (RSS). To effectively capture the intricate spatial variations structure inherent in near-field environments, a regularized RBF interpolation method is developed to enhance radio map reconstruction accuracy. Leveraging theoretical insights from interpolation error analysis of RBF, an inverse {\mu}-law-inspired nonuniform sampling strategy is introduced to allocate measurements adaptively, emphasizing regions with rapid RSS variations near the transmitter.…
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
TopicsElectromagnetic Compatibility and Measurements · Millimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization
