# RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting

arXiv: https://arxiv.org/abs/2608.07444 · HTML: https://mlsanity.com/paper/2608.07444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds](https://mlsanity.com/paper/2009.02818/) (2020)
- [Near-Field Communications: A Comprehensive Survey](https://mlsanity.com/paper/2401.05900/) (2024)
- [Joint Beam Training and Positioning For Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications](https://mlsanity.com/paper/2009.03536/) (2020)
- [MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization](https://mlsanity.com/paper/2011.09323/) (2020)
- [Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band](https://mlsanity.com/paper/2101.08607/) (2021)
- [On the Limits of Single Anchor Localization: Near-Field vs Far-Field](https://mlsanity.com/paper/2307.00078/) (2023)

## Abstract (arXiv)

Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable. This paper proposes a beam-domain fingerprint framework that maps the received signal-to-noise ratio (SNR) across a small set of predefined RIS reflection states to the UE azimuth angle and range, without requiring channel state information (CSI). Crucially, we extend the framework to a realistic interference-impaired scenario in which a nearby cross-link interferer (CLI) corrupts the clean SNR fingerprint, yielding a signal-to-interference-plus-noise ratio (SINR) fingerprint; an interference-to-noise ratio (INR)-constrained calibration strategy keeps the interference level physically interpretable. Four machine-learning (ML) regressors are evaluated under both conditions. Simulation results at 28 GHz with a 20x20 RIS show that k-nearest neighbors (KNN) achieves the lowest angle MAE of 0.37 degrees and range MAE of 4 cm under clean conditions, rising to 1.4 degrees and 7.6 cm under interference. A key finding is that interference degrades angle estimation substantially more than range estimation across all models, a consequence of the asymmetric encoding of location information in the beam-domain fingerprint.