# SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming

arXiv: https://arxiv.org/abs/2511.11391 · HTML: https://mlsanity.com/paper/2511.11391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Beam Squint Assisted User Localization in Near-Field Integrated Sensing and Communications Systems](https://mlsanity.com/paper/2309.14012/) (2023)
- [Joint Phase-Time Arrays: A Paradigm for Frequency-Dependent Analog Beamforming in 6G](https://mlsanity.com/paper/2312.11682/) (2023)
- [Near-Field Wideband Beam Training Based on Distance-Dependent Beam Split](https://mlsanity.com/paper/2406.07989/) (2024)
- [Hybrid Near/Far-Field Frequency-Dependent Beamforming via Joint Phase-Time Arrays](https://mlsanity.com/paper/2501.15207/) (2025)
- [Beamforming with Joint Phase and Time Array: System Design, Prototyping and Performance](https://mlsanity.com/paper/2502.00139/) (2025)

## Abstract (arXiv)

Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This paper proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating the PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error.