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

TL;DR

An end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions and reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error is proposed.

Abstract

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.

Figures (6)

• Figure 1: System model.
• Figure 2: System architecture of the proposed SPOT scheme.
• Figure 3: Maximum received power of rainbow beam (dBm).
• Figure 4: Distance sensing performance of CBS approach. (a) Subcarrier index vs. distance mapping for a rainbow beam scanning from $(0^{\circ}, 5 \text{ m})$ to $(0^{\circ}, 300 \text{ m})$. (b) Distance RMSE.
• Figure 5: 2D RMSE under different user maximum distances.