MARBLE-Net: Learning to Localize in Multipath Environment with Adaptive Rainbow Beams

Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a frequency-dependent rainbow beam and a neural localization network for high-accuracy position estimation. By treating the phase-shifter (PS) and true-time-delay (TTD) parameters as learnable weights, the system adaptively refines its sensing beam to exploit environment-specific multipath characteristics. A structured multi-stage training strategy is proposed to ensure stable convergence and effective end-to-end optimization. Simulation results show that MARBLE-Net outperforms both a fixed-beam deep learning baseline (RaiNet) and a traditional k-nearest neighbors (k-NN) method, reducing localization error by more than 50% in a multipath-rich scene. Moreover, the results reveal a nuanced interaction with multipath propagation: while confined uni-directional multipath degrades accuracy, structured and directional multipath can be effectively exploited to achieve performance surpassing even line-of-sight (LoS) conditions.

💡 Research Summary

This paper tackles the challenging problem of high‑accuracy target localization in rich‑multipath environments, a key requirement for integrated sensing and communication (ISAC) systems envisioned for 6G. The authors propose MARBLE‑Net (Multipath‑Aware Rainbow Beam Learning Network), a fully differentiable end‑to‑end framework that jointly optimizes the analog beamforming of a frequency‑dependent “rainbow” beam and a deep neural network that regresses the 2‑D position of a single UAV from the received signal.

The physical layer employs a phase‑time array (PTA) consisting of conventional phase‑shifters (PS) and true‑time‑delay (TTD) elements. For each OFDM sub‑carrier m the beamforming weight is
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