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

TL;DR

MARBLE-Net is a deep learning framework that adaptively learns to optimize rainbow beamforming for precise target localization in complex multipath environments, outperforming traditional methods.

Contribution

The paper introduces MARBLE-Net, a novel deep learning approach that jointly optimizes beamforming and localization in multipath-rich environments, leveraging environment-specific multipath characteristics.

Findings

Reduces localization error by over 50% compared to baselines.

Effectively exploits structured and directional multipath for improved accuracy.

Degrades gracefully in the presence of confined multipath.

Abstract

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.