Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations

TL;DR

This paper introduces SCADF-MoE, a dynamic multimodal fusion network using mixture-of-experts for improved sub-meter localization accuracy in 6G ISAC, effectively handling varying conditions and NLOS scenarios.

Contribution

The paper presents the first large-scale multimodal MoE approach for frequency-robust ISAC localization, combining spatial context clustering and adaptive fusion with a novel MoE routing mechanism.

Findings

Achieves consistent sub-meter MSE across three urban layouts and carrier bands.

Halves localization error in NLOS scenarios compared to prior methods.

Demonstrates robustness across multiple frequencies and environments.

Abstract

Multimodal fingerprinting is a crucial technique to sub-meter 6G integrated sensing and communications (ISAC) localization, but two hurdles block deployment: (i) the contribution each modality makes to the target position varies with the operating conditions such as carrier frequency, and (ii) spatial and fingerprint ambiguities markedly undermine localization accuracy, especially in non-line-of-sight (NLOS) scenarios. To solve these problems, we introduce SCADF-MoE, a spatial-context aware dynamic fusion network built on a soft mixture-of-experts backbone. SCADF-MoE first clusters neighboring points into short trajectories to inject explicit spatial context. Then, it adaptively fuses channel state information, angle of arrival profile, distance, and gain through its learnable MoE router, so that the most reliable cues dominate at each carrier band. The fused representation is fed to a modality-task MoE that simultaneously regresses the coordinates of every vertex in the trajectory and its centroid, thereby exploiting inter-point correlations. Finally, an auxiliary maximum-mean-discrepancy loss enforces expert diversity and mitigates gradient interference, stabilizing multi-task training. On three real urban layouts and three carrier bands (2.6, 6, 28 GHz), the model delivers consistent sub-meter MSE and halves unseen-NLOS error versus the best prior work. To our knowledge, this is the first work that leverages large-scale multimodal MoE for frequency-robust ISAC localization.