Papers for

urban network operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Vision wireless fusion improves locating multiple users in cities

Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach

Abstract: Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual observations provide complementary spatial context. This paper presents a vision-wireless fusion framework for multi-user localization using pilot-indexed channel state information (CSI). Orthogonal pilot indices preserve the identities of the communicating UEs in the CSI token sequence and localization outputs. The model encodes each pilot-indexed CSI observation as a query token and uses cross-attention to retrieve user-specific information from spatial visual memory. Self-attention among CSI tokens further captures inter-user interactions, while the resulting multimodal representations are used for user-wise localization. Experiments on different datasets show consistent improvements over model-based, CSI-only, and multimodal-fusion baselines. Further experiments evaluate the model under different wireless and visual conditions.

Sun 20 SeptComputer Vision and Pattern Recognition

The gist

Finding the positions of many people in busy city areas is hard because radio signals can get mixed up by buildings and noise. The authors created a new method that combines wireless signal data tagged with unique user codes and images of the environment. Their system matches each user's wireless signals with visual clues using a special transformer design, allowing better guessing of where each person is. Tests showed this combined approach works better than using signals or images alone.

Open → 2609.23372v1