SymNetPro is a localization framework that extends SymNet by adding a line‑of‑sight aware attention bias and a transmitter‑drop augmentation technique. These components help the model learn obstruction‑aware spatial relations and handle varying numbers of transmitters. Experiments on ray‑traced urban environments demonstrate lower OSPA errors compared to baseline methods, especially under sparse sampling, noise, and higher transmitter counts.
arXiv:2607. 15713v1 Announce Type: cross Abstract: Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing.
arXiv:2606. 11914v1 Announce Type: cross Abstract: CSI-based localization with spatially distributed antenna arrays exposes a basic resource trade-off.
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
The paper introduces a self‑localizing MIMO beam‑mapping framework that builds a hierarchical wireless memory using sparse channel state information (CSI) without explicit location labels. It employs beam‑domain RSS as compact inputs, a dual‑scale extractor for angular and temporal dependencies, and a hybrid temporal encoder to infer physical anchors that index a structured radio map. The radio‑map embedding enables continuous updates and full‑CSI reconstruction, yielding over 30% better anchor recovery and more than 20% channel‑capacity gains in NLOS beam tracking compared to Kalman‑filter methods.
arXiv:2609.23372v1 Announce Type: new Abstract: Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage,...
arXiv:2511. 17007v2 Announce Type: replace-cross Abstract: Open and intelligent radio access networks (RANs) envisioned for 6G require accurate and reusable wireless channel knowledge for intelligent inference and control.
arXiv:2607. 08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks.
arXiv:2608. 16167v1 Announce Type: cross Abstract: High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation.
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
arXiv:2607. 20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks.
The paper introduces GrayTrack, a vehicle‑tracking system that fuses weak, indirect observations from third‑party sensors with sparse direct sensor data using a road‑constrained particle filter. Experiments on a CARLA‑Mininet‑WiFi pipeline show that the system achieves an F1 score of 0.989 for anonymous vehicle passages and reduces trajectory RMSE by 60.1% while cutting catastrophic track loss from 35.8% to 0.3%. These results demonstrate that incorporating indirect third‑party observations can substantially extend tracking capabilities when direct sensor access is limited.