Environment-Aware Generalized Wireless Localization via Transformer

Abstract

Wireless localization is expected to play a key role in future communication systems by providing location-aware services and supporting efficient network operation. However, existing deep learning (DL)-based localization methods often suffer from limited generalization when the deployment environment changes, since they tend to learn environment-specific propagation patterns. To address this issue, this article proposes an environment-aware generalized wireless localization framework that jointly exploits wireless channel, base station (BS) geometric information, and environmental information. Irregular city structures are represented by voxel-based occupancy maps, enabling explicit modeling of environmental factors that affect radio propagation. A transformer-based architecture is developed to comprehensively process wireless channel, geometric information of network nodes, and environmental information, thereby capturing the interaction between channel observations and surrounding urban structures. In addition, the proposed framework estimates a confidence map instead of directly regressing user equipment (UE) coordinates, which improves robustness under ambiguous propagation conditions. To support training and evaluation, we also develop an urban environment generator and a ray tracing-based channel simulator that produce large-scale datasets with physically consistent alignment between channels and 3-D environments. This framework enables systematic evaluation and robust localization in previously unseen urban environments.