Channel2World: A Wireless Foundation Model for RF Environment Representation

Abstract

Wireless channels are commonly treated as link-specific observations, although their multipath structure is governed by the surrounding radio-frequency (RF) environment. In this paper, we propose Channel2World, a wireless foundation model that learns a reusable environment-level representation from multiple-input multiple-output (MIMO) channel-position observations. The model aggregates channels collected within the same base-station-centered environment into a wireless world embedding using a Transformer-based encoder. The encoder is pretrained through context-query prediction, where context channels condition user equipment (UE) position and relative path-gain prediction for disjoint query channels. After pretraining, the encoder is frozen and used as a task-agnostic environment-conditioning module for downstream wireless models, enabling adaptation to unseen environments without site-specific fine-tuning. To learn an environment-level latent space that generalizes across deployments, we pretrain Channel2World using ray-tracing data from 26,000 environments, with approximately 5,000 channel measurements per environment. Evaluations on UE localization, beam-domain channel state information (CSI) reconstruction, and RF-observable geometry reconstruction show that the learned embeddings provide effective conditioning in unseen environments. For localization and CSI reconstruction tasks, embedding-based conditioning outperforms or remains competitive with site-specific fine-tuning, although fine-tuning requires task-specific labeled data and additional gradient-based adaptation. The embeddings also support the reconstruction of dominant reflector structures, indicating their utility as reusable environmental priors across tasks.