AI 中文总结
该研究提出无线基础模型Channel2World，用Transformer编码器聚合多环境MIMO信道数据，预训练后作为环境条件模块，在UE定位等任务中能有效适配未见环境，性能优于或媲美特定站点微调。
AI 中文摘要
无线信道通常被视为与链路相关的观测值，尽管它们的多径结构由周围的射频（RF）环境决定。本文提出Channel2World，一种无线基础模型，其从多输入多输出（MIMO）信道-位置观测值中学习可复用的环境级表征。该模型使用基于Transformer的编码器，将同一基站为中心的环境内收集的信道聚合为无线世界嵌入。编码器通过上下文-查询预测进行预训练，其中上下文信道对用户设备（UE）位置和不相交查询信道的相对路径增益预测进行条件设置。预训练后，编码器被冻结，作为任务不可知的环境条件模块用于下游无线模型，无需针对特定站点微调即可适配未见环境。为学习可跨部署泛化的环境级潜在空间，我们使用来自26000个环境的射线追踪数据预训练Channel2World，每个环境约有5000个信道测量值。在UE定位、波束域信道状态信息（CSI）重建和射频可观测几何重建上的评估表明，学习到的嵌入在未见环境中提供了有效的条件设置。对于定位和CSI重建任务，基于嵌入的条件设置优于或与特定站点微调相当，尽管微调需要特定任务的标记数据和额外的基于梯度的适配。这些嵌入还支持主导反射器结构的重建，表明它们可作为跨任务的可复用环境先验。
英文摘要
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.
Comments13 pages, 10 figures
