发表机构
School of Communication and Information Engineering, Shanghai University; Xi’an Jiaotong-Liverpool University（上海大学通信与信息工程学院; 西交利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对信道基础模型评估缺乏统一基准的问题，发布CFM-Bench，涵盖多种信道配置、官方分区，规定数据使用规则，组织六个任务组，为比较信道表示跨模型、域和任务的可迁移性提供通用平台。
AI 中文摘要
信道基础模型（CFMs）发展迅速，近期研究表明预训练对下游无线任务有益。然而，CFMs通常在特定模型的管道中进行评估，存在数据、无线电配置、分区、适应程序、任务定义和指标等方面的差异。本文发布了CFM-Bench，这是一个统一的多域、多任务基准测试，旨在解决这一差距。它精心策划了六种信道配置，涵盖3GPP统计模拟、两个独立的射线追踪管道、工业和航空测量以及同步车辆多模态模拟。官方分区隔离了完整的轨迹、测量会话、车辆链路、模拟实现或缓冲的空间区域。CFM-Bench不规定外部预训练语料库或策略，任何基准测试分割都不得用于基础模型预训练，官方训练分割专门保留用于下游微调。此外，该基准测试要求披露模型开发过程中使用的所有数据，并禁止在训练阶段使用官方测试单元。六个任务组沿着三个CFM应用维度组织：物理层（PHY）信道智能、无线接入网络（RAN）决策智能和集成传感与通信（ISAC）。它们涵盖了CSI反馈、频率和时间信道外推、传播状态分类、当前和未来波束预测以及单帧和时间定位。CFM-Bench为比较跨模型、域和任务的信道表示的可迁移性提供了一个通用平台。
英文摘要
Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptation procedures, task definitions, and metrics, preventing reproducible comparison across CFMs and against task-specific networks. We release CFM-Bench, a unified multi-domain, multi-task benchmark comprising 157,900 official single-frame examples from six domains spanning 3GPP statistical simulation, two ray-tracing pipelines, terrestrial and aerial measurements, and synchronized vehicular multimodal simulation. Source-specific interfaces preserve complex channel state information (CSI) and the physical metadata available in each domain while allowing documented model-specific preprocessing. To reduce spatio-temporal leakage, official partitions isolate complete trajectories, measurement sessions, flights, vehicle links, simulation realizations, or buffered spatial regions. CFM-Bench excludes all benchmark splits from foundation-model pretraining, reserves the official training split for downstream fine-tuning, and reports the data used during model development. Six task groups across PHY, RAN, and ISAC applications cover CSI feedback, frequency and temporal channel extrapolation, propagation-state classification, current- and future-beam prediction, and single-frame and temporal localization. Representative experiments on CSI feedback, channel extrapolation, current-beam prediction, and wireless positioning provide reproducible reference results for pretrained channel-prediction models and task-specific neural networks. These results show that relative model performance can vary across data domains, highlighting the importance of using common data partitions, task definitions, and evaluation metrics. CFM-Bench provides a common substrate for evaluating the transferability of channel representations across models, domains, and tasks.
CommentsCFM-Bench dataset download: https://www.chaspark.com/\#/s/CFM-Bench
