---
title: "CFM-Bench：跨域多任务信道基础模型统一基准"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.14975v1"
permalink: "https://feed.astralor.com/items/itm_bef843e461bfc4dbe18c6c5c"
published: "2026-07-16T13:29:02.000Z"
score: 81
channel: "ai"
tags: ["CFM-Bench","信道基础模型","基准测试","无线通信","通信AI"]
---

# CFM-Bench：跨域多任务信道基础模型统一基准

## AI 点评

> 这篇论文对从事无线通信与AI交叉研究的团队至关重要。它直面行业痛点——不同模型间无法公平比较，且缺乏统一的任务和数据规范。CFM-Bench不仅提供标准化流程，还严格限制预训练数据使用，避免作弊风险。如果你在研究信道表示或开发通信基础模型，这个基准将是验证和对比你模型效果的首选工具。

## 速览

CFM-Bench是一个用于信道基础模型（CFM）的统一基准，解决当前CFM评估中管道碎片化、无法公平比较的问题。它整合了六种信道配置，覆盖3GPP统计仿真、射线追踪、工业及空域测量、车载多模态仿真等。基准禁止使用外部预训练语料，训练集仅用于下游微调。包含六大任务组，覆盖物理层信道智能、RAN决策智能及通感一体化。旨在提供跨模型、域和任务的传输性比较基础。

## 关键点

- 整合六种信道配置，包括统计仿真、射线追踪、实测与多模态仿真
- 禁止使用外部预训练语料，训练集仅用于下游微调
- 覆盖物理层、RAN决策与通感一体化三大应用维度
- 提供跨模型、域与任务的传输性公平比较框架

## 全文

Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks. Yet CFMs are commonly evaluated in model-specific pipelines with different data, radio configurations, partitions, adaptation procedures, task definitions, and metrics. Reported comparisons therefore tend to show that pretraining improves over supervised training from scratch within one pipeline, but neither rank CFMs nor compare them fairly with task-specific models. We release CFM-Bench, a unified multi-domain, multi-task benchmark designed to address this gap. It curates six channel configurations spanning 3GPP statistical simulation, two independent ray-tracing pipelines, industrial and aerial measurements, and synchronized vehicular multimodal simulation. Official partitions isolate complete trajectories, measurement sessions, vehicle links, simulation realizations, or buffered spatial regions. CFM-Bench does not prescribe an external pretraining corpus or strategy; no benchmark split may be used for foundation-model pretraining, and the official training split is reserved exclusively for downstream fine-tuning. The benchmark additionally requires disclosure of all data used during model development and prohibits training-stage use of official test units. Six task groups are organized along three CFM application dimensions: physical-layer (PHY) channel intelligence, radio-access-network (RAN) decision intelligence, and integrated sensing and communication (ISAC). They cover CSI feedback, frequency and temporal channel extrapolation, propagation-state classification, current- and future-beam prediction, and single-frame and temporal localization. CFM-Bench provides a common substrate for comparing the transferability of channel representations across models, domains, and tasks.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_bef843e461bfc4dbe18c6c5c