研究提出在Radio Foundation Model预训练中加入信道配置信息
Beyond Channel Data: Configuration-Enriched Radio Foundation Models
一篇无线通信基础模型的研究：把信道配置信息放进预训练，在 LoS 分类和 beam selection 等任务上，小样本场景效果比基线更好。
该论文提出将信道配置（CFG）信息融入 Radio Foundation Model（RFM）的预训练过程。实验在 LoS/NLoS 分类、timing advance（TA）估计、beam selection 三类下游任务上进行，并覆盖三种数据量场景。评测同时包含同分布（in-distribution）和跨配置（out-of-distribution）两种设置。结果显示 CFG 增强嵌入在标签稀缺条件下相比原始信道 token 和多个基线模型表现更优或相当。
Beyond Channel Data: Configuration-Enriched Radio Foundation Models
This paper proposes incorporating channel configuration (CFG) information into Radio Foundation Model (RFM) pre-training and demonstrates that the resulting CFG-enriched embeddings serve as effective inductive priors for downstream tasks under label scarcity. We compare the proposed embeddings with raw channel tokens and multiple baseline models, under three different data regimes, for multiple downstream tasks --- Line-of-Sight/Non-Line-of-Sight (LoS/NLoS) classification, timing advance (TA) estimation, and beam selection. Evaluation is done in both in-distribution and out-of-distribution configuration settings. Experimental outcomes show that the proposed embeddings consistently outperform and/or are competitive to the baselines, across experiments in both seen and unseen configuration scenarios.