arXiv cs.AI· Shrihari Vasudevan, H. G. Ranjani, Raaghul Ranganathan, Satwik Sahu, Thirumaran Ekambaram·· 2 小时前AI 评分28
超越信道数据：融合配置信息的无线电基础模型（RFM）研究
Beyond Channel Data: Configuration-Enriched Radio Foundation Models
AI 导读
一项 arXiv 论文提出在无线电基础模型（RFM）预训练中引入信道配置（CFG）信息，所得嵌入可作为标签稀缺下下游任务的有效归纳先验。研究在 LoS/NLoS 分类、timing advance（TA）估计和波束选择三类任务、三种数据规模以及同分布与分布外配置设定下，与原始信道 token 及多个基线模型对比。结果显示该方法在已见和未见配置场景中均持续优于或持平于基线。
正文 · 原文
Abstract: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.
| Comments: | Submitted to IEEE ICC 2027 for review |
| Subjects: | Artificial Intelligence (cs.AI); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.32542 [cs.AI] |
| (or arXiv:2610.32542v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32542 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ranjani H. G. [view email]
[v1]
Fri, 23 Oct 2026 12:21:27 UTC (1,274 KB)
来源：arXiv cs.AI · arxiv.org