---
id: 20260313-T0-27
title: "LWM-Temporal：基于稀疏时空注意力的无线信道表示学习"
title_en: "LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channels"
url: https://ai.daily.yangsir.net/daily/20260313-T0-27
issue_date: 2026-03-13
publish_date: 2026-03-12T04:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.10024
---

# LWM-Temporal：基于稀疏时空注意力的无线信道表示学习

LWM-Temporal作为大型无线模型家族新成员，采用稀疏时空注意力机制学习无线信道嵌入。该模型能捕捉移动性导致的信道动态变化，在5G信道预测任务中误差降低22%。

## English Version

**LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channels**

LWM-Temporal, a new Large Wireless Model, uses sparse spatio-temporal attention to learn wireless channel embeddings. It captures mobility-induced channel dynamics and reduces prediction error by 22% in 5G channel tasks.

---

**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.10024)

**详情页**：https://ai.daily.yangsir.net/daily/20260313-T0-27

---

*智语观潮 · Daily — https://ai.daily.yangsir.net/llms.txt*