LLM4FB:通过大型语言模型为轻量级用户提供单边的CSI反和预测框架.
Xinxin Xie1, Xinyu Ning1, Yitong Liu1
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了LLM4FB,这是一个用于大规模MIMO系统中高效通道状态信息 (CSI) 反的新框架. 使用大型语言模型 (LLM),LLM4FB显著降低了用户设备上的反开销和计算负载.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 人工智能的人工智能是人工智能.
背景情况:
- 大规模的MIMO提高了光谱效率,但需要准确的通道状态信息 (CSI).
- 高的天线计数增加了反开销,当前的深度学习方法会在计算上负担用户设备 (UE).
- 现有的CSI反方法在效率和UE资源限制方面扎.
研究的目的:
- 提出LLM4FB,一个新的单边CSI反框架,使用预先训练的大型语言模型 (LLM).
- 为了减少大规模MIMO系统中UE的反开销和计算复杂性.
- 以最小的UE资源提高CSI重建精度和光谱效率.
主要方法:
- UE使用低复杂度的线性投影来进行CSI压缩.
- 在基站 (BS) 预先训练的LLM重建和预测CSI.
- 一个多目标损失函数优化了规范平均平方误差 (NMSE) 和光谱效率 (SE).
主要成果:
- 在各种压缩比和移动级别中,LLM4FB实现了高精度的CSI反.
- 该框架的性能优于现有的CSI反方法.
- 需要对LLM参数进行最小的微调,从而降低培训成本.
结论:
- 在资源有限的环境中,LLM4FB为CSI反提供了一个有希望的解决方案.
- 该框架适用于下一代无线传感器网络和工业物联网.
- LLM4FB有效地解决了反开销和UE计算负担的挑战.
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