用大模型预测无线信道，支持任意步长历史数据。
Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training
- 将信道状态信息建模为可变步长序列，突破固定时间间隔限制。
- 在多种场景下稳定提升预测性能，支持连续多步预测。
- 借鉴自然语言模型训练方式，实现无线数据与大模型的跨模态对齐。
下行链路信道时序预测是大规模多输入多输出（MIMO）系统中的关键技术。然而，现有依赖固定步长历史序列的方法严重制约了信道预测的精度、实用性与可扩展性。最近的研究表明，大语言模型（LLMs）在复杂序列上展现出强大的模式识别与推理能力。其挑战在于如何有效将无线通信数据与自然语言处理所用模态对齐，以充分发挥这些能力。本文提出Csi-LLM，一种基于大语言模型的新型下行链路信道预测方法，能够建模可变步长的历史序列。为实现有效的跨模态应用，我们设计并训练Csi-LLM时，使其与自然语言任务的处理方式保持一致，利用大语言模型的下一个词生成能力来预测信道状态信息（CSI）的下一步。仿真结果表明，这种对齐策略具有显著效果，Csi-LLM在各种场景下均表现出稳定的性能提升，展现出在连续多步预测中的巨大潜力。
原文摘要 · Abstract (English)
Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality, and scalability of channel prediction. Recent advances have shown that large language models (LLMs) exhibit strong pattern recognition and reasoning abilities over complex sequences. The challenge lies in effectively aligning wireless communication data with the modalities used in natural language processing to fully harness these capabilities. In this work, we introduce Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences. To ensure effective cross-modality application, we align the design and training of Csi-LLM with the processing of natural language tasks, leveraging the LLM's next-token generation capability for predicting the next step in channel state information (CSI). Simulation results demonstrate the effectiveness of this alignment strategy, with Csi-LLM consistently delivering stable performance improvements across various scenarios and showing significant potential in continuous multi-step prediction.
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