# Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training

arXiv: https://arxiv.org/abs/2409.00005 · HTML: https://mlsanity.com/paper/2409.00005/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions](https://mlsanity.com/paper/1912.11330/) (2019)
- [Ten issues of NetGPT](https://mlsanity.com/paper/2311.13106/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks](https://mlsanity.com/paper/2503.01116/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction](https://mlsanity.com/paper/2604.21957/) (2026)
- [A Short Overview of Multi-Modal Wi-Fi Sensing](https://mlsanity.com/paper/2505.06682/) (2025)
- [CSI Prediction Using Diffusion Models](https://mlsanity.com/paper/2510.11214/) (2025)

## Abstract (arXiv)

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.