# Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks

arXiv: https://arxiv.org/abs/2503.01116 · HTML: https://mlsanity.com/paper/2503.01116/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://mlsanity.com/paper/2402.02368/) (2024)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [6G Wireless Channel Measurements and Models: Trends and Challenges](https://mlsanity.com/paper/2012.06381/) (2020)
- [ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence](https://mlsanity.com/paper/2410.13379/) (2024)
- [Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate](https://mlsanity.com/paper/2403.19409/) (2024)
- [Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training](https://mlsanity.com/paper/2409.00005/) (2024)
- [Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search](https://mlsanity.com/paper/2201.12725/) (2022)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [New Delay Doppler Communication Paradigm in 6G era: A Survey of Orthogonal Time Frequency Space (OTFS)](https://mlsanity.com/paper/2211.12955/) (2022)
- [OTFS -- Predictability in the Delay-Doppler Domain and its Value to Communication and Radar Sensing](https://mlsanity.com/paper/2302.08705/) (2023)
- [From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain](https://mlsanity.com/paper/2311.15215/) (2023)
- [Multi-Carrier Modulation: An Evolution from Time-Frequency Domain to Delay-Doppler Domain](https://mlsanity.com/paper/2308.01802/) (2023)

## Built on by (most cited first, 2 of 2)

- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach](https://mlsanity.com/paper/2601.12311/) (2026)

## Abstract (arXiv)

Channel prediction is crucial for high-mobility vehicular networks, as it enables the anticipation of future channel conditions and the proactive adjustment of communication strategies. However, achieving accurate vehicular channel prediction is challenging due to significant Doppler effects and rapid channel variations resulting from high-speed vehicle movement and complex propagation environments. In this paper, we propose a novel delay-Doppler (DD) domain channel prediction framework tailored for high-mobility vehicular networks. By transforming the channel representation into the DD domain, we obtain an intuitive, sparse, and stable depiction that closely aligns with the underlying physical propagation processes, effectively reducing the complex vehicular channel to a set of time-series parameters with enhanced predictability. Furthermore, we leverage the large artificial intelligence (AI) model to predict these DD-domain time-series parameters, capitalizing on their advanced ability to model temporal correlations. The zero-shot capability of the pre-trained large AI model facilitates accurate channel predictions without requiring task-specific training, while subsequent fine-tuning on specific vehicular channel data further improves prediction accuracy. Extensive simulation results demonstrate the effectiveness of our DD-domain channel prediction framework and the superior accuracy of the large AI model in predicting time-series channel parameters, thereby highlighting the potential of our approach for robust vehicular communication systems.