# Beam Prediction based on Large Language Models

arXiv: https://arxiv.org/abs/2408.08707 · HTML: https://mlsanity.com/paper/2408.08707/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Semantic Communications using Foundation Models: Design Approaches and Open Issues](https://mlsanity.com/paper/2309.13315/) (2023)
- [Deep Learning Based Predictive Beamforming Design](https://mlsanity.com/paper/2302.01035/) (2023)
- [Towards Intelligent Communications: Large Model Empowered Semantic Communications](https://mlsanity.com/paper/2402.13073/) (2024)
- [Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information](https://mlsanity.com/paper/2011.02332/) (2020)

## Built on by (most cited first, 20 of 20)

- [A Wireless Foundation Model for Multi-Task Prediction](https://mlsanity.com/paper/2507.05938/) (2025)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [Large AI Models for Wireless Physical Layer](https://mlsanity.com/paper/2508.02314/) (2025)
- [BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2503.10432/) (2025)
- [Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks](https://mlsanity.com/paper/2503.01116/) (2025)
- [Edge Large AI Models: Revolutionizing 6G Networks](https://mlsanity.com/paper/2505.00321/) (2025)
- [Wireless Power Control Based on Large Language Models](https://mlsanity.com/paper/2603.00474/) (2026)
- [TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning](https://mlsanity.com/paper/2505.13831/) (2025)
- [Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images](https://mlsanity.com/paper/2506.19893/) (2025)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [Investigating cybersecurity incidents using large language models in latest-generation wireless networks](https://mlsanity.com/paper/2504.13196/) (2025)
- [RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning](https://mlsanity.com/paper/2509.12089/) (2025)
- [Agentic Link Construction for Environment and Intent Aware 6G Communication](https://mlsanity.com/paper/2511.05094/) (2025)
- [Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks](https://mlsanity.com/paper/2511.21707/) (2025)
- [LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation](https://mlsanity.com/paper/2512.08955/) (2025)
- [Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks](https://mlsanity.com/paper/2603.11392/) (2026)
- [Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction](https://mlsanity.com/paper/2603.16143/) (2026)
- [SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction](https://mlsanity.com/paper/2606.00120/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

In this letter, we use large language models (LLMs) to develop a high-performing and robust beam prediction method. We formulate the millimeter wave (mmWave) beam prediction problem as a time series forecasting task, where the historical observations are aggregated through cross-variable attention and then transformed into text-based representations using a trainable tokenizer. By leveraging the prompt-as-prefix (PaP) technique for contextual enrichment, our method harnesses the power of LLMs to predict future optimal beams. Simulation results demonstrate that our LLM-based approach outperforms traditional learning-based models in prediction accuracy as well as robustness, highlighting the significant potential of LLMs in enhancing wireless communication systems.