# Large AI Models for Wireless Physical Layer

arXiv: https://arxiv.org/abs/2508.02314 · HTML: https://mlsanity.com/paper/2508.02314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Large Wireless Model (LWM): A Foundation Model for Wireless Channels](https://mlsanity.com/paper/2411.08872/) (2024)
- [WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication](https://mlsanity.com/paper/2502.06877/) (2025)
- [Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities](https://mlsanity.com/paper/2412.14538/) (2024)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [Large Language Model Enabled Multi-Task Physical Layer Network](https://mlsanity.com/paper/2412.20772/) (2024)
- [BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction](https://mlsanity.com/paper/2501.01802/) (2025)
- [LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks](https://mlsanity.com/paper/2507.05121/) (2025)
- [Prompt-Enabled Large AI Models for CSI Feedback](https://mlsanity.com/paper/2501.10629/) (2025)
- [Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2501.10630/) (2025)
- [M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2506.14532/) (2025)

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

- [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)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models](https://mlsanity.com/paper/2606.01899/) (2026)
- [MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation](https://mlsanity.com/paper/2608.05076/) (2026)
- [Learnware for CSI Feedback: Scene-specific Small Models Can Do Big](https://mlsanity.com/paper/2608.17760/) (2026)

## Abstract (arXiv)

Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying LAMs to physical layer communications, addressing obstacles of conventional AI-based approaches. LAM-based solutions are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. The motivations and key frameworks of these approaches are comprehensively examined through multiple use cases. Both strategies significantly improve performance and adaptability across diverse wireless scenarios. Future research directions, including efficient architectures, interpretability, standardized datasets, and collaboration between large and small models, are proposed to advance LAM-based physical layer solutions for next-generation communication systems.