# Large Language Models for Wireless Communications: From Adaptation to Autonomy

arXiv: https://arxiv.org/abs/2507.21524 · HTML: https://mlsanity.com/paper/2507.21524/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication](https://mlsanity.com/paper/2502.06877/) (2025)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [WiFo: Wireless Foundation Model for Channel Prediction](https://mlsanity.com/paper/2412.08908/) (2024)
- [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2505.01074/) (2025)
- [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)
- [A Wireless Foundation Model for Multi-Task Prediction](https://mlsanity.com/paper/2507.05938/) (2025)
- [Rethinking Generative Semantic Communication for Multi-User Systems with Large Language Models](https://mlsanity.com/paper/2408.08765/) (2024)

## Built on by (most cited first, 11 of 11)

- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [Learning Multi-Access Point Coordination in Agentic AI Wi-Fi with Large Language Models](https://mlsanity.com/paper/2511.20719/) (2025)
- [Wireless Power Control Based on Large Language Models](https://mlsanity.com/paper/2603.00474/) (2026)
- [A Graph Foundation Model for Wireless Resource Allocation](https://mlsanity.com/paper/2604.07390/) (2026)
- [Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models](https://mlsanity.com/paper/2606.10277/) (2026)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)
- [LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach](https://mlsanity.com/paper/2510.10895/) (2025)
- [SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction](https://mlsanity.com/paper/2606.00120/) (2026)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)

## Abstract (arXiv)

The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning. These strengths open new frontiers in wireless communications, where increasing complexity and dynamics demand intelligent and adaptive solutions. This article explores the role of LLMs in transforming wireless systems across three key directions: adapting pretrained LLMs for communication tasks, developing wireless-specific foundation models to balance versatility and efficiency, and enabling agentic LLMs with autonomous reasoning and coordination capabilities. We highlight recent advances, practical case studies, and the unique benefits of LLM-based approaches over traditional methods. Finally, we outline open challenges and research opportunities, including multimodal fusion, collaboration with lightweight models, and self-improving capabilities, charting a path toward intelligent, adaptive, and autonomous wireless networks.