# Telecom Foundation Models: Applications, Challenges, and Future Trends

arXiv: https://arxiv.org/abs/2408.03964 · HTML: https://mlsanity.com/paper/2408.03964/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory](https://mlsanity.com/paper/2207.01843/) (2022)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks](https://mlsanity.com/paper/2208.14380/) (2022)
- [Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network](https://mlsanity.com/paper/2406.04779/) (2024)
- [Test Code Generation for Telecom Software Systems using Two-Stage Generative Model](https://mlsanity.com/paper/2404.09249/) (2024)

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

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting](https://mlsanity.com/paper/2511.00651/) (2025)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

Telecom networks are becoming increasingly complex, with diversified deployment scenarios, multi-standards, and multi-vendor support. The intricate nature of the telecom network ecosystem presents challenges to effectively manage, operate, and optimize networks. To address these hurdles, Artificial Intelligence (AI) has been widely adopted to solve different tasks in telecom networks. However, these conventional AI models are often designed for specific tasks, rely on extensive and costly-to-collect labeled data that require specialized telecom expertise for development and maintenance. The AI models usually fail to generalize and support diverse deployment scenarios and applications. In contrast, Foundation Models (FMs) show effective generalization capabilities in various domains in language, vision, and decision-making tasks. FMs can be trained on multiple data modalities generated from the telecom ecosystem and leverage specialized domain knowledge. Moreover, FMs can be fine-tuned to solve numerous specialized tasks with minimal task-specific labeled data and, in some instances, are able to leverage context to solve previously unseen problems. At the dawn of 6G, this paper investigates the potential opportunities of using FMs to shape the future of telecom technologies and standards. In particular, the paper outlines a conceptual process for developing Telecom FMs (TFMs) and discusses emerging opportunities for orchestrating specialized TFMs for network configuration, operation, and maintenance. Finally, the paper discusses the limitations and challenges of developing and deploying TFMs.