Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
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Abstract (arXiv)
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2025.
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Citing papers per year: 2025: 8; 2026: 4.
12 papers.
- 2026RF-GPT: Teaching AI to See the Wireless Worldcited by 6
- 2025MX-AI: Agentic Observability and Control Platform for Open and AI-RANcited by 4
- 2025Agentic AI in 6G Software Businesses: A Layered Maturity Modelcited by 3
- 2025LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modelingcited by 1
- 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Designcited by 1
- 2025Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Modelscited by 1
- 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?cited by 1
- 2026Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6Gcited by 1
- 2025A Conformal Predictive Measure for Assessing Catastrophic Forgettingcited by 0
- 2025Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillationcited by 0
- 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0
- 2026Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judgecited by 0
- 2026Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge21 Aug 2026
- 2026Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G20 May 2026
- 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?18 May 2026
- 2026RF-GPT: Teaching AI to See the Wireless World16 Feb 2026
- 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization25 Nov 2025
- 2025Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models25 Oct 2025
- 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design26 Sep 2025
- 2025MX-AI: Agentic Observability and Control Platform for Open and AI-RAN8 Aug 2025
- 2025Agentic AI in 6G Software Businesses: A Layered Maturity Model5 Aug 2025
- 2025Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation4 Aug 2025
- 2025A Conformal Predictive Measure for Assessing Catastrophic Forgetting15 May 2025
- 2025LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling7 May 2025