Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

2025/03/06 by Adnan Shahid, Shahid, Adnan, Adrian Kliks +229 · 14 citations
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Context (archaeology) #FOS: Computer and information sciences #Focus (optics) #Key (lock) #Networking and Internet Architecture (cs.NI) #Resource (disambiguation) #Software deployment #Software-Defined Networks and 5G #Standardization #Telecommunications network #White paper

paper · pdf · doi:10.48550/arxiv.2503.04184

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

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• Sovereign AI for 6G: Towards the Future of AI-Native Networks
• Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
• DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
• MX-AI: Agentic Observability and Control Platform for Open and AI-RAN
• Agentic AI in 6G Software Businesses: A Layered Maturity Model
• Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation
• Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
• Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations
• LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling
• Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation
• A Conformal Predictive Measure for Assessing Catastrophic Forgetting

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