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

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arxiv 2503.04184 v1 pith:TXEK74ZX submitted 2025-03-06 cs.NI cs.AIcs.CL

Adnan Shahid , Adrian Kliks , Ahmed Al-Tahmeesschi , Ahmed Elbakary , Alexandros Nikou , Ali Maatouk , Ali Mokh , Amirreza Kazemi
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Antonio De Domenico Athanasios Karapantelakis Bo Cheng Bo Yang Bohao Wang Carlo Fischione Chao Zhang Chaouki Ben Issaid Chau Yuen Chenghui Peng Chongwen Huang Christina Chaccour Christo Kurisummoottil Thomas Dheeraj Sharma Dimitris Kalogiros Dusit Niyato Eli De Poorter Elissa Mhanna Emilio Calvanese Strinati Faouzi Bader Fathi Abdeldayem Fei Wang Fenghao Zhu Gianluca Fontanesi Giovanni Geraci Haibo Zhou Hakimeh Purmehdi Hamed Ahmadi Hang Zou Hongyang Du Hoon Lee Howard H. Yang Iacopo Poli Igor Carron Ilias Chatzistefanidis Inkyu Lee Ioannis Pitsiorlas Jaron Fontaine Jiajun Wu Jie Zeng Jinan Li Jinane Karam Johny Gemayel Juan Deng Julien Frison Kaibin Huang Kehai Qiu Keith Ball Kezhi Wang Kun Guo Leandros Tassiulas Lecorve Gwenole Liexiang Yue Lina Bariah Louis Powell Marcin Dryjanski Maria Amparo Canaveras Galdon Marios Kountouris Maryam Hafeez Maxime Elkael Mehdi Bennis Mehdi Boudjelli Meiling Dai Merouane Debbah Michele Polese Mohamad Assaad Mohamed Benzaghta Mohammad Al Refai Moussab Djerrab Mubeen Syed Muhammad Amir Na Yan Najla Alkaabi Nan Li Nassim Sehad Navid Nikaein Omar Hashash Pawel Sroka Qianqian Yang Qiyang Zhao Rasoul Nikbakht Silab Rex Ying Roberto Morabito Rongpeng Li Ryad Madi Salah Eddine El Ayoubi Salvatore D'Oro Samson Lasaulce Serveh Shalmashi Sige Liu Sihem Cherrared Swarna Bindu Chetty Swastika Dutta Syed A. R. Zaidi Tianjiao Chen Timothy Murphy Tommaso Melodia Tony Q.S. Quek Vishnu Ram Walid Saad Wassim Hamidouche Weilong Chen Xiaoou Liu Xiaoxue Yu Xijun Wang Xingyu Shang Xinquan Wang Xuelin Cao Yang Su Yanping Liang Yansha Deng Yifan Yang Yingping Cui Yu Sun Yuxuan Chen Yvan Pointurier Zeinab Nehme Zeinab Nezami Zhaohui Yang Zhaoyang Zhang Zhe Liu Zhenyu Yang Zhu Han Zhuang Zhou Zihan Chen Zirui Chen Zitao Shuai
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classification cs.NIcs.AIcs.CL
keywords telecomltmsdeploymentexperiencesinnovationlarge-scalemodelsnetwork
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 6 Pith papers

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  1. DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications

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    DeepTelecom provides a multimodal LoD3 digital-twin channel dataset generated via LLM-assisted scene modeling and Sionna ray tracing.

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  3. Sovereign AI for 6G: Towards the Future of AI-Native Networks

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    Sovereign AI, defined as national or operator-level control over AI lifecycles, is proposed as a foundational pillar for 6G networks and mapped onto O-RAN's RIC architecture.

  4. Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations

    cs.IT 2025-07 conditional novelty 5.0 of 10

    A mixture-of-experts network that adaptively fuses wireless signal fingerprints across frequency bands and jointly localizes short trajectories achieves sub-meter errors on simulated urban 6G test cases.

  5. Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A survey and tutorial that organizes LLM-enabled wireless network optimization into formulation, solution, and verification stages, with case studies drawn from the authors' own prior papers.

  6. Agentic AI in 6G Software Businesses: A Layered Maturity Model

    cs.SE 2025-08 conditional novelty 4.0 of 10

    A preliminary thematic review distills 29 motivators and 27 demotivators for agentic AI adoption in 6G software businesses into five themes each and outlines a future maturity model.

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