Contributors: Aalto University, Beijing Institute Of Technology, Brunel University London, Bubbleran, Cea-Leti, Central South University, Centralesupélec, University Of Paris Saclay, Centre Tecnològic De Telecomunicacions De Catalunya, China Mobile Communications Corporation, China Telecom, China Unicom, East China Normal University, Emirates Integrated Telecommunications Company (Du), Ericsson, Eurecom, Fentech, Gsma, Huawei, Imec – Ghent University, Itu, Katim, Khalifa University, King’s College London, Korea University, Kth Royal Institute Of Technology, Lighton, Nanjing University, Nanyang Technological University, Nokia Bell Labs, Northeastern University, Northwestern Polytechnical University, Nvidia, Orange, Qualcomm, Rimedo Labs, Singapore University Of Technology And Design, Sun Yat-Sen University, Technology Innovation Institute, Ulsan National Institute Of Science And Technology, Universitat Pompeu Fabra, University Of Electronic Science And Technology Of China, University Of Granada, University Of Hong Kong, University Of Houston, University Of Leeds, University Of Michigan, University Of Oulu, University Of York, Virginia Tech, Xidian University, Yale University, Zhejiang University
Executive Summary
The rise of generative artificial intelligence (AI) as a novel frontier that uniquely merges advanced levels of intelligence with revolutionary user experiences is redefining the AI landscape for future cellular networks. In particular, the transition towards 6G systems has introduced a myriad of challenges inherent to their AI-native network design, requiring innovative solutions to enable real-time network orchestration, intelligent decision-making, and adaptive dynamic configurations. Meanwhile, the envisioned user experiences for 6G are growing increasingly complex, exceeding the capabilities offered by vintage wireless technologies and conventional AI solutions to satisfy their advanced demands.
With its disruptive impact evident across diverse fields, generative AI possesses immense potential to tackle these challenges, leveraging its exceptional capabilities to manage complex tasks, operate autonomously, and adapt seamlessly to scenarios beyond its training domain. Remarkably, generative AI provides a transformative opportunity for telecom and cellular networks to bridge this defined gap in 6G systems, thereby shifting towards a new era with cutting-edge AI innovations across the different system and user levels.
In essence, the introduction of generative AI into the telecom domain is primarily facilitated by a set of large-scale AI models denoted as large telecom models (LTMs). These LTMs are specifically designed to tailor the abilities of large scale AI models to effectively meet the demands of the telecom ecosystem. The goal of this white paper is to shed light on the potential of LTMs to revolutionize the telecom functions and applications from the theoretical design, implementation, and deployment perspectives, while touching on the regulatory, standardization, and industrial frameworks that govern their realization in practice. To this end, this white paper provides an explanatory overview of LTMs and their distinctive role in the radio access network (RAN) and core network, while expanding the discussion to cover several key areas that include:
Fundamentals of large-scale AI: Reflecting on the generative architectures and models that compose large-scale AI, along with recent trends in handling multi- modal training data, pre-training and fine-tuning techniques, alignment techniques (e.g., reinforcement learning (RL) with human feedback), and deployment strategies on the network.
From large-scale AI models to LTMs: Moving beyond the state-of-the-art large scale AI models that can be vulnerable in the telecom domain, while highlighting the necessary modifications to the underlying theory of large-scale AI models to foresee the emergence of LTMs.
LTMs for physical and MAC layer designs: Addressing resource allocation, spectrum management, channel modeling, and mobility management, among others.
LTMs for network management and optimization: Spanning adaptive monitoring and control in emerging frameworks such as Open RAN networks (i.e., O-RAN), while highlighting the critical role of leveraging LTMs with RL to enable user- centric network optimization.
Datasets for LTMs: Supporting the deployment of LTMs with telecom-specific datasets and providing benchmarks with evaluation frameworks to assess the performance of LTMs.
Hardware advancements and requirements for LTMs: Focusing on the role of high computing platforms to accelerate the deployment of LTMs and how the convergence of the RAN with AI plays a role in enabling LTMs over future cellular networks.
New use cases and applications of LTMs: Encompassing distributed LTM frameworks over the edge, novel approaches for federated learning in LTMs, RL with LTMs interaction, intent-based management with LTMs, etc.
Regulatory and ethical considerations for LTMs: Emphasizing that data governance and accountability are crucial considerations to acquire trustworthy LTM operations.
Industry insights into large-scale AI models and LTMs: Including the current trends and ongoing projects in the industry that include large action models and on- device generative AI models, recent model breakthroughs such as TelecomGPT, and practical challenges that face LTMs such as the limited decoding rate and massive model sizes.
Standardization activities and LTM roadmap: Discussing the key efforts to bring forth LTMs through focus groups within regional bodies, while setting the roadmap for LTMs by defining their roles in network infrastructure, network management, business operations with the corresponding timeline for LTMs to reach their milestones.
Ultimately, this white paper serves as an inaugural roadmap for LTMs in networks and provides a basis for telecom experts and industry professionals to build on the state-of-art in LTMs to push the boundaries of large-scale AI models for next- generation wireless networks.
