The ITU vision document for IMT-2030 (6G) calls out ubiquitous intelligence as one of the overarching aspects commonly available to all usage scenarios of IMT-2030 (6G). AI-related capabilities such as distributed data processing, distributed learning, AI computing, AI model execution, and AI model inference, are to be supported throughout IMT-2030 (6G). This session analyses the applications in IMT-2030 but with a critical lens of requirements for AI-related capabilities in the network. How does artificial intelligence enable new usage scenarios in IMT-2030 (6G) which previous generations of IMT were not designed to support? How to use Generative AI to bring virtually generated or remote experiences closer to the user and at the same time bridge digital divides? Can AI help to achieve the twin goals of replicating the real world in a digital world and providing virtual experiences to humans while satisfying sustainability goals? The session chair would kick off the discussion and the experts would present their views on the topic, including important challenges such as the expectations on AI to enable new-age applications, AI-empowered features in 6G, and the current state of AI/ML. Discussions would focus on some of the important questions to answer such as the relation between application and services, AI, and networks, AI-enabled management of applications, and identify some of the major areas that need further study including current standards and gaps.
Gyu Myoung LeeProfessorLiverpool John Moores University (LJMU)
Jakob HoydisPrincipal Research ScientistNVIDIA
Jose M. Alcaraz CaleroFull Professor in Cognitive Networks and ServicesUniversity of the West of Scotland (UWS)
Thierry LestableChief ResearcherTechnology Innovation Institute (TII)
Zoran UtkovskiSenior ResearcherFraunhofer Heinrich Hertz Institute (HHI)
AI/ML overlays may not be the preferred approach in IMT-2030 (6G). ML pipeline-based integration of intelligence is often module-specific, focusing on specific components or tasks, which may or may not interact harmoniously with the rest of the system. Retrofitting AI capabilities into an existing framework leads to potential inefficiencies. This leads us to a system where AI pervades numerous functions, features, and user interactions, making it an integral part of the user experience. Hence AI Native seems to be an important technical enabler for IMT-2030 (6G) usage scenarios. Applications and services impose expectations on the network, such as intelligent placement of workloads, management of energy vs. performance, and proactive resilience, while assuring privacy, trust, and transparency. To satisfy these expectations, networks transform from providers of AI-as-Service to AI-Native platforms where application developers, integrators, and consumers can meet. Data is moved and crunched, at edge or core, AI algorithms are pulled and prompted, efficiently and on-demand, while applications and services are agnostic to these complexities. Discussions in this session will focus on some of the important considerations in AI-native design and network architectures that can host those designs. How would the creation, configuration, and management of network functions be done by the network itself? How can a network be the platform for the training and inference of models while enabling distributed edge nodes for inference? What does it take to host AI as a native service in the network?
Buse BilginNext Generation R&D EngineerTurkcell Technology- i
Speaker abstract
AI/ML integration in Wi-Fi: an overview; The unlicensed spectrum is (and will continue to be) an essential part of wireless communications, as it provides ease of access and great flexibility to fulfill a plethora of use cases. Wi-Fi is one of the flagship technologies operating in the unlicensed bands and it is now facing fundamental changes toward unprecedented requirements such as ultra-high reliability. This talk will provide an overview of the current evolution path of Wi-Fi towards a deeper integration of AI/ML functionalities, highlighting activities discussing unified architecture, common standardized interfaces and strategical functionalities.
Francesc WilhelmiPostdoctoral Researcher, Radio Systems Research GroupNokia Bell Labs - i
Speaker abstract
Driving Telco Innovation & Business Growth with Cloud-Native & AI; Telcos are undergoing a full transformation concerning their business model & services portfolio. Cloud is perceived as the primary catalyst for digital transformation, enabling new technologies such as Open Ran & 5G and unlocking new monetization opportunities. The role of AI and the contribution towards the Cloud Native platform are embraced by most of the Telcos. Join us for an exciting workshop exploring the technological evolution of cloud computing in the Telco domain and how it converges with other technologies to accelerate the telcos' digital transformation. We will examine the role of Artificial Intelligence in the Telco Cloud, the potential use cases, and the intersection with concepts such as Edge Computing & Cloud-Native.
Karim RabiePrincipal ArchitectRed Hat 
Lina BariahLead AI ScientistOpen Innovation AIAdjunct Professor, Khalifa University
Lucian PetricaSenior ResearcherAMD Research and Advanced Development (RAD)- i
Speaker abstract
Deep Learning in the Physical Layer: Transforming 5G and 6G Performance with Data and AI; Wireless systems, access schemes, and band allocations are becoming increasingly complex and heterogeneous. Deep learning-based solutions within the physical layer allow for a rapid and accurate state of the art approach for both spectrum sensing as well as channel access and physical layer design. In this talk, we’ll provide an overview of the background and enablers for this trend of ML in the physical layer of communications and highlight how we are building software solutions based on these approaches and techniques at DeepSig. OpenRAN based wireless solutions are also becoming increasingly prevalent within 5G RAN deployments and provide a key opportunity for rapid deployment of these technologies and to improve their performance in real-world deployments. We’ll highlight how we are using both DL driven PHY techniques and ORAN software to bring these capabilities into ORAN deployments in order to improve energy efficiency and to improve spectral efficiency, capacity, and user experience. Finally, we’ll discuss where we believe these technologies are leading future RAN technologies and standards, and provide highlights from our own trials and partnerships, highlighting the maturity of the technology and its likelihood for adoption in next generation wireless deployments.
Tim O’SheaCo-Founder & CTODeepSig Inc
On the sidelines of day 1, there would be demos scheduled that may showcase specific open source or other implementations, bringing out the applications, platforms, or architectures related to AI in xG.
