Dr. Yue Wang: A Global Leader in 6G Research and Telecom Innovation

Your Professional Journey

Q: How has your experience leading Samsung's 6G Research Group and Advanced Network Research Lab, along with your work at Philips, Toshiba, and Nvidia, shaped your journey and most significant contributions to telecommunications innovation in your current role as Chief Technologist at China Telecom?

Leading Samsung’s 6G Research Group and Advanced Network Research Lab was a transformative chapter in my career. It gave me a front-row seat to the earliest stages of next-generation mobile technology - where ideas are still unproven, and success depends on building consensus across industry and academia. I learned how to set a vision, structure multi-year research roadmaps, and, most importantly, translate deep technical concepts into clear strategies that inspire teams and attract global collaboration. Those skills — vision-setting, strategic innovation, and ecosystem leadership — are exactly what I apply today as Chief Technologist at China Telecom, where the challenge is not just creating cutting-edge solutions, but integrating them into one of the world’s largest operational networks.

Throughout my career, I’ve learnt a great deal that has significantly deepened and broadened my technical skills and how to best apply these to drive innovation. Addressing today’s needs while staying ahead of the curve means understanding emerging technologies, anticipating business dynamics, and assessing long-term feasibility—which requires sound judgement in prioritizing impact over urgency, sustainability over short-term gains, and collaboration over siloed thinking.

One of my most significant contributions has been leading a portfolio of research and innovation initiatives that define and evolve AI-driven network architecture and services. Through fundamental research, experimental validation, and real-world deployment, from pioneering AI-driven network optimization to shaping industry standards and advancing 6G technologies, I bridged theory and practice to unify communications and compute for AI-native services, that empower emerging AI services such as robotic dogs and smart glasses. These results are delivered as white papers, publications, patents, experiments, and standard contributions. I am keen to continue working with industry partners towards advancement for more unified, scalable, and AI-ready digital infrastructure.

Industry Insights

Q: As a leader in both 6G research and AI integration in telecommunications, how do you envision AI-native networks transforming telecom operations and customer experiences over the next five years?

I envision AI-native networks are the foundation of transforming telecommunications from mere connectivity providers into intelligent platform enablers. By deeply integrating AI across core, edge, and RAN, we can create self-optimizing, context-aware systems capable of end-to-end automation. This intelligent platform forms the critical foundation for supporting next-generation services —from AI glasses and embodied robots to industrial automation— under one unified platform, with the opportunity of unlocking new services and business models. And I believe AI-native networks are the foundation to redefine telecom from a ‘hidden pipe’ into a true platform for innovation.

I see networks transforming from pipes to platforms - unifying data, AI, compute and communications into a single, intelligent fabric that powers our digital future.

As researchers and innovators, we are very fortunate standing at the intersection of two accelerating curves—6G evolution and AI breakthroughs. By embedding AI as an intrinsic capability rather than an add-on, we’ll enable networks to be self-perceiving, self-optimizing, and responsive in real time.

Operationally, this means end-to-end intelligence: from the core to the RAN, networks will coordinate connectivity, computing, and AI resources (‘Byte + Compute dual-drive’) to match the needs of new applications. This will cut operational costs, improve service agility, and open monetization paths such as AI-enhanced content processing and intelligent edge services.

For customers, the experience will shift from static connectivity to context-aware, on-demand performance — low latency for embodied robots, elastic compute for AI glasses, or deterministic response for industrial automation means new services must rely on both the connectivity and compute capabilities. And I think this is the opportunity where operators can regain the ecosystem leadership in 6G.

The key is not to wait for AI to adapt to the network, but for the network to evolve rapidly to serve AI.

Q2. What do you see as the most significant challenges in balancing innovation with standardization in the emerging field of AI-driven telecommunications?

AI-native networks sit at the crossroads of two very different innovation cultures — telecom’s careful, deterministic, standards-driven mindset and IT’s rapid, iterative, and ‘fast fail’ development. Balancing these is not trivial.

The main challenges are:

1. Standards vs. speed – 3GPP’s release cycles don’t align with AI’s rapid iteration. Waiting for formalized and rigid standards, we risk missing the technology, ecosystem, and monetization window, bypassed with new services built on hyperscale cloud platforms.

2. ‘Legacy’ architecture – Traditional network architectures, with their rigid hierarchical designs, originated in an era before AI and cloud computing existed. While the industry has shifted toward more flexible, software-defined networks, certain components - particularly the RAN - remain to be proprietary and rigid, and a bottleneck to realising truly intrinsic end to end intelligence in modern network infrastructure, creating challenges in the transition to AI-native systems.

3. Business model alignment – AI in the network must prove commercial value, not just technical feasibility. We must avoid expensive ‘GPU-everywhere’ models that burden OPEX, and instead find flexible, schedulable compute strategies.

4. Ecosystem coordination – Platform thinking is essential. Without a vibrant ecosystem of device makers, AI developers, and vertical industries, killer apps won’t emerge, no matter how advanced the network is.

Your NETWORK X Participation

Q: What key insights about the intersection of AI and network technology are you most excited to share with the Network X audience?

I will be in a panel session on ‘Transforming telco operations, services and experiences with AI’. What I am most excited about is to share some of our recent developments on AI-native RAN and their potential of enabling new services and business models. For example, by computing resources virtualisation and exposure, we introduce the RAN AI layer, which dynamically schedules both network and cloud resources, supporting both connectivity-focused RAN functions and AI-driven computing tasks, achieving token response time of 32 ms while supporting 400 active robotic dogs simultaneously - a glimpse into the future of real-time, AI-powered network services and a step toward our long term vision of seamless network and cloud convergence. I look forward to discussing how this approach is shaping future telecom infrastructure and services.