Annapurna
Pradhan
CNRS Postdoctoral Fellow at ETIS Lab, ENSEA, France
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
Wed May 13 - 8:10 AM EDT/New York (See in local time)
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05/13/2026 8:10 AM
05/13/2026 08:50 AM
America/New_York
#WTGC2026
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
#WTGC2026
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
https://www.womentech.net/ringcentral
https://www.womentech.net/ringcentral
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
"Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks"
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Session: Toward AI-Native 6G: Secure, Reliable, and Context-Aware Future Wireless Networks
AI-native 6G is increasingly conceptualized as an intelligent and context-centric networking paradigm that couples communication with computation, control, and learning, shifting the objective from transporting bits to delivering task-relevant meaning with measurable effectiveness under stringent URLLC constraints. Emerging communication frameworks where AI is built into the network as a foundational design principle, like semantic communication, propose representation and intelligent radio resource management strategies that exploit context to prioritize information that maximizes task success while reducing latency, energy, and bandwidth consumption in distributed intelligent systems. However, embedding deep ML models and learned latent representations into the end-to-end communication loop broadens the attack surface beyond classical eavesdropping to semantic leakage, model inversion, adversarial perturbations, and generative reconstruction, motivating new threat taxonomies and security by design principles. To address these risks without violating future 6G requirements, recent research increasingly combines robust learning with physical-layer mechanisms to enhance confidentiality and resilience under channel uncertainty and vulnerable conditions. The session will outline open challenges for deployable AI-native 6G including formalizing semantic reliability metrics, developing privacy-preserving context sharing interfaces at the edge, and creating deployable AI Native wireless architecture aligned with trustworthy and resilient future 6G service requirements.Key Takeaways
- How AI-native 6G shifts the goal from transporting bits to delivering task-relevant meaning.
- What semantic communication and context-aware resource management.
- The key new threat landscape in AI-native links: semantic leakage, model inversion, adversarial attacks, and generative reconstruction.
- Open research challenges and opportunities.
Bio
I am Annapurna, a CNRS Postdoctoral Fellow at ENSEA, France, where my research focuses on semantic communication and context-aware communication, integrating machine learning for secure and reliable communication in next-generation networks.Over the years, I have been fortunate to receive several honors, including being featured in the “100 Brilliant and Inspiring Women in 6G”- List for 2025, the ‘Odisha Women Leader’ award, and the 'Dr. Tarita Shankar Award of Excellence' in 2023 for my research and social work. I have had the privilege to present my work at numerous national and international conferences as a keynote speaker, and I actively promote awareness of emerging technologies through public talks and live shows. I am the member of COST Action 6G-PHYSEC (EU), Ambassador of AI Exponential Thinker for India, a global ambassador of the Women Tech Network, and a proud member of the Women in 6G initiative. My journey continues with a vision to blend research, innovation, and social impact in everything I do.
