Abstract: To meet the heterogeneous service demands of future applications, ranging from holographic communications, massive digital-twins, to large scale Internet of Things, next generation mobile networks (6G) will require unprecedented management agility, stringent quality-of-service guarantees, and real-time adaptability. To cope with the increasing control complexity and physical constraints of wide-area deployments, 6G networks will evolve to be AI-native by design, integrating machine-learning (ML) capabilities through layers of the architecture. In this talk we focus on how distributed intelligence can deliver efficient network-resource management at scale. We highlight some use cases for harnessing distributed intelligence to unlock edge devices’ potential, demonstrating tangible gains in throughput, energy efficiency, and reduced control overhead across large-scale systems.
