6G AI-Native Networks

- Overview

AI-native networking for 6G means artificial intelligence (AI) is integrated at every level of the network from the ground up, moving beyond simple optimization to enabling autonomous operation. 

Its implementation involves using AI to control the entire network life cycle, including self-configuration, self-optimization, and self-healing, through continuous learning and real-time adaptation. 

Key areas of implementation include AI-driven radio access networks (RAN) that are dynamically adapting to conditions, and the use of AI for creating closed-loop automation and new applications like agentic AI.

1. Key characteristics of AI-native 6G networks:

• Integrated intelligence: AI is not an add-on but a fundamental, pervasive layer built into the network's architecture.
• Autonomous operation: Networks will be capable of performing self-configuration, self-optimization, and self-healing autonomously.
• Real-time adaptation: AI models will enable continuous, real-time adaptation to changing traffic, channel conditions, and user behavior.
• Performance-driven protocols: Instead of fixed parameters, the network will use AI to dynamically adjust protocols based on performance targets (KPIs) and user-specific needs.

2. Implementation and technology examples: 

• AI-powered RAN: AI models will drive the physical layer, optimizing everything from waveform selection to spatial multiplexing and adapting the radio interface to the transmission content.
• Closed-loop automation: An automated system where AI agents constantly monitor network behavior and autonomously make and enforce decisions, moving beyond today's Self Optimizing Network (SON) concepts.
• Spectrum agility: AI will allow for the dynamic and intelligent use of spectrum, for instance, by isolating and freezing only those frequencies affected by interference and keeping the rest of the system online, as demonstrated in early prototypes.
• Edge AI integration: Networks will leverage the network edge to host AI services, enabling new applications like autonomous vehicles, precision agriculture, and advanced manufacturing.
• AI platforms and research kits: Companies like NVIDIA are building AI-native wireless stacks and open platforms, such as the Sionna Research Kit, to accelerate development and experimentation by making it easier for researchers to build and test AI models in real-time network environments.