Wireless Infrastructure Association (WIA) has published its final blog as part of its AI Series, which offers a forward-looking perspective on how artificial intelligence will shape the next generation of wireless networks – 6G. This blog has been written by Iain Gillott, Senior Research and Technical Advisor, WIA.
As global discussions around 6G intensify—covering everything from spectrum allocation to technical architecture—AI is emerging not only as a tool for optimization, but also as a fundamental building block of the 6G ecosystem. Unlike in 5G, where AI is layered onto existing infrastructure, 6G networks are expected to be AI-native from the ground up. This means AI will be deeply embedded into every layer of the network, enabling capabilities like real-time closed-loop automation, distributed learning, and environment-aware sensing.
In this concluding piece, WIA examines the critical AI innovations expected to define the Radio Access Network (RAN) of 6G, the ethical and governance challenges ahead, and the broader strategic implications for the telecom industry. The key topics discussed in this blog include:
- AI as a native foundation of 6G;
- Real-time closed loop automation;
- Distributed learning and AI collaboration;
- AI-enhanced network sensing and environment awareness;
- Ethical AI and governance in 6G; and
- Strategic implications.
AI as a Native Foundation of 6G
With 5G, AI is bolted onto existing architectures and networks – 5G was not designed, or even imagined, to use AI to the degree anticipated today. But 6G is being designed with AI as a native, built-in function from the very start. The likely implications of this are that:
- AI will define how the network operates at a core level, not just optimize existing operations and parameters;
- AI models will likely be responsible for autonomous service creation, zero-touch management, and intent-based orchestration – in essence, the 6G network will define, manage and operate itself;
- Similarly, the fundamental 6G RAN design will be driven by AI models that optimize the physical layer functions, including waveform selection, coding schemes and spatial multiplexing strategies.
As with today’s AI models, continuous learning will also apply to the AI-native 6G network. Embedded feedback loops will enable real-time adaptation and self-improvement of the model, which will then be applied to the network.
Real-Time Closed-Loop Automation
Today’s 5G networks use Self Optimizing Network (SON) concepts. (This effort was actually started back in 4G LTE). In 6G, these concepts will extend to self-evolving networks where network architectures, resource allocation and even software functions are dynamically and continuously updated and refined.
One of the main goals for 6G is the implementation of fully closed-loop, real-time network automation such that:
- AI agents continuously observe network behavior;
- Decision-making and enforcement occur autonomously;
- Performance is validated and tuned with minimum human intervention.
This means that the RAN will fundamentally change from being simply a passive layer that transmits and receives data to and from end-user devices into a context-aware system driven by AI.
Distributed Learning and AI Collaboration
Just as the RAN is distributed across the country, 6G will require massively distributed AI systems. These distributed AI models will be:
- Trained across thousands of edge nodes using federated learning to minimize privacy and security risks;
- Continuously updated through multi-agent learning – the various RAN elements will work together across the network to improve the performance of the AI model; and
- Able to adapt to changing environments without the need to retrain a new AI model from scratch.
The AI fabric incorporated into the 6G network therefore will be distributed to provide reliability, adaptability and scalability to meet dynamic service demands as the 6G network, and the applications it enables, evolve.
AI-Enhanced Network Sensing and Environment Awareness
Network sensing will be integrated into 6G infrastructure such that the RAN senses its own environment, as well as transmitting and receiving data. This means that:
- The RAN likely becomes a distributed sensor grid, capable of detecting motion, presence and object shapes in its immediate vicinity and further afield via reflected signals; and
- Machine learning models will interpret these signals to potentially enable services such as traffic monitoring, intrusion detection and real-time environmental mapping.
These sensing and detection functions will mean that 6G will enable a range of new cross-industry use cases, from smart buildings and infrastructure monitoring to precision agriculture and autonomous vehicle coordination. Rather than using a GPS signal (as many devices do today) which can be blocked, the RAN itself will be used to determine location and movement.
Ethical AI and Governance in 6G
The extensive use of AI in upcoming 6G networks raises new questions of ethics and governance. In short, procedures and frameworks will be required that enable:
- Fairness in algorithmic decision-making such as resource allocation to ensure that all parts of the network have access to the necessary capacities and capabilities;
- Explainability and accountability for autonomous actions – network changes and actions must be audited and accountable;
- Global standards for secure, privacy-preserving AI operations.
Standards organizations such as ITU, ETSI, and IEEE are already working on principles for trustworthy AI in telecommunications, which will be critical for 6G. The goal is to enable these trust and accountability frameworks at the start of 6G network design and implementation and not to have to add the required features later on.
Conclusion
The evolution toward AI-native RAN in 6G will require significant shifts across the mobile ecosystem. Network operators, vendors, and regulators must prepare for a future where telecom, AI, and software engineering converge. This means cultivating cross-domain expertise, expanding edge computing capabilities to support real-time AI processing, and fostering open, agile platforms that can rapidly integrate new models and services. As this series has shown, AI is already enhancing today’s RAN, but in 6G it will be central to how networks are built, optimized, and experienced. From improving spectral efficiency and user experience to enabling more flexible operations, AI addresses the growing complexity of mobile networks. While 6G development poses challenges—including spectrum readiness and deployment planning—AI will remain a foundational block shaping the networks of tomorrow.