29 Oct 2025
by Professor Weisi Guo

Embedding Native AI in 6G Telecom Infrastructure to Create New Services

The challenge for telecoms

Telecommunications have traditionally served as a data pipeline between end-users and cloud services, moving data seamlessly as end-users travelled on railways and drones flew across the sky. The profit per data transmitted is low and market growth is weak. With 6G, there is an opportunity to host advanced AI services within the telecom infrastructure, bringing native AI services closer to the end-user and developing personalised services that can benefit the telecom industry.

Unlocking edge agility

Today’s telecoms ecosystem is largely closed, but must evolve into one that enables self programmed automatically customised services. Native AI in 6G offers exactly this – agility at the network edge for business and end users.

Potential applications include integrated sensing and communications (ISAC) and augmented reality. Yet success depends on developing the right agentic AI services implementing strong guardrails and verification processes and building trust in new levels of automation.

Our approach at CHEDDAR

At CHEDDAR, we see AI in telecoms taking three forms:

  1. AI to improve the radio network
  2. AI services hosted on the radio network
  3. Unrelated AI and radio sharing common infrastructure

Our primary focus is on the second: hosting mobile AI services at the edge. We are developing AI agents that can both optimise the radio network and self-programme new services for end-users. This is supported by a telecoms ontology foundation model and innovative AI pipelines.

We are also mindful of the energy implications. Edge AI can dramatically increase consumption, which is why CHEDDAR invests in low-energy AI/ML pipelines and uses digital twins to reduce the carbon footprint of AI-enabled networks.

Innovation in verification and trust

What sets CHEDDAR apart is our emphasis on verifying and safeguarding agentic AI in telecoms.

Highlights include:

  • Developing explainable AI interpretations (logical, causal and counterfactual) for complex 6G decision making, ensuring mission critical services receive appropriate resources.
  • Addressing the coexistence of multiple vendor AI algorithms within the RAN. We ensure not only their individual compliance but also that their cascade effects remain stable.

Results and insights

Embedding agentic AI into the telecoms radio network offers clear benefits: sovereignty, low latency, new services, and new revenue opportunities. It also positions the UK at the forefront of standardisation and thought leadership.

Yet challenges remain chiefly energy consumption, trust in AI, transparency, and the ability to verify interoperability across diverse AI agents. These are central to CHEDDAR’s research, ensuring future networks are both innovative and reliable.

 

Tales Gaspar

Tales Gaspar

Programme Manager, UK SPF and Satellite, techUK

Usman Ikhlaq

Usman Ikhlaq

Programme Manager - Artificial Intelligence, techUK


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Authors

Professor Weisi Guo

Professor Weisi Guo

CHEDDAR Principle Investigator , Cranfield University