HASC Research Pillar: C2 Adaptivity | Imperial College London

What if a wireless network could learn to serve you better – without needing to ask where you are, what device you’re holding, or which way you’re facing? That’s exactly the question a team at Imperial College London set out to answer.

Led by Professor Kin Leung and Dr. Nancy Nayak, this research sits at the heart of HASC’s Adaptivity pillar – a challenge focused on building networks that can intelligently respond to changing conditions in real time. Their approach: replace the rigid, standards-dependent methods that underpin today’s mobile networks with something far more flexible. An AI that learns.

“We are not just jumping on to the band wagon of AI – we have developed AI-based wireless communication technologies which are fundamentally different from the conventional technologies used today.” ~ Professor Kin Leung, Imperial College London

“By designing wireless technologies with AI, we move beyond dependence on channel state information—unlocking faster development cycles and enabling networks to adapt to user needs and dynamic environments, unconstrained by standards.” ~ Dr. Nancy Nayak, Imperial College London

Why We’re Experimenting

The demand on wireless networks is accelerating. More users, more devices, more data – and the greater the expectation. People want and expect seamless connectivity everywhere, from city centres to crowded stadiums. Meeting those demands with today’s tools is increasingly difficult, and the gap between what users want and what networks can reliably deliver is widening.

The team at Imperial saw an opportunity to fundamentally rethink how networks allocate their resources. Rather than optimising within the constraints of existing standards, they asked: what if the AI could simply observe a network in action and learn the best way to manage it – without any of the traditional scaffolding?

The Industry Challenge

At the core of most modern wireless systems is something called Channel State Information, or CSI. This is data that describes the communication channel between a base station and a user’s device – think of it as the network’s way of understanding who’s where and how best to reach them.

The problem is that collecting and communicating CSI comes at a cost. For complex antenna systems (the kind increasingly used in 5G and future 6G networks) the overhead is significant. And because CSI exchange relies on standardised protocols, any improvement to the process must go through the slow, uncertain machinery of the international standards process, which can take years.

Add to this, the computational burden of recalculating antenna configurations as users move, and the challenge becomes clear: current approaches don’t scale well to the networks of tomorrow.

Our AI-Enabled Technology Innovation

The Imperial team has developed a Reinforcement Learning (RL) technique that sidesteps these constraints entirely. Their system allocates wireless resources (directing antenna beams and managing radio resources) without relying on CSI, user location data, or any information obtained through standardised protocols.

Instead, it learns. Using only the radio measurements already available within the network, the AI explores different resource-allocation strategies, receives feedback on what works, and progressively improves – just like a person learning a new skill through practice rather than instruction.

The results are striking. In testing, the approach achieves performance within 6% of the theoretical optimum for CSI-based methods – without any of the associated overhead. The system can also co-exist with conventional CSI-based approaches used by neighbouring base stations, making it compatible with real-world deployment scenarios.

Crucially, because the technology is standards-agnostic, operators don’t need to wait for industry alignment before deploying it. New antenna technologies and networking innovations can be adopted as soon as they’re ready.

The Impact of CSI-Free Networks 

The implications reach well beyond the lab. For network operators, this approach reduces complexity, lowers the cost of upgrades, and dramatically shortens the path from innovation to deployment. For users, it means more reliable connections in the places that matter most: busy transport hubs, large events, dense urban environments.

Looking further ahead, the team is building a prototype for cellular network settings and exploring civilian and defence applications – with defence interest from Defence Science and Technology Laboratory (Dstl) and the Royal Air Force. The commercial opportunity is significant, with the short-term market for private 5G and enterprise networks estimated at $20–200 million, and long-term integration into baseband units pointing to a $0.5–1.5 billion opportunities. The global baseband unit market is ~$5–7B today and growing beyond $10B; as a core signal processing function (~5–20% share), resource allocations including beamforming underpin this serviceable segment  [1, 2, 3].

This is a project that doesn’t just improve a single component of how wireless networks function. It reframes how they can be built, deployed, and improved – putting adaptivity, rather than standardisation, at the centre.

Interested in this research or the path to commercialisation?  

We’re currently keeping our IP confidential while we finish our patent filings. At the same time, we’ve started building a live demo to show the tech in action. This demo is a key part of our spinout plan, as it will give investors a clear look at the value the new AI wireless technology brings to network users.

If you would like to get in touch with this project, please contact us here.