Teaching the network to think: Smarter fibre for the future of connectivity
The challenge
Today’s networks are under growing pressure. As emerging technologies like 5G, 6G, AR/VR, IoT, cloud services, and AI continue to evolve, they’re driving an explosion in data demand, placing unprecedented strain on existing fibre infrastructure. And this demand is only going to increase.
Traditional optical networks mostly operate within the C-band, but multi-band (MB) elastic optical networks (EONs) offer a powerful alternative. By unlocking additional spectral regions such as the L and S bands, MB-EONs expand the usable spectrum from around 5 THz to as much as 20 THz. So far, so good.
The challenge? Managing this expanded spectrum is becoming increasingly complex. Conventional algorithms simply aren’t up to the task. The result is a high rate of service blocking, where connection requests are denied or dropped, leading to poor user experience and significant inefficiencies in resource use.
The solution
A new approach to spectrum management is required. The team wanted to establish new ways of addressing four main issues:
- Service blocking
- Complex spectrum management
- Quality of transmission (QoT)
- Scalability
The team turned to AI and deep reinforcement learning (DRL), which is the same novel technology used to power things like autonomous driving algorithms and robots learning to walk, jump, or perform a backflip.
Reinforcement Learning (RL) is a type of machine learning where an (AI) agent learns to make increasingly better decisions by interacting with its environment. It gets rewarded for correct calculations and optimisations and penalties for bad decisions.
Deep learning (neural networks) allows the agent to handle more complex, high-dimensional environments.
Key technological innovations
Applying DRL to network management, the team developed an innovative framework designed to do a number of things.
Learn and continually improve
The new framework learns by interacting with simulated networks, rewarding itself for successful provisioning. Inputs to the DRL include both route-level and band-level features, giving it a rich view of the whole network and current conditions. This allows it to adapt and make holistically informed decisions.
The DRL accelerates its own learning and optimises for long-term throughput (not just short-term wins). The innovative reward function enables it to learn effective strategies way beyond what traditional heuristics can achieve.
The system trains itself to get better over time instead of needing constant human fine-tuning.
Handle complex spectrum management
DRL handles the complexity of multiple bands and modulation formats that traditional algorithms simply cannot. It can dynamically allocate spectrum across MB-EONs, making more efficient use of the available spectrum.
Provide sufficient signal quality
The built-in QoT assessment model ensures that allocated resources provide sufficient signal quality, avoiding wasted or unusable connections. It does this by profiling the real performance of modulation formats per channel, so the algorithm makes physics-aware decisions. This means fewer wasted resources and ultimately more reliable connections overall.
Network adaptivity
AI adapts and scales with growing demand, unlike static rules.
The results
- Service blocking reduced by 35% - 85% compared with existing heuristic methods: Making networks significantly more efficient and capable of handling surging traffic.
- Improved resource utilisation: Allocating spectrum more efficiently and with higher QoT guarantees.
- Scalability: System adapts to increased traffic loads and network complexity.
- Operational efficiency: Operators can serve more users and higher bitrates without physically laying more fibres (which is costly, disruptive and not always viable - especially in built-up areas). Boosting capacity in this way offers a more effective and sustainable way to scale.
This method avoids the need for costly, high-emission fibre deployment, aligning with the UK’s sustainability goals and net-zero strategy by maximising existing infrastructure efficiency. This innovation could benefit many real-word applications including 5G and 6G backhaul, high throughput networks and even data centre interconnects for hyper-scalers such as Google and Meta. Without solving these issues, users face slower speeds, dropped connections, and spiralling infrastructure costs. For users, this innovation means improved services such as consistently reliable streaming and improved mobile performance.
Time to deployment
Short term: Algorithm validated in simulation; lab prototypes are feasible now
Medium term (3–5 years): Field trials for 5G backhaul expected as MB optical systems evolve
Long term (5–10 years): Key enabler for 6G core network infrastructure and widespread MB-EON adoption
This is not something operators can roll out tomorrow, but it is an important stepping stone towards preparing optical infrastructure for 6G-scale traffic. By laying the groundwork now, operators will be ready for a future where 6G-scale traffic is not longer a challenge, but a significant opportunity.
Proud to see how AI can reshape optical networking for the next generation of scalable, resilient infrastructures.
TEAM & PAPERS
University of Bristol – Smart Internet Lab
-
Yiran Teng
-
Haiyuan Li
-
Shuangyi Yan
-
Dimitra Simeonidou
Chalmers University of Technology, Sweden
-
Carlos Natalino
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Paolo Monti
Universidad Carlos III de Madrid, Spain
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Farhad Arpanaei
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Alfonso Sánchez-Macián
Supporting Projects
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ECO-eNET - EU-SNS funded initiative for energy-efficient confluent networks
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ALLEGRO EU - Horizon Europe project for ultra-low energy secure networks
Paper: DRL-Assisted QoT-Aware Service Provisioning in Multi-Band Elastic Optical Networks - https://ieeexplore.ieee.org/abstract/document/11131684
Tales Gaspar
Tales has a background in law and economics, with previous experience in the regulation of new technologies and infrastructure.
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
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Contact the team
Kir Nuthi
Kir Nuthi
Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
Kir has an MSc in International Public Policy from University College London and a BA in both Political Science (International Relations) and Economics from the University of California San Diego.
Outside of techUK, you are likely to find her attempting studies at art galleries, attempting an elusive headstand at yoga, mending and binding books, or chasing her dog Maya around South London's many parks.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
Sue Daley OBE
Sue Daley OBE
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This includes work programmes on cloud, data protection, data analytics, AI, digital ethics, Digital Identity and Internet of Things as well as emerging and transformative technologies and innovation policy.
In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List.
She has been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and in 2021 was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame.
A key influencer in driving forward the data agenda in the UK, Sue was co-chair of the UK government's National Data Strategy Forum until July 2024. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries for 2020 Sue has also been shortlisted for the Milton Keynes Women Leaders Awards and was a judge for the Loebner Prize in AI. In addition to being a regular industry speaker on issues including AI ethics, data protection and cyber security, Sue was recently a judge for the UK Tech 50 and is a regular judge of the annual UK Cloud Awards.
Prior to joining techUK in January 2015 Sue was responsible for Symantec's Government Relations in the UK and Ireland. She has spoken at events including the UK-China Internet Forum in Beijing, UN IGF and European RSA on issues ranging from data usage and privacy, cloud computing and online child safety. Before joining Symantec, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Masters Degree on International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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