If you’ve been wondering whether artificial intelligence is actually useful for research, or whether it’s just hype, the short answer is: it’s real, it’s here, and it’s moving fast.

We recently sat down with Alok Sahu, Senior Research Software Engineer at the AI Competency Centre, University of Oxford, to talk through what’s changed, what’s possible, and what researchers should be doing about it.

Why are LLMs Relevant to Research and Why Now?

Researchers have always worked with considerable volumes of text, reading papers, writing them, taking notes and reviewing others’ work. So, tools that handle text more efficiently have always had direct relevance. What’s shifted dramatically in the last couple of years isn’t the idea of using AI, it’s the quality of what these models can actually now do. Where they once struggled beyond a simple sentence completion, they can now summarise multiple long documents, draft and debug code, and carry out complex multi-step tasks with limited supervision.

The Leap to Agentic AI

One of the most significant recent developments is agentic AI – models that don’t just generate text but take actions. They can run literature searches, extract data from research papers, search the web, and return results, all in one workflow. For researchers, this opens up possibilities that simply weren’t practical before.

AI Is Not Just for One Field

The applications span multiple industries and sectors. In agriculture, Alok discussed with us, how AI is being used to predict crop yields and monitor fields. In construction, it’s flagging unsafe worksite behaviour and running design simulations. In healthcare, specialist models are emerging that can assist with drug discovery and genetic analysis. Whatever your research domain, there’s likely something relevant already developing.

New to LLM’s and AI? Where to Start

Alok’s advice is straightforward: dive in. Try the tools, experiment with your prompts, and see how the outputs change. Beyond the general platforms like ChatGPT, Claude, and Gemini, it’s worth exploring whether specialist models exist in your own field. The pace of development means domain-specific tools are emerging all the time.

The gap between those engaging with AI and those who aren’t is widening. The good news is it’s not too late to start and there’s no better time than now.

Watch the full conversation on our YouTube channel.

Connect with Alok:  https://www.linkedin.com/in/alokkrsahu/ 

Follow the AI Competency Center: https://www.linkedin.com/groups/15690177/ 

SMALLER, CHEAPER & GREENER MOBILE NETWORK HARDWARE

Mobile networks are quietly one of the most energy-intensive parts of our digital infrastructure. The towers, antennas, and radio units that keep us connected consume enormous amounts of power – and much of that consumption comes not from inefficiency in the obvious sense, but from a fundamental design problem that the industry has largely accepted as unavoidable. That is, until now.

The Problem: Duplicated Hardware, Multiplied Costs

Modern mobile networks operate across multiple frequency bands simultaneously. A single base station might need to serve users on a mid-band 5G frequency while also handling a separate private network on a different band. The way this has traditionally been handled is straightforward, but wasteful: build a separate radio unit for each band.

Two bands. Two sets of hardware. Two sets of power draws. Two sets of components to manufacture, deploy, and maintain.

Multiply that across thousands of base stations and the cost and carbon implications become significant. It’s one of the reasons that energy expenditure is a major operational burden for mobile network operators – and a genuine obstacle to meeting net-zero targets.

The question the University of Sheffield’s Wireless Communication Systems team set out to answer through the YO-RAN project was simple: why are we building two of everything, and can we stop doing that?

The Solution: One Radio Unit, Two Bands, a Single Shared Architecture

Demonstrated at Mobile World Congress 2026 in Barcelona, the Dual Band Open-RAN radio unit represents a significant step towards a leaner and more efficient model for mobile network hardware.

The core innovation is architectural. Rather than pairing two independent radio chains – each with its own digital-to-analogue converter (DAC), analogue-to-digital converter (ADC), and RF components – the Sheffield team has developed a radio unit in which two frequency bands share a single RF chain. Specifically, the prototype operates across the n78 (3.5 GHz) and n77-upper (4.005 GHz) frequency bands, which together cover both public 5G networks and private enterprise deployments.

The digital front-end (DFE) is built on the AMD/Xilinx ZCU670 RFSoC platform, a high-performance reconfigurable system-on-chip that handles two component carriers simultaneously. On the analogue side, a custom-designed RF front-end board manages both bands through shared circuitry – a single low-noise amplifier (LNA) for the receiver, and a shared transmit path enhanced with digital predistortion (DPD).

That last technique is worth pausing on. Power amplifiers are most efficient when operating close to their maximum output – but pushing them too hard distorts the signal. DPD corrects for that distortion digitally, allowing the amplifier to run at a more efficient operating point without sacrificing signal quality. It’s a meaningful contributor to reducing the overall power consumption of the unit. The result: fewer components, lower power draw, and a smaller physical footprint – without sacrificing performance.

Validated Against Industry Standards

Innovation in radio hardware only exists so far without interoperability. Open RAN – the disaggregated, vendor-neutral architecture that is reshaping how mobile networks are built – requires radio units to communicate with distributed units (DUs) from different suppliers using standardised interfaces.

The Sheffield prototype has been validated using the Keysight S5040A Distributed Unit Emulator, demonstrating compliance with the O-RAN 7.2x fronthaul interface specification. End-to-end testing confirmed that the dual band unit can exchange 5G NR signals cleanly across both frequency bands, with fronthaul synchronisation and RF chain performance verified across the full signal path.

This matters for the commercialisation pathway. A radio unit that works in a lab is one thing; a radio unit that can plug into a real Open-RAN ecosystem and interoperate with third-party DUs is quite another.

“The opportunity to present our neutral host, dual band, O-RAN radio unit technology at MWC was immensely valuable. The realisation of two concurrent, independent frequency bands on a unique single RF architecture yields significant cost and energy consumption savings. MWC and the HASC Hub provided the ideal environment to showcase this advanced radio technology, supporting our pathway to commercialisation and exploitation.”

~ Professor Timothy O’Farrell FREng,

Why This Matters Beyond the Lab

The implications of this work extend well beyond hardware engineering.

For network operators, a single compact radio unit covering two bands means lower capital expenditure on hardware, reduced installation complexity, and lower energy bills over the lifetime of the deployment. For operators running neutral-host models – where a single piece of infrastructure serves multiple operators or use cases – the ability to allocate the two bands independently is particularly powerful.

For vendors and the Open-RAN supply chain, simplified radio designs with fewer duplicated components reduce manufacturing costs and open up opportunities to deliver more competitive, sustainable products into a market that is increasingly cost-sensitive.

For society and the environment, the arithmetic is straightforward: lower-power radio units, deployed at scale across national and international networks, contribute significantly to reducing the energy footprint of mobile connectivity. At a time when both governments and operators are under pressure to demonstrate credible progress toward net-zero, such hardware-level efficiency gains matter.

The YO-RAN project, funded by DSIT, is one of a portfolio of research programmes housed within Sheffield’s National 6G Radio Systems Facility, a £2.4m testbed that provides one of the UK’s most capable platforms for physical layer 6G research.

What Comes Next

The prototype demonstrated at MWC is an early-stage proof of concept designed to establish that the architecture works and to inform the next phase of design refinement. Characterisation results from the prototype are now being used to optimise the hardware toward higher-TRL versions with improved RF performance and tighter integration.

The team is actively seeking research collaborators and industry partners to help accelerate that journey from prototype to deployment. If you’re working in network infrastructure, Open-RAN, spectrum policy, or sustainable connectivity and want to explore what a partnership might look like, get in touch.

6gfacility@sheffield.ac.uk  

The YO-RAN project is funded by the Department for Science, Innovation and Technology (DSIT). The National 6G Radio Systems Facility is funded by EPSRC. The work was presented at Mobile World Congress 2026, Barcelona, with support from the Hub for Access to the Spectrum Community (HASC).

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. 

Meet ORLANDO – the AI-powered xApp making wireless networks smarter, safer, and more resilient

Picture a busy summer festival. Thousands of people are live-streaming, uploading, chatting and scrolling – and then an emergency happens. First responders try to make contact, but the network is overloaded. They can’t get through.

Network congestion isn’t just frustrating. In critical moments, it can be dangerous. This is exactly the problem that researchers at the University of York, in collaboration with Imperial College London, are working to solve – through a project called ORLANDO (O-RAN intelligent adaptive Load blaNcing and efficiency in highly Dense deplOyments).

Why We’re Experimenting

As we move towards 6G, networks must serve more devices, more applications, and more people – all at once, and in dense environments. The ability to balance traffic intelligently and adapt in real time, is no longer a nice-to-have. It’s essential.

ORLANDO sits within the HASC Core Challenge on Adaptivity: a research programme dedicated to building networks that can think, learn, and self-optimise. The project is investigating how AI and machine learning (ML) can be embedded directly into Open RAN (O-RAN) architecture to manage load balancing at scale.

The Industry Challenge

Dense O-RAN networks struggle to maintain performance under uneven traffic loads. When demand spikes (at a concert, a sports stadium, or a major public event for example) spectrum is wasted, latency climbs, and quality of service degrades. Traditional, rule-based systems simply can’t respond fast enough to keep up with the unpredictability of real-world demand.

Our Technology Innovation

ORLANDO is an AI-powered xApp – a software application that runs within the O-RAN Intelligent Controller (RIC) – designed to perform real-time network slicing and intelligent load balancing. Using a digital twin integrated with live network data and precise access point locations, the system simulates and optimises performance across different user load scenarios before applying changes to the live network.

The team is also training a traffic prediction ML model on real user movement and traffic data – and fine-tuning a generative AI to produce synthetic data for new or unforeseen environments. This makes ORLANDO not just reactive, but predictive.

The Impact of the ORLANDO Project

Already tested on the York O-RAN testbed – and due to be trialled in a real-world deployment in Blackpool – ORLANDO is designed to deliver tangible benefits: dynamic traffic prioritisation, optimised Quality of Service (QoS), and reliable connectivity for critical services, even at peak demand.

For citizens, this means fewer dropped calls, less buffering, and the confidence that emergency services will always be able to get through – even in a crowd. For industry, it signals a new era of intelligent, self-optimising networks – built not just for today’s demands, but for whatever comes next.

ORLANDO Team Showcase

“In ORLANDO we move a step forward towards the AI-Native networks where we learn the user behaviour and train a scalable ML to predict network load and allocate network resources accordingly.” ~ Dr. Hamed Ahmadi, Reader in Digital Engineering

To connect directly with the ORLANDO researchers, register your here. 

See ORLANDO – The Intelligent Load Balancing xApp from University of York

Links, Papers & Further Resources    

• Scalable machine learning-based approaches for energy saving in densely deployed Open RAN – https://arxiv.org/pdf/2604.00201
• Unlocking Efficient Connectivity: Advanced AI-Driven Planning for Multi-Hop IAB Networks – https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11426753
• Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing – https://arxiv.org/pdf/2602.11076
• Green O-RAN Operation: a Modern ML-Driven Network Energy Consumption Optimisation – https://arxiv.org/pdf/2512.07006

Connect with Hamed Ahmadi

• https://www.linkedin.com/in/hamed-ahmadi-24504b36/
• https://www.itwins-lab.uk/
• https://scholar.google.com/citations?hl=en&user=lEcMAWwAAAAJ&view_op=list_works&sortby=pubdate

The security of communication networks is usually discussed in cryptographic terms, such as asymmetric cryptography and symmetric encryption. However, these cannot easily be applied to the billions of low-cost, limited-capability Internet of Things (IoT) devices coming online. This presents a major security risk, potentially giving malicious actors a back door into our private lives.

Yet every wireless connection – from a baby monitor to a satellite link – fundamentally depends on electromagnetic signals travelling through the physical environment. This ‘physical layer’ of radio frequency (RF) communication is becoming a promising new frontier for providing security across the wireless network.

What is RF physical layer security?

Physical layer security (PLS) describes security protocols applied to the very lowest layer of communications, and which exploit the inherent physical properties of channels. For RF PLS, this means harnessing characteristics such as noise, interference, and channel fading.

Unlike cryptography, which relies on the difficulty of solving a mathematical problem and hiding algorithms, PLS exploits physics – in particular the randomness of the wireless environment and the unique behaviour of real-world hardware.

Every wireless link has its own, location-dependent behaviour. Walls, furniture, other objects, and people moving around all cause signals to reflect, scatter and fade in ways that are unique to the positions of the transmitter and receiver.

Two legitimate devices in fixed positions ‘see’ the same rapidly changing channel between them and can treat it as a shared source of randomness for generating keys or authenticating each other. An eavesdropper in a different place, even a few wavelengths away (over 10 cm for Wi-Fi), experiences a different channel pattern, so cannot easily reproduce the same measurements or derive the same secret information. In this way, the physical environment itself becomes part of the security mechanism.

Why does physical layer security matter now?

Traditional cybersecurity tools have worked reasonably well for laptops, servers, and smartphones. These devices have powerful processors and large memories that can support heavyweight cryptographic algorithms.

The picture is different for the tens of billions of IoT devices now being deployed in homes, factories, cities, and elsewhere. Many of these sensors and embedded devices are:

• extremely low cost,
• battery-powered, with tight energy constraints,
• designed for ‘install and forget’ deployments in unattended environments.

In these settings, strong cryptography can be difficult to deploy or maintain. PLS, on the other hand, can be implemented with lightweight signal processing, can operate continuously, and be applied to legacy devices. Ultimately, PLS is not a replacement for cryptography, but a way to raise the baseline for these vulnerable endpoints – the ‘Achilles heel’ of the network.

HASC leadership

Researchers within the Hub for All Spectrum Connectivity (HASC) are leading the charge in three PLS areas in particular:

1. Physical-layer authentication

Physical-layer authentication uses channel measurements – for example, detailed channel state information – to create an ‘environmental fingerprint’ for a device. If a device suddenly appears with a channel profile that does not match its historical pattern, this could indicate spoofing or impersonation. HASC researchers have developed a practical deep learning-based physical-layer authentication scheme that can cope with mobile, time-varying wireless channels.

By training a neural network on a mix of synthetic and real Wi-Fi channel data, they reduced the amount of field measurements needed while still learning to distinguish between genuine devices and impostors. This shows that channel-based authentication can remain reliable even when users and devices are moving.

2. Wireless key generation

In wireless key generation, two legitimate devices observe their shared channel and run signal-processing algorithms to extract a shared secret key from the common randomness in the channel. Because an eavesdropper does not see the same channel, they cannot easily derive the same key.

Much early work in this area has been based on a single link between a pair of users. However, in real IoT networks, a central node typically interacts with many devices. HASC researchers have addressed this challenge by designing an efficient multi-user key generation protocol based on Wi-Fi 6 orthogonal frequency-division multiple access (OFDMA), which allows a central node to split the spectrum into many subchannels and talk to multiple users at the same time. The protocol uses OFDMA capability so that the access point can probe and harvest randomness from several user channels in parallel.

HASC researchers have also conducted extensive experimental work on key generation for Wi-Fi and long-range IoT technologies, opening up additional security options for future multi-technology 6G systems.

3. Radio frequency fingerprinting (RFF)

Every RF transmitter, even when manufactured to the same specifications, has subtle hardware imperfections in its amplifiers, oscillators, mixers, and other components. These imperfections imprint a unique, repeatable signature on the transmitted waveform.

Machine learning-based RF fingerprint identification (RFFI) can learn and recognise these signatures, enabling device-level authentication at the physical layer. Deep learning models, originally developed for image and speech recognition, are particularly well-suited to learning these patterns and classifying multiple signals.

HASC researchers have carried out comprehensive studies on exploring how RF fingerprinting could add an additional layer of security to networks the moment a device starts transmit information, without relying solely on user credentials. Their work ranges from LoRa, Wi-Fi, Bluetooth low energy to LTE.

HASC researchers at the University of Liverpool, Heriot-Watt University, and Queen’s University Belfast have demonstrated real-time RF fingerprinting using off-the-shelf Wi-Fi USB dongles, identifying which of multiple dongles sent a given packet based purely on their RF ‘fingerprints.’

Another HASC research focus is developing methods to ensure that RFF is applicable in the real world, and not just controlled, laboratory settings. For instance, a recent HASC study showed that advanced AI models, such as denoise diffusion models, can first suppress channel noise and effectively ‘amplify’ unique RF fingerprints, making them detectable and reliable even in noisy, real-world wireless environments.

Challenges and open questions

PLS could play a key role in supporting the UK Government’s Secure by Design agenda, and recently-introduced laws that require device manufacturers to implement minimum security standards.

But despite the rapid progress in PLS research, several challenges need to be addressed before this can become routine in commercial products:

• Standardisation and integration: PLS techniques are not yet widely embedded in mainstream standards. Integrating them without disrupting existing communication systems is a major research and engineering challenge.
• Scalability: Many experimental studies focus on tens of devices, but real deployments may involve thousands or millions of devices. Ensuring that key generation, authentication, and RF fingerprinting scale robustly remains an open question – one that requires more realistic testbeds and larger trials to answer.
• Access to physical-layer data: Techniques like channel-based authentication often require detailed channel state information that is not typically exposed by commercial RF chipsets. Closer collaboration with chipset vendors and equipment manufacturers will be important to unlock these capabilities at scale.
• All-spectrum connectivity: As we move towards a vision of multi-tech networks spanning RF, millimetre-wave, terahertz and optical links, this will create new opportunities to harness the diversity of channels for security – but also new opportunities for attack that need to be understood and managed.

PLS will never replace cryptography, but it offers a promising pathway to making wireless systems more resilient, adaptable, and trustworthy. By leveraging the underlying physics of the radio environment, HASC research is helping to make future networks inherently more secure from the moment a device connects.

With special thanks to Dr Junqing Zhang, University of Liverpool

Even whilst 5G is still being rolled out worldwide, the groundwork for 6G is already being laid, with standards delivery expected around 2030. Data use worldwide is increasing exponentially (rising 15% in the UK between 2022 and 2023 alone), and this will only accelerate with the advent of emerging and future applications, such as autonomous vehicles, smart cities and immersive healthcare. This means that future telecommunications networks will need to deliver a step-change in capability, rather than incremental gains.

Introducing 6G is not only about faster downloads; the vision is for an extremely fast, ultra-reliable, low-latency, and intelligent communication fabric, achieving coverage everywhere and helping to close the digital divide. This network will also need the capacity to accommodate future 6G applications that demand extremely high data rates – and the terahertz (THz) spectrum is emerging as a promising candidate to extend standard technologies into new domains.

What is the THz Spectrum? 

The THz spectrum spans frequencies from around 0.1 THz (100 GHz) to 10 THz, with corresponding wavelengths from about 3 mm down to 0.03 mm – shorter than microwaves, longer than mid-IR/near-IR, and overlapping the far-infrared. What makes THz particularly appealing is the enormous amount of bandwidth available. This allows for ultra-high-speed connectivity, potentially supporting wireless data rates exceeding 100 Gbps – well beyond what is possible with 5G or even advanced mmWave.

Because of this, the THz band has become an active 6G research topic, with engineers and scientists worldwide exploring how it could reshape future mobile networks.

Why THz Spectrum for 6G? 

 The THz spectrum offers:

• Massive bandwidth: Enabling peak throughputs of 100 Gbps and beyond.
• Faster data transmission (lower latency): Because THz provides an exceptionally large bandwidth, each data symbol can be transmitted over a much shorter duration, reducing overall latency. In addition, the very high data rates available at THz frequencies make it possible to send information without compressing it first – avoiding the extra processing time needed for compression and decompression in lower-bandwidth systems.
• Closer integration with optical fibre: THz signals can be generated directly from optical signals by photomixing (where THz radiation is generated by combining two different laser signals on a high-speed optical detector). This makes it possible to create seamless hybrid fibre-wireless networks, combining the reach of optical fibre with the flexibility of wireless.
• Advanced sensing capabilities: Thanks to their very short wavelengths and unique interactions with different materials, THz signals can be used not just for data transfer but also for high-resolution imaging and sensing – enabling applications from gesture recognition to integrated communication-and-radar systems.

These qualities position THz as an enabler of the capabilities policymakers have identified for 6G: intelligence, sustainability, security and universal access.

Utilising the THz spectrum in 6G could also unlock new innovations, including:

• Immersive communication: Holographic conferencing, volumetric (3D) video and ultra-realistic augmented or virtual reality will demand ultra-high-speed connectivity with millisecond latency.
• Data centre connectivity: Short-range high-speed comms using THz links could replace some fibre interconnects, reducing cabling complexity and cost.
• Wireless backhaul: THz could provide fibre-like performance in places where laying fibre is impractical.
• ‘Smart’ applications and automation: By enabling high-resolution sensing, THz could support applications that include ‘Internet of Things,’ ‘smart’ factories and cities, and autonomous driving – areas that require precise motion tracking and/or machine coordination.
• Robotics: Highly-responsive connectivity and integrated sensing could enable advanced robotics, including for healthcare, surgery and industry.

Barriers and Challenges in THz

Despite its promise, THz communications face steep barriers:

• Severe propagation loss: THz signals attenuate rapidly in free space and are highly vulnerable to obstacles and environmental conditions.
• Short transmission ranges: Links are typically limited to tens of metres without special equipment.
• Regulatory uncertainty: Unlike microwaves and mmWave, which already have clearly defined and regulated spectrum allocations, the THz band still lacks globally agreed allocations for communications.
• Device engineering: Generating and detecting THz efficiently requires specialised photonic and electronic devices that are still under development.

HASC Research into THz 

To address these challenges, the Hub in All Spectrum Connectivity (HASC)’s THz research portfolio includes device development, optical-wireless integration and network modelling. Some highlights include:

• THz–Optical Convergence: UCL researchers in HASC, in collaboration with the University of Duisburg-Essen and ACST GmbH, recently demonstrated the conversion of optical fibre signals into THz wireless links, achieving data rates up to 180 Gbps. This shows how existing fibre infrastructure could be seamlessly extended into the THz domain.
• Dark fibre experiments: HASC is investigating the potential of the THz spectrum using the UK’s EPSRC-funded ‘dark fibre’ network. This gives researchers hundreds of kilometres of real-world fibre to test new ideas, such as carrying and regenerating THz signals over long distances to explore how to link high-speed fibre networks with future THz wireless systems.
• Device innovation: A promising technology is the UTC-PD (Uni-Travelling Carrier Photodiode), which converts optical signals into electrical / THz signals. HASC researchers are developing new ways of housing and integrating ultra-fast UTC-PDs, so they deliver more power, handle higher speeds and work more reliably in THz transmitters and receivers.

HASC’s broader portfolio includes advances to provide the underlying capabilities needed to reliably generate, stabilise and harness THz signals within future 6G systems. These include developing methods to create ultra-high-frequency signals by combining laser sources, and designing advanced THz receivers that detect high-frequency signals efficiently and are compact enough for easy integration into real-world systems. Together, these efforts lay the groundwork for turning the promise of THz into practical, everyday 6G connectivity.

6G and Beyond

As the 2030 target for standardising 6G rapidly approaches, the next few years will see THz research move from controlled experiments to field-ready prototypes, allowing researchers to assess how THz can fit into the next-generation communications network. If the attractive qualities of THz are to support the vision for a robust, flexible future telecommunications network, it is vital that the challenges of THz propagation, regulation and device design are addressed.

Ultimately, no single technology will deliver 6G: instead, it is likely this will dynamically integrate THz, mmWave, microwave and legacy frequencies to balance coverage, mobility and capacity. For industry professionals and academics alike, the message is clear: keep watching the terahertz 6G research space. The next generation of wireless is being built now, and the exciting capabilities of THz are starting to take shape.

Want to stay involved? Keep an eye on our channels for updates, opportunities, and news from across the HASC community.

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The vision for next-generation connectivity

Each new mobile network generation has brought greater data capacity and faster downloads, and in this respect 6G will be no different. But the vision for future connectivity goes far beyond this. Instead of networks focused on transferring information from one point to another, the aim is for a system that embeds digital communications into the fabric of all areas of society – bringing unprecedented levels of connectivity, resilience and innovation.

Such a communications landscape would enable a far greater variety of nodes to come online, for instance autonomous vehicles and intelligent ‘Internet of Things’ devices. This will allow a wealth of new technologies to become mainstream, from smart cities and virtual healthcare, to immersive digital environments and even applications we can’t yet foresee.

HASC is a partner in the Federated Telecoms Hubs (FTH), working alongside hubs such as TITAN, CHEDDAR, and JOINER, to address this ‘grand challenge’ of connectivity. Our particular focus is on the underlying physical connections within this fabric.

The technology behind future connectivity

Achieving next-generation technology will require integrating both existing communications infrastructure and new domains into a single coherent architecture. This is expected to include:

• New frequency bands, including millimetre-wave (mmWave) and terahertz (THz) frequencies, enabling ultra-fast, short-range data transfer in dense environments.
• Advanced optical communications, using both fibre-optic cables and free-space laser links. These will provide an ultra-high-capacity backbone for data-intensive applications, such as data centres.
• Radio systems, which will continue to provide reliable, wide-area coverage and mobility.
• Artificial intelligence (AI) and machine learning to manage complexity and optimise performance dynamically.

Together, these elements form the foundation of HASC’s work on future connectivity: a spectrum-agnostic, adaptive network fabric that unifies the best of wired and wireless systems. Ultimately, this will ensure that the UK’s communications infrastructure of tomorrow is not only faster, but more intelligent, efficient, and secure.

How is HASC helping to bring next-gen connectivity forward?

Delivering next-generation connectivity is a multifaceted challenge, with hurdles to overcome across technology, regulatory and policy domains. To address these, HASC is leading a holistic research portfolio, spanning Modelling and Measurement, Connectivity, Adaptivity, and Security. In particular, the hub stands out for its work in both optical and radio frequency communications, with a goal of generating insight on how we can unite wired and wireless domains.

Three particularly promising new technologies that HASC are investigating are:

• Hollow-core optical fibre (HCF)

This next-generation fibre technology guides light through air rather than solid glass, which has the potential to substantially reduce the signal delay and distortion that limit today’s conventional single-mode fibres. HASC is investigating how these fibres can enable ultra-low-latency, high-bandwidth communication while also supporting new capabilities. In recent work, HASC researchers demonstrated that hollow-core and multicore fibres can carry both optical power and communications simultaneously, with the potential to improve the resilience of networks by providing ‘back up’ power.

• Integrating sensing and communications

One of the emerging frontiers in Future Connectivity is the fusion of sensing and communications into a unified system sharing spectrum, hardware and signals. HASC researchers are making the measurements that underpin some of this work.

• Quantum key distribution (QKD)

QKD can be used to improve network security by applying quantum mechanics to create cryptographic keys that are theoretically immune to eavesdropping. HASC researchers are investigating how to integrate QKD into both wired and wireless systems, to enable tamper-proof communications. In a recent study, they demonstrated secure data transmission using quantum encryption at speeds of up to 5 Mb/s over 25 kilometres of fibre. The system used an innovative approach where each receiver generated its own local reference signal, rather than sending one through the fibre – a design that makes the link far more secure against interception or tampering.

Other areas that HASC researchers are exploring include hybrid fibre–wireless links that combine the capacity of optical fibre with the flexibility of mmWave wireless; intelligent surfaces to boost the propagation of weakly-penetrating signals; optical wireless integration for high-capacity data transfer over short distances; and how AI can be applied to improve spectrum utilisation and network performance.

Breaking down barriers between academia, industry, and policy

The road to 6G and next-generation connectivity can be accelerated through close alignment between industry, policy professionals and academic researchers. A strong example is HASC partner Imperial College London co-chairing the European Telecommunications Standards Institute (ETSI)’s Industry Specification Group, collaborating with a range of companies including BBC and Viavi. This pre-standardisation forum focuses on developing future multiple access techniques for 6G standardisation.

Another case study is HASC’s collaboration with BT to develop Power-over-Fibre (PoF): a novel approach to delivering electrical power to communications equipment without relying on traditional copper cabling. As copper infrastructure is phased out in favour of all-fibre networks, the capability to support critical communications even during periods of local power outages is lost. PoF offers a promising alternative, enabling remote powering solutions in all-fibre communication systems. This work has already resulted in a series of demonstrations showing optical power delivery to remote equipment through the communication fibre, and several publications. The research is supported by BT, who developed the use cases motivating the investigation into PoF implementation solutions, sponsored a PhD studentship in this area, and loaned equipment for experimental network demonstrations.

Delivering the vision

Next-generation connectivity represents a paradigm shift in how we understand, design, and experience communication. Instead of treating modes as separate technologies, these will be united into a single, intelligent system.

 But there remain many unknowns. For instance, how can we diversify communications while reducing energy consumption and aligning with net zero targets? Can we leverage advances in quantum computing and integrate these into classical systems? How can we strengthen network security and resilience, for instance using new satellite capabilities?

These are difficult questions to address, and HASC is working together with our FTH partners to help answer them.

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Demand for wireless connectivity is rising exponentially. As requirements for speed, capacity and reliability grow, engineers and researchers are pushing beyond today’s mobile technologies to explore new frontiers in the electromagnetic spectrum. Two promising candidates for future wireless technologies are millimetre wave (mmWave) and the terahertz spectrum (THz). Both sit in the high-frequency wireless end of the spectrum and promise extraordinary capabilities, but they also bring challenges. Understanding the differences, and how they might work together, is a task that the Hub in All Spectrum Connectivity (HASC) is fully embracing.

What are mmWave & THz?

The electromagnetic spectrum is divided into frequency bands. The higher the frequency, the shorter the wavelength and the more data that can be carried, though this often comes with greater transmission challenges.

• mmWave typically refers to frequencies between around 24 and 300 GHz, with wavelengths measuring between 1 and 10 mm. mmWave is already being commercially deployed in US 5G networks (with the UK moving towards implementation) and is a core part of the discussion around 6G.
• THz generally refers to frequencies from 100 GHz up to 10 THz (10,000 GHz), with wavelengths between 3 mm and 30 µm. This range offers a very large bandwidth and high-capacity transmission, making it a key candidate for meeting the data demands of future network applications.

Since the ranges of mmWave and THz overlap, the boundaries between these are not always rigid, and their characteristics can sometimes blur.

The Similarities and Differences of mmWave & THz

As mmWave and THz are both high-frequency bands, they share many fundamental traits. These include:

• High transmission loss: signals weaken quickly as they travel, which limits range and requires more transmitters or repeaters.
• Poor penetration: walls, furniture and even human bodies can block or absorb the signal, making indoor coverage difficult.
• Line-of-sight requirement: reliable links often need a clear path between the transmitter and receiver, as signals do not bend easily around obstacles.
• Environmental sensitivity: conditions such as rain, humidity or atmospheric absorption can further reduce performance.

However, mmWave and THz also have key differences:

• Data rates: whilst mmWave could deliver high-speed connectivity to modern networks, the capabilities of THz far exceed this, with the potential for ultra-high data rates exceeding 100 Gbit/second.
• Propagation and loss: THz frequencies experience even higher propagation loss than mmWave. This means that while they can transmit vast amounts of data, their range is more limited.
• Penetration: both mmWave and THz have significantly lower penetration than radio frequencies. However, whilst mmWave can penetrate certain materials such as glass with manageable loss, THz waves penetrate much more weakly and are mostly restricted to direct, unobstructed paths.

In short: mmWave can travel further, while the THz spectrum can deliver more data over shorter distances.

Use Cases: Today and Tomorrow

mmWave today: mmWave is already finding its place in commercial systems. Telecoms are integrating mmWave into mobile networks to boost capacity and deliver faster wireless experiences. For users, this means higher speeds in dense urban centres, stadiums or transport hubs where data demand is extremely high.

Emerging THz applications: THz is currently much less commercially developed, but global interest and research is expanding rapidly. Potentially, THz in telecommunications could:

• Replace short stretches of fibre optic cable, especially in areas where fibre installation is impractical or costly (for instance, across rivers or challenging terrain), or where fibre networks have been damaged during disasters such as earthquakes.
• Enable short-range high-speed comms in data centres, where stable, ultra-fast links are essential.
• Support advanced applications such as holographic conferencing or virtual reality/ augmented reality environments.

The exciting part is how the two might work together: mmWave providing robust, wide-area coverage, while THz delivers extreme data rates for high-capacity, short-range applications.

Challenges and Innovation

The main challenge for both mmWave and THz is overcoming the physical limitations of high-frequency signals, in particular, the fact they lose power quickly and don’t diffract around or penetrate obstacles well. Researchers are tackling these hurdles on multiple fronts:

• Device engineering: building transmitters and receivers capable of generating and handling such high frequencies efficiently.
• Hybrid integration: combining THz wireless with existing optical fibre infrastructure to extend range and resilience.
• Algorithms and adaptation: designing systems that adapt dynamically to user movement and channel conditions, ensuring reliable connections even in difficult environments.

HASC Research Spotlight

At HASC, researchers are working to address key unknowns about the use of mmWave and THz in telecommunications. This has included measuring and modelling the performance of mmWave signals and their ability to deliver ultra-reliable WiFi in factory settings, and demonstrating the generation of precise THz signals by combining two different laser signals (known as photo-mixing) a technique which could reduce the implementation and operation cost of a THz communications system.

A strong focus is the integration of THz wireless with fibre networks to create seamless, end-to-end systems. For example, HASC researchers at UCL, in conjunction with German colleagues, have demonstrated a fully-optoelectronic 300 GHz wireless link, achieving up to 180 Gbps over 1.5 metres. This was done by mixing optical signals in order to generate and receive THz wireless signals. Such experiments show how fibre and THz wireless can be combined, paving the way for networks that are faster, more flexible and more efficient.

The Future: Complement, Not Compete

Looking ahead, it is unlikely that the future will be one of mmWave vs THz; instead of competing, these will complement one another:

• THz will excel in scenarios demanding extreme data rates over short distances, such as data centres or specialised industrial environments.
• mmWave will continue to support mobile users who need higher speeds than 4G/5G mid-bands can provide, while accommodating movement and broader coverage.

Together, mmWave and THz will form part of a flexible, multi-band ecosystem. This is central to HASC’s vision: an integrated network of wired and wireless systems, dynamically adapting to user needs. Through our research programmes and by brokering exchange between academia, industry and policy, HASC is working to accelerate the transition to a high-frequency wireless future.

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