Tuesday, 15 September 2026

Huawei LampSite Evolves to Five Bands in One Box

I first wrote about Huawei's LampSite solution on this blog back in 2014. At the time, digital indoor systems were emerging as an alternative to traditional Distributed Antenna Systems (DAS), with the idea of bringing the radio much closer to indoor users rather than simply distributing RF signals around a building.

Interestingly, Huawei was already talking about a future generation of LampSite capable of delivering 1 Gbps indoor throughput. At the time this looked ambitious, with technologies such as multi-stream aggregation being considered as part of the evolution. More than a decade later, LampSite has evolved considerably.

In June 2026, MTN Zambia and Huawei announced what they described as the world's first commercial deployment of a new five-band LampSite solution, installed at the Mulungushi International Conference Center in Lusaka.

The new system combines 1.8 GHz, 2.1 GHz, 2.3 GHz, TDD 2.6 GHz and 3.5 GHz in a single box, while supporting 2G, 3G, 4G and 5G. Huawei says the deployment uses 3.5 GHz 4T channels together with TDD 2.6 GHz carrier aggregation to provide a single-user downlink peak speed of around 1 Gbps.

So there is an interesting historical symmetry here. In 2014, the roadmap was pointing towards gigabit indoor connectivity. In 2026, Huawei is reporting a commercial LampSite network delivering that figure, although obviously the radio technology, spectrum and architecture used to achieve it are very different from what was envisaged twelve years ago.

The Zambia example also highlights why indoor networks remain challenging even when good outdoor 5G coverage is available.

According to Huawei, Mulungushi had previously relied partly on outdoor 3.5 GHz macro coverage, but building penetration meant that the macro network could not provide sufficiently strong indoor signals. The existing DAS was also unable to support the required multi-band configuration. Adding separate systems for different bands would have increased equipment complexity, deployment time and operational costs.

This is where digital indoor systems such as LampSite differ fundamentally from traditional passive DAS.

Rather than taking a high-power RF signal and distributing it through coaxial cables, splitters and antennas, LampSite distributes baseband or digitised signals towards small remote radio units located throughout the building. Huawei calls these pRRUs, or pico Remote Radio Units.

The principle itself is not new. The original LampSite architecture already used centralised baseband processing connected through rHUBs to distributed pRRUs. What has changed significantly is how much radio capability can now be integrated into those remote units.

When I looked at LampSite 3.0 in 2017, Huawei was already adding concurrent multi-band operation, more flexible carrier aggregation, distributed MIMO and 256-QAM. Huawei was claiming speeds of up to 2 Gbps under suitable configurations. LampSite 3.0 could also accept signals from an external base station, including equipment from another vendor, making the architecture more interesting for multi-operator and neutral-host deployments.

I returned to that subject in 2019 when looking at small cells and neutral-host networks. Huawei was then presenting LampSite Sharing as a digital indoor system in which multiple operators could share common infrastructure rather than each operator installing a completely independent indoor network.

By 2020, 5G LampSite had arrived. Huawei said that the product, originally launched in 2018, integrated 5G NR and LTE and could support both C-band and sub-3 GHz frequencies in a unit of less than two litres.

The latest five-band generation takes that consolidation considerably further.

Huawei says the Zambia pRRU combines all five required frequency bands into a single indoor radio head. This is significant because a multi-band indoor network would otherwise potentially require multiple radios, antennas or separate signal-distribution paths.

Huawei claims its optical multi-band architecture can reduce the number of indoor headends by up to 50% for equivalent coverage, reducing installation requirements and construction time. The solution also incorporates intelligent dormancy and symbol-level power-saving techniques, which Huawei markets as part of its "0 Bit 0 Watt" approach. These are Huawei figures rather than independent measurements from the Zambia deployment, but they illustrate the wider objective: increasing indoor capacity while reducing the amount of physical infrastructure needed to provide it.

There is another important development in 2026 that helps put the five-band solution into context.

At MWC Barcelona, Huawei launched LampSite iSharing, its latest approach to shared indoor networks. The company describes iSharing as a digital-analogue hybrid architecture designed for both single-operator and multi-operator deployments.

Three elements are particularly interesting:

  • The first is the five-band integrated pRRU.
  • The second is what Huawei calls the Multi-mode Extended Hybrid Card, or MEHC, which combines digital, RF and data access.
  • The third is RRU-free BBU digital feed-in. In other words, an operator can potentially feed the shared indoor infrastructure directly from its baseband equipment rather than first needing a dedicated radio unit to generate an RF signal that is then fed into the sharing system.

Independent reporting from MWC adds some useful detail. GlobalData Network Matter reports that the MEHC enables as many as three operators' BBUs to connect directly to a distributed control unit for signal aggregation, allowing a neutral-host deployment to avoid separate radio-end equipment for each participating operator.

This is potentially just as important as putting five bands into one pRRU.

The evolution can therefore be seen at both ends of the indoor network. At the signal-source end, more of the equipment required by multiple operators can be consolidated. At the coverage end, more frequency bands and radio technologies can be combined into fewer physical remote units.

Huawei's current DBS5900 LampSite Sharing portfolio gives an indication of how this architecture fits together. It combines elements including the DCU, rHUB, pRRU and hRRU, and supports both BBU digital feed-in and traditional RF feed-in. Huawei's DCU5910, for example, is specified to support both methods. The rHUB then provides distribution towards the remote indoor radios.

The publicly listed pRRU5755 provides an example of how much radio functionality can now be integrated into a single unit. It supports 900, 1800, 2100, 2600 and 3500 MHz, with 4T operation available at 3.5 GHz. Other pRRU variants support different combinations according to market requirements.

It is worth noting that Huawei has not publicly identified the exact pRRU model used by MTN Zambia, and the published pRRU5755 frequency combination is not the same as the Zambia configuration, which includes 2.3 GHz rather than 900 MHz. It would therefore be wrong to assume that the Zambia installation is simply using the currently published pRRU5755 configuration.

Likewise, although Huawei's LampSite iSharing launch and the Zambia deployment clearly share the same broader direction towards five-band integration and simplified indoor architecture, Huawei's Zambia announcement does not explicitly identify the deployment as LampSite iSharing.

The broader point is that the indoor mobile network is steadily becoming less like a passive extension of the outdoor macro network.

Traditional DAS remains widely used and can be very effective, particularly where multiple operators and frequencies need to share an established antenna system. But adding higher frequencies, wider bandwidths, MIMO, multiple RATs and eventually more advanced 5G-Advanced capabilities makes purely passive distribution increasingly challenging.

Digital indoor systems move more of the radio intelligence towards the edge of the distribution network. They also make capacity easier to manage independently across different parts of a building and provide greater flexibility when new frequencies or technologies need to be introduced.

Huawei is also positioning LampSite for further evolution towards 5G-Advanced, including services such as high-precision positioning, AIoT and immersive applications. The Zambia announcement mentions future use cases such as XR, AR navigation and glasses-free 3D, although these should be seen as potential applications enabled by future network evolution rather than services demonstrated by the current deployment.

Perhaps the most interesting aspect of the latest LampSite evolution is therefore not the headline 1 Gbps speed, or even the fact that five bands fit into one box. It is the continuing consolidation of indoor mobile infrastructure.

What started as a way of replacing long RF distribution paths with digital connections and small indoor radio units has progressively added multi-band operation, MIMO, 5G, neutral-host capabilities, digital sharing and now five-band integrated radios with more flexible multi-operator signal sources.

For large venues, airports, transport hubs, shopping centres and other locations where several generations of mobile technology and multiple spectrum bands need to coexist, reducing that infrastructure complexity may ultimately prove more important than the headline peak speed.

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Tuesday, 25 August 2026

Rome 5G Shows How Neutral Host Infrastructure Can Modernise a Historic City

Deploying better mobile connectivity in a modern city is rarely straightforward. Doing it in Rome, where much of the historic centre is protected and almost every street seems to contain something of archaeological or architectural significance, takes the challenge to another level.

This makes the Roma 5G project particularly interesting from a telecoms infrastructure perspective. Rather than relying simply on more conventional macro sites, the project combines neutral host infrastructure, small cells, fibre, Distributed Antenna Systems (DAS), Wi-Fi and IoT infrastructure to improve connectivity while minimising the impact on the city itself.

The project was originally led by Boldyn Networks through a 25-year public-private partnership with Roma Capitale. The original plan included more than 2,200 small cells, public Wi-Fi across 100 squares, around 1,800 IoT modules, 2,000 connected cameras and extensive mobile coverage across the Rome Metro.

One of the biggest challenges is obvious when walking around central Rome. Large towers and conspicuous new infrastructure are simply not suitable in many areas. The Boldyn case study explains that existing urban infrastructure, including lamp posts and bus shelters, could instead be used as mounting locations for small cells. This allows capacity to be placed closer to users while keeping the radio equipment relatively unobtrusive.

This is an increasingly important aspect of mobile network densification. In very busy locations, improving capacity is not necessarily about building another large macro site. A layer of smaller radio nodes, supported by fibre and integrated with existing street furniture, can provide capacity exactly where it is required.

Rome provides an unusually demanding example of this approach because infrastructure deployment also has to coexist with archaeological restrictions, visual-impact requirements and huge numbers of residents and visitors.

The project goes beyond outdoor small cells. The Metro is another major part of the connectivity problem.

The original project envisaged 4G and 5G coverage through the stations and tunnels, creating a shared infrastructure that could be used by the mobile operators. The network uses a multi-operator Distributed Antenna System, with fibre and radio infrastructure carrying the operators' services through the underground environment.

This is important because neutral host infrastructure avoids every operator having to independently install essentially the same physical infrastructure. A shared fibre, DAS and antenna layer can support multiple operators while reducing duplication, construction work, cost and disruption.

The same principle applies above ground. The infrastructure is designed to be open to mobile network operators rather than acting as another competing mobile network. Neutral hosting effectively separates ownership of much of the physical connectivity infrastructure from the retail mobile services running over it.

This is arguably one of the most interesting lessons from Roma 5G.

As networks become denser, particularly in city centres, transport systems, stadiums, airports and other high-footfall locations, it becomes increasingly difficult to justify every operator installing a completely separate set of infrastructure. Shared infrastructure can provide a more practical way of achieving the required density.

The project has evolved considerably since Boldyn published its original case study.

In October 2024, INWIT acquired a controlling 52.08% stake in Smart City Roma, the project company originally known as Boldyn Networks Smart City Roma. Boldyn remained involved in the infrastructure build-out, while Smart City Roma became part of the INWIT Group.

Deployment has also moved from plans into operational infrastructure.

In April 2025, the first nine Metro A stations were activated with multi-operator 4G and 5G coverage, including connectivity through the tunnels between the stations. The infrastructure supports Fastweb-Vodafone, Iliad, TIM and WINDTRE.

By September 2025, coverage had been completed across all 27 stations and tunnels on Metro Line A, using more than 85 km of fibre and around 1,500 mini-antennas. At the same time the city was progressively activating Wi-Fi 6, cameras and other smart-city infrastructure across Rome's public squares.

The rollout has continued during 2026. INWIT reported that by the end of June, work had been completed across Lines B and B1, reaching 48 Metro stations overall, while deployment was under way at seven stations on Line C.

The outdoor network has also continued expanding. By March 2026, infrastructure including public Wi-Fi, IoT systems, cameras and small cells had reached 92 squares, and INWIT subsequently reported free Wi-Fi connectivity across 100 Rome squares during summer 2026.

There is an important distinction here between connectivity infrastructure and smart-city applications.

The network does not automatically make Rome a smart city. What it provides is the digital foundation on which applications can be built. The original plan included connected cameras, environmental sensors and other IoT systems, potentially supporting areas such as traffic management, public safety, environmental monitoring and waste management.

The diagram in the Boldyn case study provides a good summary of this layered approach. It combines indoor coverage for buildings, street-level sensors and cameras, outdoor small-cell coverage, Wi-Fi 6 in public squares, fibre connectivity and 4G/5G DAS throughout the Metro.

The result is therefore much more than a conventional mobile-network rollout.

It is effectively a shared digital infrastructure layer for the city, with different access technologies being used depending on the requirement: macro mobile where appropriate, small cells for street-level capacity, DAS underground, Wi-Fi for public access, fibre for transport and backhaul, and IoT connectivity for municipal applications.

The recently released Threads of Connectivity documentary, featured on National Geographic's digital platforms, looks at the challenge of bringing modern connectivity into two cities whose infrastructure was built long before anyone imagined mobile networks: Rome and New York. The Rome segment is particularly interesting because much of the successful infrastructure is deliberately designed not to be noticed.

That may ultimately be one of the most important lessons from Roma 5G.

As mobile networks continue to densify, the infrastructure supporting them may increasingly need to disappear into the environment rather than dominate it. Small cells integrated into street furniture, shared fibre, multi-operator DAS and neutral-host models make it possible to add significant network capacity while reducing duplication and visual impact.

Rome may be an extreme example because of its history and architecture, but the underlying problem is common to cities everywhere.

The future of urban mobile infrastructure may therefore be less about building more obvious mobile sites and more about creating a dense, shared and largely invisible connectivity layer woven into the city itself.

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Tuesday, 4 August 2026

Vodafone’s Robotic Antenna Trial Brings Physical AI to the Mobile Mast

In Tirana, Albania, Vodafone is testing an approach to mobile network optimisation that goes beyond software: physically rotating and tilting a mobile mast antenna using an AI-controlled mechanical positioning unit supplied by HUMAX Networks.

Vodafone describes the trial as an early example of “Physical AI” in telecoms. Its algorithm analyses network demand and local conditions, decides where coverage is most needed and instructs the mechanism to reposition the antenna. The company says the system can also consider factors such as the weather when determining where to focus the strongest signal.

The trial site overlooks a busy shopping centre. Vodafone’s example is that the antenna can focus capacity towards shoppers during the day and then redirect coverage towards a nearby residential area as evening internet usage increases. Repositioning currently takes around 20 to 30 minutes. Vodafone says it could technically happen faster, but the movement has been deliberately slowed to reduce energy consumption.

The mechanism is not the large industrial robotic arm that the announcement’s headline might bring to mind. It is a compact antenna-positioning system installed between the panel and its supporting structure.

How the HUMAX OCRA system works

The equipment comes from HUMAX Networks and forms part of its OCRA solution, short for Optical Compass Robot Arm. The Optical Compass measures the antenna’s azimuth to establish its physical orientation, while the Robot Arm makes fine-grained rotation and tilt adjustments. An optional OC Controller can provide communication between OCRA and the operator’s server.

For the Vodafone trial, HUMAX says Vodafone’s AI-based system analysed real-time network conditions at the site, identified the preferred antenna orientation and instructed the Robot Arm to adjust the panel accordingly. HUMAX positions the technology as a way of responding more flexibly to changing traffic conditions while reducing the need to dispatch engineers to sites.

OCRA itself is not entirely new. HUMAX has previously described deployments associated with stadiums, major public events and Urban Air Mobility trials in South Korea. Its broader system can incorporate optical azimuth measurement, a controller and physical antenna alignment.

But remote antenna adjustment is not new

It is important to distinguish Vodafone’s trial from the remote antenna-control capabilities already used in mobile networks.

Operators have been using Remote Electrical Tilt, or RET, controlled through Antenna Interface Standards Group (AISG) interfaces, for many years. RET changes the vertical direction of the radio beam by adjusting components inside the antenna. The antenna panel, its mounting brackets and its physical bearing on the mast remain fixed.

AISG specifications also cover antenna functions such as Remote Azimuth Steering and Remote Azimuth Beamwidth. These can alter aspects of horizontal coverage in suitably designed antennas without mechanically rotating the complete antenna panel. The current AISG standards catalogue includes RET under AISG 3.0, while RAS and RAB remain listed among the AISG 2 extension specifications.

The difference is therefore not simply remote adjustment versus manual adjustment. It is electrical adjustment within a fixed antenna installation versus physical repositioning of the complete antenna panel.

The publicly available descriptions of Vodafone’s trial do not indicate that AISG is controlling the OCRA mechanism. Vodafone’s AI system appears to instruct the HUMAX positioning system through its own control arrangement. The antenna may separately support RET, but Vodafone does not identify this as part of the trial.

This also means that OCRA should not necessarily be considered a replacement for RET. The two approaches address different ranges and types of change.

Electrical tilt is well suited to fine-grained optimisation within the adjustment range designed into an antenna. Physical repositioning can change the antenna’s mechanical boresight and potentially redirect the sector towards a substantially different area.

Why physically move the antenna?

The most obvious advantage is the ability to redirect a sector without sending an engineer to realign the antenna manually.

Vodafone notes that a conventional physical adjustment may involve more than an engineer climbing a mast. Depending on the site, it can require access agreements, road closures, lifting equipment, planning permission and suitable weather conditions. Installing another antenna can take even longer.

A remotely controlled mechanical system could therefore be useful where the geographical distribution of demand changes significantly over time. Possible examples include:

  • Daily demand shifts: Shopping, business and residential districts with contrasting traffic patterns.
  • High-density events: Stadiums, festivals and other large temporary gatherings.
  • Seasonal demand: Coastal towns, ski resorts and other destinations with large seasonal variations.
  • Infrastructure disruption: Temporary coverage changes during major construction or transport disruption.
  • Aerial coverage: Urban Air Mobility corridors and drone routes requiring coverage above ground level.

These are not all use cases demonstrated in Vodafone’s Albania trial, but they follow from HUMAX’s previous work around high-density events and Urban Air Mobility, as well as the system’s ability to change physical azimuth and tilt.

There are also practical questions that would need to be considered before wider deployment. Moving equipment introduces mechanical components that must withstand wind, rain, temperature changes and repeated operation over many years. Operators would need to understand maintenance requirements, energy consumption, movement accuracy and the consequences of a mechanism becoming stuck in an unsuitable position.

Any significant sector redirection would also need to be coordinated with neighbouring cells. Moving one antenna can change interference patterns, coverage overlaps, handover boundaries and traffic distribution across the surrounding network. The AI decision cannot therefore be based only on demand at an individual site. It needs visibility of the wider RAN and suitable performance guardrails.

The bigger story is closed-loop network automation

The robotic mechanism is visually interesting, but the more important development is the closed loop connecting network intelligence with physical infrastructure.

Vodafone’s algorithm observes conditions, determines the preferred orientation and instructs the antenna system to act without waiting for a human decision. That moves network automation beyond changing software parameters and into controlling the physical configuration of the site.

Vodafone says the project is also intended to feed into its wider open, standards-based platform for automating RAN components. This could allow internal teams and third-party developers to create optimisation algorithms that treat antennas as active elements of an adaptive RAN.

In future, an autonomous system could potentially select between several optimisation options. A small coverage correction might be achieved using electrical tilt. A change in beam shape or horizontal steering might be possible within the antenna. A more substantial redirection could justify physically repositioning the panel.

Vodafone has not said that the Tirana system currently selects between all these methods. However, the trial points towards networks in which AI has a growing range of electrical and physical controls available to it.

Whether robotic antenna positioning becomes common will depend on the operational and commercial results. Operators will need to determine how often large physical changes are genuinely required, whether the performance gains justify the additional hardware and how reliably the mechanism operates over the full life of a mobile site.

Nevertheless, the trial is a useful reminder that autonomous networks will not be limited to software running in a data centre. Increasingly, intelligence may also control what happens at the top of the mast.

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Tuesday, 14 July 2026

Inside a Cellular Base Station Antenna

Most people will have seen the familiar white rectangular antenna panels mounted on telecom towers, rooftops and other structures. They are a visible part of the mobile network, but what is actually inside one of these panels is rarely seen.

In a recent Wireless Future video, Prof. Emil Björnson carries out a teardown of a cellular base station antenna that was discovered in a box when his research division moved offices. The antenna operates in the 3.5 GHz band, which is widely associated with 5G deployments, but it is important to understand that this is not specifically a ‘5G antenna’.

It is a passive sector antenna. It can be used with either 4G or 5G, depending on the radio signal connected to it.

The antenna is designed to cover a 60-degree sector and would be installed vertically using mounting points at the top and bottom. A conventional macro site may use multiple sector antennas pointing in different directions to provide coverage around the site.

At the bottom of the antenna is a radio frequency connector through which the transmitted and received signals pass. The antenna does not create or process the radio signal itself. The signal is generated by an external radio unit and carried to the antenna through a feeder cable.

There is also a small drain hole at the bottom of the enclosure. Although antenna panels are designed to operate outdoors, condensation or small amounts of water may still enter the enclosure. The drain allows this moisture to escape rather than accumulating inside.

After removing the enclosure, the internal antenna structure becomes visible. The antenna contains eight radiating elements arranged vertically. Each of these elements is a dipole antenna.

The radio signal enters through the connector and is distributed through a feed network to all eight elements. The elements transmit the same signal coherently. Their individual radio waves combine through constructive interference to produce a stronger and more directional radiation pattern.

The physical dimensions of the antenna are closely related to the wavelength of the radio signal. At around 3.5 GHz, one wavelength is approximately 8.5 centimetres. Each dipole is therefore around half a wavelength long, or approximately four centimetres.

Behind the dipoles is a metal reflector positioned approximately a quarter of a wavelength away, which is around two centimetres at this frequency. The reflector reduces radiation behind the antenna and helps direct more of the energy forwards.

Metal structures along the sides of the antenna further shape the horizontal radiation pattern, limiting the main coverage area to approximately 60 degrees. Instead of transmitting energy equally in every direction, the antenna concentrates it towards the geographical area that the sector is intended to serve.

The eight dipole elements are separated vertically by approximately eight centimetres, which is roughly one wavelength. Arranging the elements in a vertical column produces a narrow beam in the vertical, or elevation, plane.

This is useful because a base station normally needs to transmit signals across the surrounding area rather than towards the sky or directly into the ground. The horizontal beam provides coverage across the 60-degree sector, while the narrow vertical beam concentrates the radio energy towards users within the intended coverage area.

According to the antenna specifications shown in the video, it has a gain of 17.5 dBi. This corresponds to a peak power density roughly 56 times greater than that of an ideal isotropic antenna transmitting the same total power.

The antenna is not amplifying or creating additional power. Antenna gain is achieved by concentrating the available radio energy in particular directions while reducing the energy transmitted in others. This improves coverage and signal strength within the sector.

The maximum input power shown in the specifications is 150 watts. The data sheet also includes horizontal and vertical radiation patterns, mounting instructions and information about wind loading. These mechanical considerations are important because antennas must remain safely mounted and correctly aligned while exposed to wind, rain and changing temperatures.

The antenna was manufactured by the Italian company Sira Sistemi Radio. Its relatively simple internal construction demonstrates how passive base station antennas have traditionally worked. One radio signal is distributed across several antenna elements to create a fixed directional radiation pattern.

The same antenna could carry a 4G or 5G signal because the antenna itself does not understand the mobile technology being used. Provided that the radio frequency falls within its supported operating range, it simply converts electrical RF signals into electromagnetic waves and performs the reverse process when receiving signals.

This is different from many modern 5G Massive MIMO antenna systems.

In the passive antenna shown in the video, the eight radiating elements collectively behave as one large directional antenna. They receive the same input signal and produce a largely fixed beam.

An active Massive MIMO antenna has many individually controlled antenna elements or subarrays, together with multiple radio frequency chains. By adjusting the phase and amplitude of the signals supplied to different elements, the system can electronically form and steer beams, serve several users simultaneously and adapt the radiation pattern as network conditions change.

Modern active antenna units may also integrate the radio electronics into the antenna enclosure. This reduces feeder losses and allows closer coordination between the radio and antenna functions, although it also makes the equipment more complex than the passive panel examined in the teardown.

The video provides a useful reminder that an antenna is not simply an empty white box. Its dimensions, element spacing, reflector, feed network and surrounding metal structures all contribute to how radio energy is transmitted into the coverage area.

It also illustrates the progression of cellular infrastructure. Traditional passive sector antennas use carefully designed physical structures to create a fixed coverage pattern. Modern active antenna systems build on the same electromagnetic principles but add multiple radios, digital signal processing and dynamic beamforming.

The complete teardown and explanation from Prof. Emil Björnson can be viewed in the video below.

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Thursday, 25 June 2026

NTT's Research and Development towards Sustainable Infrastructure

When we talk about sustainable telecoms infrastructure, the conversation often jumps straight to energy consumption, carbon emissions, renewable power or more efficient network equipment. These are all important, but the May 2026 issue of NTT Technical Review reminds us that sustainability also has a very physical dimension. It is about the towers, poles, ducts, maintenance holes, conduits, closures, cables, covers, coatings and materials that quietly support communications networks for decades.

This is a timely topic because many countries are facing the same infrastructure challenge. Assets built decades ago are ageing at the same time that maintenance budgets, skilled workers and inspection capacity are under pressure. The NTT articles mention major infrastructure incidents in Japan in 2025, including sewer pipe collapses and ruptured water conduits, as examples of what can happen when ageing infrastructure and limited maintenance resources collide. The point is not that these assets were neglected, but that even well-managed infrastructure can become difficult to sustain when deterioration accelerates and resources become constrained.

For telecoms, this matters because modern networks are only as reliable as the physical infrastructure that supports them. Fibre, mobile base stations, switching equipment and transmission systems all depend on civil infrastructure. Underground ducts, maintenance holes, steel towers, poles and bridge-mounted facilities may not be as exciting as 5G Advanced, AI-RAN or 6G, but they are essential to service continuity, resilience and safety.

NTT’s approach to sustainable infrastructure is interesting because it is not limited to one technology. It defines sustainable infrastructure around four requirements: safety, economy, resource recycling and worker satisfaction. Safety means avoiding accidents and ensuring that maintenance workers can operate safely, including in enclosed spaces or at height. Economy means keeping maintenance costs low enough for assets to remain viable over the long term. Resource recycling brings in the circular economy, including reuse and recycling of equipment and materials. Worker satisfaction recognises that even with robots, AI and automation, human workers will remain essential, so maintenance needs to be practical, efficient and less burdensome.

The framework presented by NTT divides R&D into four areas. The first is maintenance, or changing the present, by improving existing maintenance work and extending service life. The second is sensing, or knowing the present, by detecting the condition of infrastructure more efficiently and ideally remotely. The third is prediction, or knowing the future, by forecasting how infrastructure will deteriorate in different environments. The fourth is design, or changing the future, by using the knowledge gained from deterioration prediction to create longer-lasting, easier-to-maintain and more recyclable infrastructure.

This way of thinking is useful because it shows that sustainability is not just about replacing old assets with new ones. Renewal buys time, but the new infrastructure will also deteriorate eventually. The real challenge is to understand degradation mechanisms, detect deterioration early, repair at the right time, and design future infrastructure so that it needs less maintenance in the first place.

One of the more practical examples is NTT’s work on smart maintenance for steel towers. Steel towers are exposed to wind, rain, humidity, salt and other environmental factors, and rust can affect long-term structural integrity. Traditional rust removal using power tools, metal brushes or sandblasting can be labour-intensive, difficult in narrow spaces and challenging around bolts. NTT is investigating laser-based rust removal as a smaller, lighter and lower-recoil alternative that could also be combined with robotics and AI in the future.

The clever part is that the laser is not only being treated as a tool for removing rust. NTT is also studying how laser irradiation changes the steel surface itself. If the process can form a stable oxide layer and improve paint adhesion, it may help suppress rust recurrence and extend repair intervals. That would reduce both labour requirements and maintenance costs. The work combines practical surface preparation with deeper materials science, including first-principles calculations and machine-learning-based molecular dynamics to understand how iron oxides form during rapid heating and cooling.

Another important area is corrosion prediction inside maintenance holes. NTT owns around 680,000 communication maintenance holes in Japan, and these spaces house fittings that support communication cables. Maintenance holes can be humid, nearly sealed environments where rainwater or groundwater enters and stagnates. Depending on the water level, metal fittings may alternate between submerged and high-humidity conditions, creating complex corrosion behaviour.

The article on corrosion deterioration focuses on metal fittings inside maintenance holes, including the local corrosion that can occur where a communication cable is secured by string. Standard salt-spray and cyclic corrosion tests are useful, but they do not always reproduce the exact corrosion behaviour found inside a maintenance hole. NTT therefore studied an air/solution alternating test, which better simulates the repeated wet and air-exposed state inside these environments. The research showed that this test could reproduce local corrosion directly under the string-contact section, making it more relevant for understanding deterioration in real facilities.

This is where the move from periodic inspection to condition-based maintenance becomes important. If operators can predict which maintenance holes or components are at higher risk, they can inspect those earlier, while extending inspection intervals for lower-risk assets. That is a far better use of limited maintenance resources than treating every asset in the same way.

Plastic materials are another area that does not receive enough attention in telecoms infrastructure discussions. Plastics are used in cable sheathing, branch cable covers, closure housings and bundling materials. They are lightweight, easy to form, electrically insulating and corrosion resistant, but they can degrade outdoors due to light, heat, water and stress. Ultraviolet light can trigger photooxidation, heat can accelerate chemical reactions, water can leach out additives, and mechanical stress can help microcracks grow into larger cracks.

NTT’s work on accelerated ageing tests for plastics, using polypropylene as an example, is about reproducing real degradation mechanisms more quickly without creating unrealistic failure modes. This distinction is important. It is easy to make a test harsher, but a harsher test is not automatically a better test if the degradation mechanism no longer matches what happens outdoors.

The researchers are therefore looking at chemical and physical indicators, such as carbonyl index measured by FT-IR, oxidation induction time measured by methods such as chemiluminescence, and mechanical properties such as tensile strength and fracture strain. They are also looking at test cycles that combine UV, heat, water and stress, as well as warm-water immersion to accelerate additive leaching. This kind of work can help identify materials with better weather resistance and support infrastructure with longer service life.

The final article broadens the discussion from telecoms infrastructure to social infrastructure. NTT’s Civil Systems Project has long worked on cable tunnels, maintenance holes, conduits and bridge-mounted facilities. The historical evolution is notable: in the 1970s and 1980s, the focus was on product development and construction methods; after the Great Hanshin-Awaji Earthquake in 1995, seismic performance became a priority; now, with ageing assets, the focus has shifted towards efficient and sustainable maintenance.

A simple but effective example is the Tapered DIAmond Iron Cover for maintenance holes. Its surface pattern changes visually as it wears, allowing inspectors to judge wear more easily without measuring groove depth. Its design also improves abrasion resistance and extends the replacement cycle to around three times that of the previous design. This is a good reminder that innovation in infrastructure is not always about advanced AI or robotics. Sometimes, better physical design can make inspection easier, reduce lifecycle costs and extend asset life.

That said, AI does play an important role. NTT has developed image-based diagnostic technologies that can inspect, diagnose and predict deterioration. One example is technology that predicts the future progression of steel corrosion from images of infrastructure facilities, such as road bridges. By combining images with environmental data, the model can generate predicted images showing how corrosion may spread. In verification using telecommunications conduit facilities attached to road bridges, the technology predicted the increase in corrosion area several years ahead with an average error of less than 10%.

NTT is also applying telecoms infrastructure know-how to wider social infrastructure, including roads, bridges, tunnels, water and sewage systems. Using accumulated facility data, it has built AI models to estimate damage risk from disasters such as earthquakes, heavy rainfall and flooding. The article also highlights the use of synthetic aperture radar satellite data to detect early signs of underground cavities before surface damage becomes visible. This could allow wide-area screening of roads and help reduce the cost and labour associated with traditional ground-penetrating radar inspections.

There is an important lesson here for the telecoms industry. Network sustainability cannot be measured only at the level of watts per bit or carbon emissions from active equipment. Those metrics matter, but they do not capture the full lifecycle of infrastructure. A sustainable network also needs long-lived materials, efficient inspection, predictive maintenance, safer working methods, lower lifecycle cost, and better reuse and recycling.

This will become even more important as networks evolve. 5G, 5G Advanced and future 6G systems will require dense, distributed and resilient infrastructure. Edge computing, fibre densification, small cells, private networks, non-terrestrial connectivity and AI-native operations all depend on physical assets that must be deployed, protected, inspected and maintained. The more digital the network becomes, the more important the physical layer of infrastructure remains.

NTT’s May 2026 feature articles are therefore a useful reminder that sustainable infrastructure is not a single technology area. It sits at the intersection of materials science, sensing, AI, robotics, civil engineering, chemistry, laser technology, satellite monitoring and practical field operations. It also shows that telecoms infrastructure expertise can be valuable beyond telecoms, especially as wider social infrastructure faces similar ageing, resilience and maintenance challenges.

The future sustainable network will not just be more energy efficient. It will also be easier to inspect, safer to maintain, smarter at predicting deterioration, built from better materials, and designed with the full lifecycle in mind. That may not sound as glamorous as the latest radio interface or AI breakthrough, but without it, the networks we rely on every day cannot remain reliable, resilient or truly sustainable.

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Tuesday, 2 June 2026

MatSing Brings Lens Antenna Innovation to High-Density Wi-Fi Networks

As wireless connectivity demands continue to grow in stadiums, arenas and large public venues, network designers face an increasingly difficult balancing act. Fans expect seamless access to video streaming, social media, mobile ticketing, digital payments and a growing range of digital services, yet delivering the required capacity often means deploying hundreds or even thousands of antennas and access points throughout a venue. At MWC Barcelona 2026, MatSing unveiled a new approach to this challenge with the introduction of its MS-16.16W45 Wi-Fi 6E lens antenna.

MatSing is already well known within the wireless industry for its distinctive spherical lens antennas that have been deployed in major sports and entertainment venues around the world. Unlike conventional antenna designs that rely on reflection or electronic beam steering, MatSing's patented RF lens technology uses refraction to focus and direct radio signals. The concept is often compared to the way an optical lens focuses light, allowing multiple highly focused beams to be generated from a single antenna structure.

According to the company, the new MS-16.16W45 supports 16 independent beams and 4x4 MIMO operation across the Wi-Fi 6E spectrum from 5.125 GHz to 7.125 GHz. It is designed to provide coverage for thousands of simultaneous users from a single mounting location, reducing the amount of infrastructure required while simplifying deployment and maintenance.

What makes this announcement particularly interesting is that it applies a proven cellular deployment model to Wi-Fi networks. Over the past decade, MatSing's lens antennas have been used to address capacity challenges in large venues and outdoor events where traditional antenna architectures can struggle to scale efficiently. One of the company's early successes came at the Coachella music festival, where extremely high user density and heavy data usage created significant connectivity challenges. By using its lens antenna technology, MatSing was able to create dozens of sectors from a single installation point, providing the capacity required to support large numbers of connected users.

The same principle is now being applied to Wi-Fi. Large venues often require extensive Wi-Fi infrastructure, with antennas and access points distributed throughout seating areas, concourses and hospitality spaces. While this approach can deliver the required coverage and capacity, it also increases complexity, installation costs and ongoing maintenance requirements. MatSing argues that a small number of centralised lens antennas can create multiple high-capacity sectors while reducing antenna clutter and simplifying the overall network architecture.

Another interesting aspect of the technology is its ability to support multiple frequency bands and deployment scenarios. The company's lens antenna portfolio already supports cellular technologies including LTE, 5G and C-band deployments. The addition of a dedicated Wi-Fi 6E solution reflects the increasingly converged nature of venue connectivity, where cellular and Wi-Fi networks work together to provide a seamless user experience.

The announcement also highlights an area of wireless innovation that often receives less attention than advances in software, artificial intelligence and radio algorithms. Much of the industry's recent focus has been on improving network intelligence and optimisation, while the physical infrastructure used to focus and distribute radio signals has changed relatively little. MatSing's approach demonstrates that significant gains can still be achieved through innovation at the antenna level, particularly in environments where capacity and coverage requirements continue to grow year after year.

As venues prepare for increasingly connected fan experiences and the transition towards Wi-Fi 7 and future wireless technologies, infrastructure efficiency will become even more important. Whether MatSing's Wi-Fi lens antenna achieves the same level of success as its cellular counterparts remains to be seen, but its debut at MWC Barcelona 2026 offers an interesting glimpse into how high-density wireless networks may evolve in the years ahead.

Here is a good video from RCRTech's Principal Analyst, Sean Kinney, talking to Leo Matytsine, EVP and Cofounder of MatSing at MWC Barcelona 2026:

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Tuesday, 12 May 2026

Can Physical AI Finally Justify MEC and AI-RAN?

The telecoms industry has spent years searching for a use case that genuinely requires edge computing, ultra-low latency and a more intelligent RAN. Multi-access Edge Computing (MEC) has long been presented as a foundational element of future network architecture, yet many early deployments struggled to move beyond limited trials and niche applications. AI-RAN has followed a similar path. While the concept has generated significant industry interest, many operators have continued to question where the real commercial and technical value lies.

Recent demonstrations by SoftBank and Ericsson around network-enabled Physical AI suggest the industry may finally have found a use case that naturally brings these technologies together.

Unlike earlier MEC demonstrations focused on content delivery, video optimisation or traffic offload, Physical AI introduces a closed-loop interaction between robots, AI inference and the network itself. Rather than treating AI as an application that operates independently on top of the network, SoftBank is positioning AI as something that must be tightly integrated with connectivity, compute and radio infrastructure. This has important implications for MEC, distributed compute and the future evolution of telecoms infrastructure.

The concept behind Physical AI is relatively straightforward. Autonomous robots, drones and industrial systems need to perceive their environment, process information and make decisions in real time. Some AI inference can run locally on the device, but onboard compute is often constrained by size, thermal limits, cost and power consumption. More advanced AI models may require significantly more processing capability than can realistically be embedded into every endpoint.

SoftBank’s approach is to treat MEC as an extension of the robot’s compute environment. The network effectively becomes part of the control loop rather than simply acting as a transport layer between devices and the cloud.

This model requires a much tighter relationship between the RAN and application workloads than traditional mobile networks were originally designed to support. In conventional architectures, the RAN largely operates independently of application behaviour. SoftBank’s AI-RAN architecture, developed in collaboration with Ericsson, instead monitors radio conditions, compute availability and application requirements simultaneously. AI workloads can then move dynamically between the device and MEC infrastructure depending on latency, congestion and processing demands.

Ericsson’s role is particularly important because it demonstrates how differentiated connectivity becomes part of the AI execution framework itself. Network slicing, priority handling and RAN automation are integrated into the orchestration process so that the network can continuously adapt to changing AI workload requirements rather than relying on static QoS policies.

A robot performing real-time perception and navigation may initially process workloads locally. If additional compute power is required, inference tasks can be offloaded to nearby MEC resources. If network conditions deteriorate or latency increases, workloads may shift back onto the device. The network continuously orchestrates these decisions while attempting to maintain predictable, deterministic performance levels.

This is significantly different from the fixed slicing models often discussed during the early 5G era. Instead of static policies, the network dynamically adjusts connectivity and workload placement according to application behaviour and infrastructure conditions.

SoftBank’s broader strategy reinforces this direction through its Telco AI Cloud architecture, which combines GPU infrastructure, MEC platforms and RAN intelligence under a unified orchestration framework. The objective is to support AI workloads that span cloud, edge and endpoint devices across sectors such as industrial automation, logistics and construction.

A critical milestone in this development is the interworking between SoftBank’s AITRAS orchestrator and the Ericsson Intelligent Automation Platform (EIAP). This collaboration reflects a future where AI orchestration platforms and RAN automation systems operate through tightly integrated control and policy frameworks. By integrating these layers, the orchestrator can make informed decisions about where to place an AI workload based on a real-time view of the RAN’s capacity and health.

This shift highlights why many previous MEC deployments struggled to achieve large-scale commercial success. Early edge computing use cases rarely demanded strict latency guarantees or continuous workload mobility between device and edge. In many scenarios, centralised cloud platforms remained entirely sufficient.

Physical AI changes that. Real-time robotics and autonomous systems require predictable latency and continuous responsiveness. They also require the ability to distribute AI workloads dynamically across multiple compute domains without interrupting operations. Achieving this level of coordination demands a far deeper integration between MEC, AI orchestration and the RAN than most operators currently deploy.

If the network is expected to participate directly in AI execution and workload placement decisions, the RAN itself becomes part of the distributed AI infrastructure platform.

The implications for telecoms infrastructure are substantial. Operators aiming to support Physical AI applications may need to rethink network architecture around distributed compute rather than centralised capacity. MEC deployments would require tighter integration with radio infrastructure, orchestration systems and AI frameworks.

The industry has discussed this convergence for years through initiatives such as the AI-RAN Alliance and broader work around AI-native networking. What makes the SoftBank and Ericsson work particularly interesting is that it connects these ideas into a practical end-to-end implementation focused on real-world autonomous systems.

Physical AI may finally provide the commercial and technical justification the industry has been searching for to move MEC and AI RAN from research projects and isolated trials into mainstream network architecture. It hints at a future where operators are no longer simply connectivity providers, but providers of distributed AI infrastructure platforms capable of supporting real-time autonomous systems.

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Tuesday, 7 April 2026

Huawei’s PanoAAU Aims to Change the Economics of 5G Coverage

As 5G networks continue to expand beyond dense urban centres, the industry has been forced to confront a difficult reality. Traditional deployment models, built around three-sector sites and relatively narrow beam coverage, do not translate well into rural and semi-rural environments. Lower population density, larger coverage areas and tighter budgets mean that operators need to extract far more value from each site.

Huawei’s PanoAAU is one attempt to address this challenge. While it has been in the market for some time, publicly available technical detail remains limited. However, by piecing together information from different announcements and deployments, a clearer picture begins to emerge of what the solution is trying to achieve and why it matters.

At its core, PanoAAU is an evolution of the active antenna unit, designed to extend coverage both horizontally and vertically. The most notable shift is the move from the conventional 120 degree sector to a 180 degree wide-angle coverage. This is enabled through extremely large antenna array technology combined with beamforming and what Huawei describes as a wide-angle beam management approach. The practical implication is that a traditional three-sector site could, in some scenarios, be replaced with a two-sector configuration while still maintaining, or even improving, coverage.

This change is not just about radio performance. It has direct implications for site economics. Reducing the number of sectors means fewer radio units, less equipment on the tower, lower power consumption and potentially reduced site rental costs. In rural deployments, where return on investment is often marginal, these savings can be significant enough to make previously unviable sites commercially feasible.

The antenna design itself appears to rely on a combination of lightweight materials, low-loss feeding structures and metamaterial-based elements. These are intended to address the physical challenges that come with larger antenna arrays, particularly weight and signal loss. The use of such materials is consistent with a broader trend in radio design, where advanced materials are being used to push beyond traditional performance limits without making deployments impractical.

Software plays an equally important role. Wide-angle coverage introduces complexity in beam management, especially when trying to maintain capacity and user experience across a broader footprint. The solution therefore depends heavily on more precise and responsive beamforming algorithms to ensure that users are still served efficiently, even as the coverage area expands. This is particularly relevant for uplink performance, which is becoming increasingly important as networks evolve towards AI-driven applications and more interactive services.

PanoAAU also sits within a wider portfolio of radio solutions that Huawei has been promoting in the context of 5G-Advanced and what it refers to as 5.5G. Alongside products such as MetaAAU and EasyAAU, it reflects a move towards more specialised radio units tailored for different deployment scenarios. In this context, PanoAAU is positioned as a coverage-focused solution, particularly suited to suburban, rural and geographically complex environments.

Early deployments outside China provide some useful context. In Zambia, for example, MTN has worked with Huawei on dual-band active antenna solutions that are part of the same broader radio evolution. These deployments highlight a similar set of challenges, including limited tower space, the need to support both 4G and 5G, and the pressure to reduce both capital and operational expenditure. Solutions that integrate multiple bands and simplify installation are particularly attractive in such markets, where infrastructure constraints are often more pronounced.

There are also indications that the concept extends beyond traditional ground-level coverage. The emphasis on vertical reach suggests potential applications in high-rise urban environments and, increasingly, in low-altitude connectivity scenarios. This is where the discussion begins to overlap with one of the more interesting developments in 5G-Advanced, namely the integration of sensing capabilities into the network.

Recent trials in China have demonstrated how 5G-Advanced base stations can go beyond communication to provide radar-like sensing. Using integrated sensing techniques, networks are able to detect, track and monitor low-altitude objects such as drones in real time. Tests have shown that even very small objects can be identified with high accuracy, with the network able to determine position, speed and trajectory without relying on external systems such as GPS. This creates the possibility of electronic fencing, intrusion detection and broader airspace monitoring using the existing mobile infrastructure.

While this capability is not specific to PanoAAU, the underlying requirement is clear. Wider and more flexible coverage, including improved vertical reach, becomes increasingly important when networks are expected to support both communication and sensing functions. In that sense, technologies like PanoAAU can be seen as part of the enabling layer for these emerging use cases, particularly in scenarios where coverage continuity is critical.

Deployments in markets such as China and trials in other regions suggest that the solution is not purely theoretical. Operators have reportedly used it to reduce the number of required sites or sectors while maintaining service levels. In some cases, it has also been associated with lower energy consumption, aligning with the broader industry push towards greener network infrastructure.

The link to energy efficiency is particularly important. By integrating multiple capabilities into fewer units and enabling both 4G and 5G operation within the same hardware, solutions like PanoAAU can reduce overall network power consumption. This is increasingly becoming a key metric for operators, not just from a sustainability perspective but also in terms of operational expenditure.

It is also worth noting that PanoAAU is part of a broader shift in how radio access networks are being designed. The traditional approach of uniform site design is giving way to a more modular and scenario-driven strategy. Different environments require different solutions, and vendors are responding with increasingly diverse portfolios of radio units. In that sense, PanoAAU is less about a single product and more about a design philosophy focused on flexibility and efficiency.

That said, there are still open questions. Much of the available information comes from vendor-led announcements, with limited independent performance data. The real-world gains in coverage, capacity and cost savings will depend heavily on deployment conditions, spectrum availability and integration with existing networks. As with many new radio innovations, the benefits are likely to vary significantly from one market to another.

Even so, the underlying idea is difficult to ignore. If operators can meaningfully reduce the number of sectors or sites required for wide-area coverage without compromising user experience, it could have a lasting impact on how 5G networks are rolled out, particularly in underserved regions.

In that context, PanoAAU represents an interesting step in the ongoing evolution of radio access technology. It highlights the industry’s efforts to balance performance, cost and sustainability, while also preparing the network for emerging use cases that extend beyond traditional mobile broadband.

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Thursday, 12 March 2026

SK Telecom Builds AI Infrastructure Momentum with GPUaaS and the Haein Cluster

The growing demand for artificial intelligence computing is reshaping the role of telecommunications operators. As AI models become larger and more computationally intensive, the need for high performance infrastructure has moved into sharp focus. In response, SK Telecom is positioning itself not only as a connectivity provider but also as a key supplier of AI infrastructure through its GPU-as-a-Service offering.

At the centre of this strategy is the Haein GPU cluster, one of the largest AI computing platforms in South Korea. Built around more than 1,000 GPUs from NVIDIA based on the Blackwell architecture, the platform provides the computing power required for large scale AI training and inference workloads. The cluster represents a significant step forward from the earlier infrastructure based on NVIDIA H100 GPUs and forms part of SK Telecom’s wider sovereign AI infrastructure initiative.

The Haein cluster is hosted within the company’s Gasan AI Data Center in Seoul and is designed to deliver high performance computing capacity at national scale. The system supports intensive AI workloads including the training of large language models while also providing the flexibility required for enterprises and research organisations developing their own AI applications. The platform’s architecture allows large GPU resources to be combined into a single cluster while still being dynamically allocated to different users depending on demand.

A key component enabling this flexibility is SK Telecom’s proprietary virtualisation platform known as Petasus AI Cloud. The software layer allows the large GPU cluster to be partitioned and reconfigured dynamically, enabling customers to access the exact amount of computing power they require. This capability is essential for GPU-as-a-Service platforms where workloads can vary significantly, from small development environments to large scale model training that requires hundreds of GPUs operating simultaneously.

Alongside this, the company provides operational management through its AI Cloud Manager platform. This AIOps based environment supports the full lifecycle of AI services including development, training, deployment and operational monitoring. By combining infrastructure with operational tooling, SK Telecom aims to provide a more integrated AI computing platform rather than simply raw GPU capacity.

The Haein cluster also plays an important role in South Korea’s national AI strategy. The platform has been selected to support a programme led by the Ministry of Science and ICT that focuses on strengthening the country’s AI computing infrastructure and enabling the development of competitive national AI foundation models. Through this initiative, the cluster will contribute computing resources to projects developing sovereign AI capabilities tailored to the Korean language and domestic industries.

The name of the cluster itself reflects this national perspective. Haein takes inspiration from Haeinsa Temple, which houses the historic Tripitaka Koreana, a vast collection of Buddhist scriptures recognised as a UNESCO World Heritage archive. The naming reflects the ambition to create a modern repository of digital intelligence, supporting the development of AI knowledge and capabilities within the country.

Delivering infrastructure at this scale requires a broad ecosystem of partners. SK Telecom has worked with companies including Supermicro and Penguin Solutions to design and deploy the server infrastructure and integrated AI data centre solutions required for the cluster. These collaborations enable the rapid deployment of high density GPU servers and the supporting cooling, power and networking systems necessary to run large scale AI workloads.

The industry has already taken notice of the platform. The Haein GPU cluster was recognised at the MWC Barcelona 2026, where SK Telecom received the Best Cloud Solution award at the GSMA Global Mobile Awards. The recognition reflects the company’s continued progress in cloud and AI infrastructure development and marks the third consecutive year that its cloud related technologies have been acknowledged in this category.

For telecoms infrastructure professionals, SK Telecom’s GPU-as-a-Service strategy illustrates how operators are expanding beyond traditional connectivity services. By building large scale AI computing platforms inside their data centre footprint, operators can leverage existing strengths in infrastructure, power management and network integration to participate in the rapidly growing AI economy.

As AI adoption accelerates across industries, the demand for scalable computing infrastructure will continue to grow. With platforms such as the Haein cluster and its GPUaaS offering, SK Telecom is positioning its network and data centre assets as part of the core infrastructure supporting the next generation of AI innovation.

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Friday, 27 February 2026

Samsung Brings AI Across Every Layer of the Network to Power Next Generation Telecom Infrastructure

Artificial intelligence is rapidly becoming a defining capability in modern telecom networks. As operators continue to expand 5G and prepare for the transition to 6G, the scale and complexity of networks are increasing significantly. In this environment, automation, efficiency and adaptability are becoming essential. Samsung is positioning artificial intelligence as a core technology that can operate across every layer of the network to help operators manage this complexity while unlocking new capabilities.

Much of the industry conversation around AI integration in telecom networks has recently focused on the concept of AI-RAN. Within the AI-RAN Alliance, this is commonly described through three dimensions: AI for RAN, AI on RAN and AI with RAN. These categories describe how artificial intelligence can enhance radio network performance, support new edge-based services and enable the coexistence of AI workloads and network functions on shared infrastructure.

Samsung is actively involved in this industry effort, but its strategy extends beyond the radio layer alone. The company is promoting a broader approach to AI-powered networks that combines end-to-end software-based architecture with distributed computing capabilities. In this model, artificial intelligence is not limited to a specific part of the network. Instead, it is embedded across the entire infrastructure, from the radio access network to the core network and operational management systems.

A key element of this approach is Samsung’s focus on software-based and virtualised network architectures. Virtualised RAN deployments running on commercial off-the-shelf servers provide a flexible platform where both network workloads and AI functions can operate together. This allows operators to introduce AI capabilities without needing to completely redesign their infrastructure.

Through its network automation platform, Samsung is applying AI to a wide range of operational tasks. These include predicting traffic patterns, identifying anomalies in network performance, optimising radio parameters and balancing loads across spectrum bands. By analysing large volumes of operational data, AI systems can automatically adjust network behaviour to maintain performance and improve efficiency.

Energy optimisation is another area where AI-driven techniques are being applied. As mobile networks expand and traffic patterns fluctuate throughout the day, intelligent algorithms can determine when certain network features can be adjusted or scaled down to reduce power consumption without affecting user experience. These types of capabilities are becoming increasingly important as operators focus on both operational efficiency and sustainability.

Samsung is also exploring how artificial intelligence can improve radio performance directly within the protocol stack. Machine learning techniques can enhance channel estimation at the physical layer, allowing the network to reconstruct radio signals more accurately even in challenging environments. At higher layers, AI can support link adaptation by identifying optimal modulation and coding schemes for each user based on real time radio conditions. Even connection management processes can benefit from AI driven optimisation, improving both device battery efficiency and network resource utilisation.

Beyond improving the network itself, Samsung is also examining how telecom infrastructure can support AI workloads. Modern base stations and edge compute platforms contain significant computing resources. When network traffic demand is low, some of this capacity can remain unused. By running AI inference tasks on the same infrastructure, operators can make better use of these resources while supporting new services.

Edge based AI applications are particularly relevant in industrial environments. Real time video analytics, safety monitoring and automated quality inspection are examples of workloads that benefit from processing close to the data source. Running these applications on infrastructure that already supports radio functions reduces latency and avoids sending large volumes of data to central cloud platforms.

Samsung describes this convergence between communications infrastructure and computing capabilities as a shift towards networks functioning as distributed data centres. In this model, the network becomes both a connectivity platform and a processing environment capable of supporting AI driven applications. The concept combines two complementary perspectives: building networks that support AI workloads and using AI to improve how networks operate.

This architectural shift also has implications for the hardware layer of telecom infrastructure. Traditional mobile network equipment has relied heavily on specialised system-on-chip designs. However, the rapid development cycle of general purpose processors and accelerators is encouraging a more flexible approach. Samsung’s virtualised infrastructure strategy allows operators to deploy workloads on a mix of CPUs and GPUs, drawing on technologies from companies such as Intel, NVIDIA and Arm Ltd.. This enables operators to scale AI capabilities across different parts of the network depending on where computing power is needed.

As telecom networks evolve towards cloud native and software driven architectures, the role of artificial intelligence will continue to expand. By embedding AI across radio, core and operational layers, Samsung is highlighting how networks can move beyond traditional connectivity and become intelligent platforms capable of continuous optimisation.

With 5G Advanced deployments underway and early discussions around 6G gathering momentum, the integration of AI into telecom infrastructure is likely to accelerate. Samsung’s strategy suggests that the future network will not simply transport data, but will increasingly analyse, optimise and process it within the network itself, transforming the way operators design and operate their infrastructure.

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