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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Tuesday, 10 February 2026

Reconfigurable Antennas and the Infrastructure Implications For 6G

Reconfigurable antennas have been a topic of academic research for many years, but as 5G networks continue to densify and the industry begins to look seriously towards 6G, their relevance to real-world telecom infrastructure is becoming increasingly clear. A recent presentation by Prof. Chenhao Qi from Southeast University, Nanjing, China, titled Reconfigurable Antennas for Wireless Communications, offers a timely and technically rich overview of how antenna reconfigurability could influence future radio access network (RAN) design across sub-6 GHz, mmWave and, in the longer term, THz frequency bands. From an infrastructure perspective, the underlying message is straightforward: future networks will operate across far more diverse spectrum and deployment scenarios, and static antenna designs will struggle to deliver the required flexibility, efficiency and performance.

The performance targets associated with 6G go well beyond those of current 5G systems. Improvements are expected not only in peak data rates and spectral efficiency, but also in latency, positioning accuracy, reliability and energy efficiency. Achieving these targets requires networks that can adapt dynamically to changing electromagnetic conditions and physical environments. Today’s RAN deployments already span multiple layers, from sub-6 GHz macro coverage to mid-band capacity and mmWave hotspots. As frequencies increase, propagation becomes more sensitive to blockage, orientation and interference, making adaptability at the antenna level increasingly important.

Reconfigurable antennas are designed to address this challenge by allowing key antenna characteristics, such as operating frequency, radiation pattern and polarisation, to be adjusted dynamically. This adaptability can be achieved either electronically or through physical changes to the antenna structure. Electronically reconfigurable antennas integrate RF components such as PIN diodes, FET switches or MEMS into the antenna design, enabling very fast reconfiguration on timescales suitable for live network operation. Structurally reconfigurable antennas instead rely on physical movement or deformation of radiating elements, including approaches based on movable parts, liquid metals or flexible structures. While these techniques can offer high flexibility, they also introduce mechanical complexity and slower reconfiguration speeds, which can limit scalability in large-scale infrastructure deployments.

From a network infrastructure standpoint, electronic reconfiguration is particularly attractive. Fast switching speeds, compact integration and long-term reliability make it well suited to dense antenna arrays and multi-band base station designs. The ability to support multiple reconfiguration modes within a single antenna system also opens the door to more efficient hardware utilisation. Frequency reconfiguration allows antennas to switch between bands as spectrum availability or traffic demand changes. Polarisation reconfiguration can improve robustness in both line-of-sight and non-line-of-sight conditions by mitigating fading and misalignment. Pattern reconfiguration enables beam steering, null placement and coverage shaping without relying solely on external beamforming networks. In more advanced designs, these capabilities can be combined, allowing frequency, polarisation and radiation pattern to be adapted jointly.

The presentation also highlights how reconfigurable antennas interact with emerging RAN architectures, particularly in the context of integrated sensing and communication (ISAC) and massive MIMO. One example is a dual-band reconfigurable antenna array, commonly referred to as a DBRAA, that supports both sub-6 GHz and mmWave operation within a shared aperture. This reflects a practical reality for infrastructure deployments, where different frequency bands offer complementary advantages and must coexist efficiently. By dynamically forming sub-6 GHz antennas from mmWave elements, the DBRAA architecture enables finer control over antenna spacing and improved performance compared to fixed-position arrays, while also reducing the need for separate antenna hardware.

Another concept explored is the use of reconfigurable pixel antennas to realise electronically movable antenna arrays, described as reconfigurable pixel antenna-based electronic movable-antenna arrays (REMAA). The key insight here is that radiation pattern reconfiguration can be equivalent, from a channel perspective, to physically moving antenna elements. Achieving this electronically avoids the mechanical complexity associated with motor-driven or fluid-based movable antennas. For dense sites and space-constrained installations, REMAA offers a practical path to improved interference management, better multi-user performance and more efficient use of available antenna real estate.

At mmWave frequencies, power consumption and RF chain count remain major concerns for infrastructure providers. Hybrid beamforming architectures have already been adopted to strike a balance between performance and complexity, but the presentation goes a step further by introducing tri-hybrid beamforming. In this approach, digital beamforming, analogue beamforming and electromagnetic beamforming enabled by reconfigurable antennas are jointly optimised. Radiation-centre selection becomes an additional degree of freedom in the beamforming process, increasing design flexibility while reducing the number of active antenna ports. For large-scale mmWave arrays, this translates into higher spectral efficiency and improved energy efficiency, particularly as array sizes grow.

Taken together, these concepts point towards a future in which antenna systems play a far more active role in network optimisation. Reconfigurable antennas have the potential to reduce hardware duplication across frequency bands, improve adaptability to changing propagation conditions and traffic patterns, and support advanced use cases such as ISAC without a proportional increase in cost or power consumption. At the same time, the presentation makes it clear that several challenges remain, including accurate modelling of reconfigurable antennas, their integration into practical beamforming architectures and a deeper understanding of their end-to-end energy efficiency.

As the industry moves towards 6G, antennas are likely to evolve from largely static components into adaptive, software-controlled elements that are tightly integrated with signal processing and network intelligence. Reconfigurable antennas are not a single solution to all future RAN challenges, but they are emerging as an important building block for next-generation telecom infrastructure. For operators, vendors and infrastructure providers, the ideas presented offer a useful glimpse into how antenna technology could shape deployment strategies and network evolution in the years ahead.

The slides of the presentation are available here and the video is embedded below:

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Tuesday, 13 January 2026

Powering Vodafone’s Mobile Network with Solar Energy in Germany

Iberdrola has brought its first solar park in Germany into operation, with the renewable electricity generated at the site now supplying Vodafone’s mobile network nationwide. The project represents a growing convergence between energy and telecom infrastructure, as mobile operators look to secure reliable and sustainable power sources to support expanding network demands.

The solar park is located in Boldekow in the state of Mecklenburg-Western Pomerania and marks Iberdrola’s entry into onshore photovoltaic generation in the German market. The site covers an area equivalent to more than 65 football pitches and is equipped with around 80,000 solar panels. Once fully operational, the installation is expected to generate up to 53 gigawatt hours of electricity each year, with the entire output dedicated to Vodafone under a long-term power purchase agreement.

For Vodafone, the project provides a direct and predictable supply of renewable electricity for its mobile infrastructure across Germany. The annual energy output is sufficient to power around 3,000 mobile base stations, supporting the radio access network and associated systems that underpin nationwide mobile coverage. As networks continue to evolve to handle higher traffic volumes and increased densification, access to stable and locally generated energy is becoming a strategic consideration alongside spectrum, sites and backhaul.

The environmental impact of the solar park is significant. By replacing conventional grid electricity with solar generation, the project is expected to reduce carbon dioxide emissions by approximately 20,000 tonnes per year. Over an anticipated operational lifetime of around 30 years, this contributes meaningfully to emissions reduction targets while aligning network operations with wider sustainability objectives.

From an infrastructure perspective, the project illustrates a shift in how telecom operators source energy. Rather than relying solely on indirect mechanisms, such as renewable energy certificates, operators are increasingly entering direct supply agreements linked to specific generation assets. This approach improves transparency, strengthens energy security and creates a clearer relationship between network growth and renewable energy investment.

For Iberdrola, the Boldekow solar park complements its existing presence in Germany, where the company already operates offshore wind assets in the Baltic Sea. Expanding into onshore solar generation allows for a more diversified renewable portfolio and demonstrates how utility-scale energy infrastructure can be closely aligned with the needs of digital networks.

The project has also delivered local benefits, including regional investment during construction and long-term contributions to municipal revenues. This underlines how renewable energy developments tied to telecom infrastructure can support local economies while addressing national connectivity and sustainability goals.

As mobile networks progress towards higher capacity, lower latency and greater automation, their energy requirements will continue to grow. Projects such as Iberdrola’s solar park supplying Vodafone’s mobile network show how renewable energy is becoming a foundational element of modern telecom infrastructure, rather than a parallel or secondary consideration.

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Monday, 29 December 2025

Top 10 Posts for 2025

Telecoms Infrastructure Blog has been around since December 2012, and in February 2020 it moved to its current home. Since then the site has grown steadily to pass more than one million views in total, with nearly 500 thousand views recorded in 2025 alone. With over 400 posts now published, the blog continues to attract interest from readers across the industry who are looking for technical depth, real world deployments and infrastructure insights.

Rather than limiting this list to posts written in 2025, it felt more useful to simply highlight the ten most-read posts during the year, regardless of when they were originally published. Some older articles continue to attract strong readership, which shows how relevant many of these topics still are to today’s networks.

  1. Decoding Starlink: The Technology Behind the Revolution, Jan 2025
  2. Huawei's Lampsite, Jul 2014
  3. Passive and Active Infrastructure Sharing, May 2020
  4. Nokia's AirScale indoor Radio (ASiR) Small Cells, Jul 2020
  5. Planning, Constructing, and Commissioning a Mobile Network Site, Oct 2024
  6. Open RAN (O-RAN) RRU (O-RU) and DU (O-DU) Design, Feb 2021
  7. Viettel’s Growing Influence in 5G, Private Networks and Open RAN, Jun 2025
  8. FDD Tri-Band Massive MIMO: Unlocking Sub-3 GHz Potential for 5G Evolution, Apr 2025
  9. Evolution of AT&T’s Flying COW (Cell on Wings), Feb 2023
  10. Meta's Project Waterworth: The Next Evolution in Subsea Connectivity, Feb 2025

2025 has been another busy year for telecoms infrastructure. The mix of topics here reflects how fast the industry continues to evolve, from satellites and subsea connectivity through to Open RAN, indoor coverage and Massive MIMO. It is also encouraging to see that older posts are still being discovered and referenced, which reinforces the value of long-form technical content.

Thank you for reading, sharing and supporting the blog. I will continue to cover new deployments, technologies and trends in the year ahead, and I hope you will keep visiting and contributing to the conversation.

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Tuesday, 16 December 2025

Architecting Resilient Global Networks with Amazon LEO

At AWS re:Invent 2025, a joint presentation from Amazon LEO and AWS Global Infrastructure Services offered a detailed look at how non-terrestrial networks are being engineered to operate as part of modern global infrastructure. Nick Matthews, Principal Solutions Architect at Amazon LEO, and John Phillibert, Principal Network Development Engineer at AWS, focused not on consumer broadband headlines but on the architectural foundations needed to deliver resilient, low-latency connectivity at global scale.

The session provided a rare infrastructure-level walkthrough of how a low Earth orbit satellite network is being integrated directly with AWS edge and backbone services, and why this matters for enterprises, operators and critical infrastructure providers.

A LEO network designed like cloud infrastructure

Amazon LEO is building a constellation of more than 3,200 satellites in low Earth orbit, operating at altitudes between roughly 590 and 630 kilometres. From an infrastructure perspective, the key distinction from traditional geostationary satellite systems is not just lower latency, but the need to manage constant motion, frequent handovers and highly dynamic routing.

Satellites orbit the Earth approximately 16 times per day, remaining in view of a given location for only tens of seconds at a time. Rather than treating this as a constraint, Amazon LEO has designed the network using cloud-scale principles. Satellite positions, terminal locations and gateway connectivity are all highly predictable, allowing routing, frequency allocation and handover behaviour to be pre-computed and continuously optimised.

The result is a system that behaves far more like a terrestrial fibre or mobile network than a traditional satellite service, with expected round-trip latencies below 50 milliseconds and enterprise-grade throughput.

Terminals as hardened network edge devices

A significant portion of the engineering effort has been invested in user terminals. From an infrastructure viewpoint, these terminals act as the true network edge, and Amazon LEO has designed them to be rugged, simple to deploy and economically scalable.

Three terminal classes were outlined. A compact nano terminal supports low-power and IoT-style use cases. A pro terminal offers higher throughput in a device comparable in size to a laptop. At the high end, an ultra terminal supports gigabit-class connectivity suitable for large enterprise sites, temporary campuses or even data centre backup.

All terminals use electronically steered phased-array antennas, eliminating the need for mechanical movement and enabling rapid satellite tracking. From a deployment perspective, installation is intentionally straightforward, using standard Ethernet into existing enterprise or operational technology networks.

Space lasers and ground gateways as resilience tools

Beyond basic satellite-to-ground connectivity, Amazon LEO is deploying inter-satellite laser links. These links create a space-based backbone that allows traffic to be routed between satellites before descending to Earth. This has two important infrastructure implications.

First, it extends coverage to locations far from ground gateways, including oceans and air routes. Second, it introduces path diversity at a global scale. If a ground gateway is unavailable due to weather, fibre damage or power issues, traffic can be dynamically rerouted through alternative gateways without service interruption.

Ground gateways themselves are treated as high-capacity aggregation points, converting radio frequency links into terrestrial fibre connectivity. Their placement is closely aligned with AWS edge locations, minimising backhaul distance and reducing overall latency.

Tight integration with the AWS global backbone

John Phillibert’s contribution focused on why AWS infrastructure is central to making this model work at scale. AWS operates one of the world’s largest private global backbones, interconnecting regions, edge locations and direct connect sites over millions of kilometres of terrestrial and subsea fibre.

By anchoring Amazon LEO gateways directly into this backbone, satellite traffic enters a network designed for high automation, rapid fault remediation and consistent performance. Most network events within AWS are resolved automatically, without human intervention, which is essential when extending connectivity into remote or harsh environments.

Security is also built in at the infrastructure layer. AWS encrypts traffic across its backbone using MACsec, while Amazon LEO adds its own end-to-end encryption from terminal to point of presence. Even physical access to gateways or satellites yields only encrypted data, an important consideration for critical national infrastructure and regulated industries.

Infrastructure patterns for resilience and continuity

Several architectural patterns emerged repeatedly during the session. One is the use of Amazon LEO as a physically diverse connectivity path. Fibre cuts, natural disasters and power outages remain common failure modes in terrestrial networks, even in developed markets. A non-terrestrial path introduces true geographical and physical diversity, rather than simply logical redundancy.

Another pattern is rapid site enablement. In many industrial or logistics environments, waiting months for fibre or microwave links is not viable. A satellite terminal that can be shipped, installed and operational in days enables faster deployment of digital infrastructure, with terrestrial connectivity added later if required.

For more mature environments, Amazon LEO can complement existing architectures such as SD-WAN, private interconnects or direct cloud access. Integration with AWS Transit Gateway and Direct Connect allows traffic to remain private end to end, avoiding exposure to the public internet.

Extending compute and control to the edge

The infrastructure implications extend beyond connectivity alone. Reliable, high-bandwidth links enable changes in where compute and control systems are located. The session highlighted scenarios where local data centres exist solely because connectivity is unreliable. With improved resilience, workloads can be shifted to cloud regions, local zones or managed edge platforms.

In operational technology environments, this supports new models for monitoring, analytics and remote operation. SCADA systems, industrial sensors and video feeds can be securely backhauled to central platforms for analysis, while still maintaining local control loops where required.

This is less about replacing existing infrastructure overnight and more about enabling gradual architectural evolution, starting with connectivity and extending into compute placement and operational models.

A non-terrestrial network built for infrastructure scale

What stood out from the presentation was not just the technology itself, but the way it is being engineered. Amazon LEO is not positioned as a standalone satellite service, but as an extension of cloud and network infrastructure, designed using the same principles of automation, resilience and integration that underpin hyperscale data centres.

For telecom infrastructure professionals, the message was clear. Non-terrestrial networks are no longer niche overlays. When tightly integrated with terrestrial backbones, edge platforms and security frameworks, they become a foundational component of global connectivity strategies.

As Amazon LEO moves through its enterprise preview and towards service launch in 2026, its impact is likely to be felt less in marketing headlines and more in the quiet redesign of how resilient networks are built, extended and operated at global scale.

The talk is embedded below:

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