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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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