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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