Samsung’s Research Vision for Energy Saving in 6G Networks

Energy efficiency is becoming one of the most important design pillars for future mobile systems. Rising energy costs, sustainability goals, and the broader climate agenda have placed network power consumption under greater scrutiny than ever. While 5G introduced several mechanisms to reduce energy use, many of these were added incrementally and were limited by legacy assumptions embedded in the technology. As the industry looks beyond 2030, 6G provides a rare opportunity to build energy efficiency into the design of the air interface, radio technologies, and network architecture from day one.

The 5G Problem Statement

Energy saving in 5G relied heavily on features such as DRX, DTX, bandwidth parts, SSB periodicity control, on demand SIB1, cell switch off and increasingly flexible antenna port management. These helped improve the energy profile of 5G but could not remove the structural inefficiencies created by always on components, frequent broadcast signalling and large antenna arrays that remained active regardless of traffic. High periodicity SSB bursts alone prevented base stations from entering deep sleep states, while legacy UEs restricted how far networks could push newer optimisations.

As demand continues to rise and network densification accelerates, the limitations of these approaches become more apparent. Energy efficiency in 6G therefore cannot be a secondary consideration. It must influence how carriers are structured, how radios operate, how signalling is delivered, and how networks collaborate with user devices. The direction taken in current research reflects a shift from incremental improvements to fundamental redesign.

Samsung's 6G Energy Saving Proposals

At the heart of 6G energy saving lies a simple principle: reduce the amount of time and space in which the network remains active. This philosophy can be summarised as less on and more off. Achieving this requires context awareness, tighter coupling between network entities, and a move towards signalling that is event driven rather than periodic.

Three architectural pillars enable this shift. The first is carrier dependent design, where low frequency carriers handle essential functions with minimal activity, and higher frequency carriers activate only when data is required. The second is dynamic spatial and power adaptation, where the number of active antenna ports, beam directions and power levels scale up or down based on real traffic demand. The third is an energy aware network management framework that monitors real time conditions and orchestrates power related decisions across the RAN and core. All three are enhanced when combined with AI driven prediction, optimisation and channel understanding.

One of the most significant departures from earlier generations is how 6G treats synchronisation and system signalling. Multi type SSBs allow networks to choose between normal, energy saving and on demand variants. During idle or low traffic periods, SSBs can be transmitted far less frequently, or only when explicitly requested. This unlocks deeper sleep states for base stations and removes a major obstacle that restricted power saving in 5G.

The on demand model also extends to other forms of signalling. Measurements that were once periodic can become event driven. Reference signals such as CSI RS and DMRS can be transmitted less often when channels are stable or when user mobility is low. This avoids unnecessary wake ups for UEs and reduces the constant RF activity that drains power at the base station.

These adaptations form the basic layer of 6G energy saving and must work even when UEs lack AI hardware. They deliver immediate benefits by cutting unnecessary transmissions, allowing both the network and UEs to remain inactive for longer without sacrificing coverage or mobility support.

Dynamic Spatial Adaptation and Intelligent MIMO Operation

Massive MIMO has been essential for the spectral efficiency gains seen in 5G, but it has also become one of the largest contributors to radio energy consumption. Operating hundreds of antenna elements and their associated RF chains continuously creates heat, raises operational costs and limits deployment flexibility. This becomes even more challenging as 6G explores larger arrays, upper mid band operation, and distributed radio topologies.

6G MIMO research focuses on making transceivers far more intelligent and energy aware. Rather than keeping all antenna ports active, the radio can activate only the necessary subset based on current traffic or channel conditions. In high load situations, the full array can be used. During quieter periods, the radio scales down, reducing the number of active power amplifiers and minimising waste. In distributed deployments, coordinated joint transmission allows multiple radio units to cooperate, switching between them intelligently so that only the most beneficial units remain active. This balances energy consumption with coverage and throughput.

AI will deepen these gains by improving beam prediction, enabling better antenna subset selection and reducing the amount of feedback needed for channel state information. Traditional codebooks become impractical at 6G antenna scales, so AI based compression and CSI estimation allow networks to learn and represent channels more efficiently. This not only cuts signalling overhead but also reduces the energy spent processing and delivering high resolution CSI.

Energy Efficiency Through AI Native Control

AI plays a complementary but increasingly essential role in the 6G energy saving roadmap. By predicting traffic patterns, user mobility, channel changes and beam trajectories, the network can operate proactively rather than reactively. This reduces the need for frequent periodic measurements and allows for smarter scheduling decisions that minimise active time for both the base station and UE.

AI can infer when UEs are likely to wake, when beams need to be updated, and which resources are worth activating. It can also help compress CSI representations, making feedback more energy efficient for the UE. While not all UEs will support complex AI models, the network can apply AI on the infrastructure side and still deliver benefits for all devices.

Evaluating the Combined Gains

When combined, these techniques demonstrate meaningful improvements. Analyses based on realistic 24 hour traffic models show that dynamic SSB operation and on demand signalling can achieve major reductions in broadcast energy. Spatial adaptation adds another measurable reduction by scaling antenna usage. Together, these mechanisms produce a significant reduction in network energy consumption while improving user perceived throughput at high load due to lower signalling overhead and more efficient resource use.

The direction of these results shows that 6G energy saving is not simply a matter of reducing power but orchestrating resources intelligently across time, frequency, spatial domains and network layers. The approach creates a scalable foundation that can adapt to traffic fluctuation, deployment variation and new service types.

Final Thoughts

Energy saving in 6G represents a shift from late stage optimisations to a holistic redesign around sustainability. It combines flexible carrier structures, event driven signalling, intelligent antenna operation and AI assisted efficiency to deliver a network that consumes less while performing more effectively. This marks an important step in aligning mobile network evolution with environmental targets and long term operational resilience.

As 6G research continues, energy efficiency will remain central to both the physical layer and overall system design. The lessons learned from 5G are being used not only to enhance performance but to reduce unnecessary activity, improve hardware utilisation and ensure that future networks are sustainable by default.

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