The work performed has focused on the identification and validation of energy-efficiency improvements across RAN elements, leading to enhanced and more sustainable radio network deployment strategies. Beyond conceptual analysis, the project has provided quantified evidence of energy savings, demonstrating that advanced architectures and control strategies can deliver substantial gains, including up to 30 dB transmit power reduction in cell-free massive MIMO, 15–84% savings through network optimisation, and up to 90% savings via relay-assisted deployments, depending on the scenario .
During Period 2, the architectural design and implementation have been the central focus. The project has advanced the baseline 3GPP and O-RAN architectures by introducing AI/ML-driven optimisation mechanisms and extending control capabilities. A key outcome is the design and validation of the BeGREEN Intelligent Plane, which enables AI-native energy optimisation across the network. This includes the definition of its components, interfaces, and integration within O-RAN, as well as the identification of architectural gaps requiring extensions (e.g. for RIS, relays, ISAC, and edge computing). The associated AI Engine, based on serverless and loosely coupled principles, provides scalable support for AI/ML services and has demonstrated up to 40% energy savings at system level without performance degradation .
At component level, WP3 has redefined the implementation of key O-RAN elements, particularly CU and DU, through optimised functional splits and hardware acceleration, targeting significant efficiency improvements beyond current SOTA deployments. For the RU, both hardware-level optimisations (e.g. PA efficiency improvements and PHY adaptations) and network-level control via RICs have been investigated to reduce energy consumption dynamically. Additionally, the integration of non-traditional components such as RIS, relays, and ISAC has been explored, demonstrating their strong potential for improving energy efficiency in diverse deployment scenarios, including challenging indoor and edge cases.
At demonstration level, under Task 5.1 multiple Proofs of Concept have been implemented and validated, confirming the feasibility and effectiveness of the proposed solutions in realistic environments. These demonstrations consolidate the project’s contributions by showing how AI-driven control, architectural innovation, and advanced radio technologies can be combined to deliver measurable energy savings while maintaining network performance.