D2.2 – Evolved Architecture and Power Enhancement Mechanisms
Creators: name: Ayala-Romero, Jose A.
name: Salvat Lozano, Josep Xavier
name: Blanco Pizarro, Alejandro
name: Suárez-Reyero, Pablo
name: Bielsa, Guillermo
name: García-Saavedra, Andres
name: Zanzi, Lanfranco
name: Li, Xi
name: Sark, Vladica
name: Özates, Mert
name: Gutiérrez Terán, Jesús
name: Catalan, Miguel
name: Municio, Esteban
name: Castellanos, German
name: Berozashvili, Revaz
name: Pryor, Simon
name: Umbert Juliana, Anna
name: Sánchez González, Juan
name: Pérez-Romero, Jordi
name: Sallent Roig, José Oriol
name: Armstrong, Joss
name: Briggs, Keith
name: Ghoraishi, Mir
Publisher: name: Zenodo
Year: 2024

description: Current document, BeGREEN D2.2, builds upon the groundwork established in BeGREEN D2.1. The latter offered an extensive overview of the State-of-the-Art (SotA) for Radio Access Networks (RANs) from an energy-efficiency standpoint, while proposing solutions for RAN optimization at hardware, link, and system levels.

BeGREEN D2.2 opens the discussion on the shift from purely performance targets previously adopted by the 5G-PPP to a more societal vision, including sustainability, security, and inclusivity. The ongoing efforts aligned with these values are explored and their integration in BeGREEN project are justified. Then, four mechanisms to enhance energy efficiency in RANs are proposed, culminating in an evolved architecture that enables their integration.

Firstly, advanced forms of MIMO technologies and related energy efficiency factors are studied. Traditional MIMO boosts throughput by transmitting multiple data streams simultaneously but struggles at cell edges due to weaker signals and interference. Distributed MIMO (D-MIMO) improves performance by combining MIMO with distributed antenna systems, ensuring consistent data rates. Cell-Free massive MIMO (CF-mMIMO), an advanced form of D-MIMO, dynamically selects access points (APs) based on signal quality, eliminating cell boundaries and improving energy efficiency by reducing the number of transmitting antennas. An empirical study to evaluate the performance of CF-mMIMO compared to traditional MIMO systems is performed. The second mechanism is Integrated Sensing and Communication (ISAC), whereby we propose extracting useful information from the sensing data that can lead to the optimization of resource allocation. The third mechanism is relaying transmitted data in RANs using relay nodes. Strategic placement of fixed or mobile relays can significantly enhance energy efficiency by optimizing network energy consumption. Relays improve signal conditions for user equipment (UE), allowing base stations (BS) to transmit with lower power. However, the power consumption of relays must also be considered. BeGREEN D2.2 explores relay deployment in two scenarios: one with single-antenna entities and another with massive MIMO at the BS. The fourth mechanism is computing allocation in virtualised RANs (vRANs) and its impact on energy consumption. The deployment of multiple virtual base stations (vBS) on shared computing infrastructure is analysed, and the "noisy neighbour" problem where energy overhead increases exponentially is characterised. The study explores CPU pinning, Last Level Cache (LLC) allocation, and various parameters such as traffic load, BS bandwidth, and modulation and coding schemes. The findings highlight trade-offs that present opportunities for developing optimisation algorithms to significantly improve RAN energy efficiency.

Finally, leveraging the mechanisms proposed in WP3 and WP4, and based on the O-RAN architecture and the initial BeGREEN architecture from BeGREEN D2.1, the evolved BeGREEN architecture is introduced and discussed. This involves architectural extensions for integrating AI/ML support, D-MIMO, ISAC, Reconfigurable Intelligent Surfaces (RISs), relays, and the Edge domain. BeGREEN D2.2 serves as input to BeGREEN D2.3 as well as to BeGREEN WP3, WP4, and WP5.