O-RAN as an Enabler for Energy Efficiency in 5G Networks
Introduction to Energy Efficiency
Network densification and Massive Multiple-Input Multiple-Output (M-MIMO) transmission are some of the key enablers for achieving high user throughput in 5G networks . Network densification results from the deployment of many Base Stations (BSs, or in gNBs in 5G) of small size that can be installed, e.g., on the street lamps, to increase network capacity through efficient frequency reuse. M-MIMO takes the advantage of large antenna arrays (utilizing up to hundreds of antenna elements), to create narrow spatial beams and simultaneously serve multiple users with the same time-frequency resources.
The price to be paid for the improved user throughput comes from increased power consumption. First, there are a lot of new BSs/gNBs deployed that contribute to the power consumption. Second, those BSs can be potentially equipped with large antenna arrays, where each antenna element requires power-consuming hardware. Thus, Energy Efficiency (EE) has been introduced as one of the 5G Key Performance Indicators (KPI) .
EE is usually defined as a ratio between the average user throughput and average power consumption. Monitoring of EE can help Mobile Network Operators (MNOs) to balance user throughput-power consumption tradeoff in order to e.g., lower energy costs, or reduce carbon footprint. However, to enable intelligent, case-dependent EE, optimization algorithms need access to network interfaces, both for monitoring and control purposes. A straightforward option having those kinds of interfaces is the Open Radio Access Network (O-RAN) . In fact, the five, tier-one European MNOs namely, Deutsche Telekom, Orange, Telefonica, TIM, and Vodafone, have recently released a new technical priority document to put focus on EE, as the key pillar for the O-RAN evolution .
In this blog post, we provide a general overview of ways to improve EE in contemporary mobile networks. This is followed up by some representative examples of algorithms aiming at EE optimization at different levels, with a focus on the utilization of Machine Learning (ML) techniques. We then put it in the context of O-RAN where the deployment of EE-focused aspects and algorithms is discussed.
Ways to Improve Energy Efficiency in Mobile Networks
There are several approaches to improving EE in mobile networks, and it can be worked on at different layers . The highest level is simply by switching off the whole BS (i.e., putting the BS into the sleep mode). Lower level EE optimization relate to the individual BSs’ components like improvement of power amplifier efficiency, or adjusting the number of active antennas. Improvement of the EE can also be focused on some protocol-specific features like dynamic power allocation for the pilot signals, adaptation of DRX (discontinuous reception) parameters, or intelligent blanking of resource blocks under specific network conditions. Finally, EE gains can be obtained by switching off individual component carriers.
The above-mentioned techniques can be classified as direct EE improvement. Summing this up, when we look from the highest to the lowest level, the following elements can be switched off (or put into the sleep mode): whole BS, one of the sectors/cells, transceiver chain related to a single antenna, individual component-carrier, specific resource block, or even resource element (e.g. pilots signals not used when there is no traffic).
Besides the direct EE improvement, the EE of the wireless network can be also increased indirectly. This indirect EE optimization can be based on e.g., load balancing between neighboring cells, or between frequency bands within the same cell. The energy-efficient load balancing is achieved, through intelligent traffic steering, e.g., by a combination of dynamic switching users between cells and frequency bands not utilizing some of them, and adaptive power allocation. Moreover, to effectively process a large amount of data captured from the network, to make proper decisions (e.g., switching off power-consuming hardware), ML algorithms are of high importance. In the next subsections, some representative examples of algorithms aiming at improvements of EE are presented.
Base Station On/Off Switching
One example algorithm aims at switching on/off Pico-BSs in a heterogeneous network (see Fig. 1) . There is one Macro-BSs that is always active and provides coverage in the area, while several Pico-BSs can be intelligently activated/deactivated depending on the current traffic volume. E.g., one could imagine that Pico-BSs are needed during the daytime in the city center where a lot of people work, but during the nighttime, some of them can be put into sleep mode as most people came back to their homes in the suburbs. The process is driven by the so-called Reinforcement Learning (RL), i.e., learning through interaction. The aim of the agent is to learn which set of active Pico-BSs would provide the best EE under a given spatial distribution of users.
This also can be referred to as Energy Saving Management (ESM), i.e. one of the Self-Organizing Networks (SON) features as per 3GPP specifications, where depending on the actual traffic volume in a particular area at a particular time the small cells can be switched on or off, while the macro-site is responsible for providing coverage at any point in time .
Antenna Selection for M-MIMO
Another example approach corresponds to a different level in the EE optimization scope. In this case, optimization of a number of active antennas in M-MIMO BS is under consideration (see in Fig. 2). M-MIMO arrays can have up to hundreds of antenna elements, and each element contributes both to the overall power consumption and improvement of the user throughput. Here an algorihtm is proposed  that intelligently adjusts the number of active antennas to the spatial distribution of users within a cell. Due to the complexity of the M-MIMO system including non-trivial dependencies between interference and spatial channel correlations decision on the number of active antennas is produced by the RL agent so as to maximize EE.
User Association and Power Allocation
Yet another example focuses on an indirect EE optimization. As the amount of power being consumed by the BSs depends on the traffic load, it can be efficiently reduced by the proper association of users to BSs. Even higher EE improvement can be achieved when also power allocation per user is taken into the account. EE power management is of high importance, especially while considering mmWaves that are characterized by very high path loss, and are sensitive to obstacles. The authors of  investigated those problems in the context of dense networks operating in mmWave and proposed an iterative algorithm that improves EE through user association and power allocation.
O-RAN as an Enabler for Energy Efficient 5G Networks
The implementation of the above-mentioned representative algorithms aiming at EE improvements in 5G networks requires access to: some specific data, e.g., power consumption, traffic volume, user throughputs; and control actions, e.g., putting particular BSs into the sleeping mode. The current trend of opening the RAN, through O-RAN ALLIANCE specifications, where interfaces are open, unified, and vendor-independent to enable network optimization by third-party software , brings value to the topic of EE.
A group of the tier-1 European MNOs, have recently formulated an update to the Technical Priorities for O-RAN, focusing solely on EE as the cross-platform use case . First, the defined priorities include the necessity for monitoring EE-related KPIs through the O-RAN interfaces, e.g., power consumed by each cell/sub-band. Next, an O-RAN-compliant hardware is expected to support sleeping mode for either whole BS or a single element, e.g., transceiver chain. The EE hardware is expected to follow the “zero Watt at zero traffic” rule. Finally, MNOs seek possibilities in EE optimization through the deployment of advanced ML algorithms within the O-RAN architecture, to dynamically adapt a number of active BSs, and/or antenna elements to the current network state, e.g., traffic load, user spatial distribution. This could be developped by means of dedicated xApps or rApps.
Due to the dense network deployment and utilization of M-MIMO technology in 5G networks, optimization of EE becomes a crucial challenge for the MNOs. This is also true with respect to the overall power consumption in the networks, where e.g. some of the base stations are underutilized, due to being deployed for coverage reasons, but where most of the time there is no or very little traffic.
Being able to dynamically control the used resources and HW adapting to the actual traffic demand, would bring significant benefits to the MNOs. For this to work, there is a need for ML-type of algorithms to learn and follow traffic patterns.
The elements, which could be subject to switching on/off depending on the need, can be at a different „aggregation levels”, e.g.:
- Whole BS/gNB;
- Part of the gNB (e.g. CU/DU/RU);
- One of the sectors;
- Transceiver chain related to a single antenna/individual power amplifier;
- Component carrier;
- Resource Block;
- Resource element/pilot signal.
While academia already put a lot of effort to invent dedicated ML-based algorithms aiming at EE improvement, it is the O-RAN concept that can bring those ideas into the real implementations in the 5G and beyond networks due to the native incorporation of intelligence and open interfaces. Using the O-RAN entities namely: Non-Real Time RAN Intelligent Controller (Non-RT RIC), and Near-Real Time-RIC (Near-RT RIC), EE of the mobile network can be effectively increased. The dedicated xApps (deployed at RT-RIC) and rApps (deployed at non-RT RIC) can monitor network data and control switching off hardware components at different levels. Moreover, xApps/rApps can optimize EE indirectly through intelligent Traffic Steering.
If you are interested in this topic, stay tuned for further blog posts related to Rimedo Labs’ developments in this area.
 A. Gupta and R. K. Jha, „A Survey of 5G Network: Architecture and Emerging Technologies,” in IEEE Access, vol. 3, pp. 1206-1232, 2015, DOI: 10.1109/ACCESS.2015.2461602.
 3GPP TS 28.310 V17.3.0, “3rd Generation Partnership Project, Technical Specification Group Services and System Aspects, Management and orchestration, Energy efficiency of 5G”, Release 17, December 2021
 M. Dryjanski, R. Lundberg, “The O-RAN Whitepaper; Overview, Architecture, and Traffic Steering Use Case”, 2021, https://rimedolabs.com/blog/the-o-ran-whitepaper/
 D. Feng, C. Jiang, G. Lim, L. J. Cimini, G. Feng and G. Y. Li, „A survey of energy-efficient wireless communications,” in IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 167-178, First Quarter 2013, DOI: 10.1109/SURV.2012.020212.00049.
 M. Hoffmann, P. Kryszkiewicz and A. Kliks, „Increasing energy efficiency of massive-MIMO network via base stations switching using reinforcement learning and radio environment maps”, Comput. Commun., vol. 169, pp. 232-242, Mar. 2021, [online] Available: https://www.sciencedirect.com/science/article/pii/S0140366421000335
 3GPP TS 32.551 V17.0.0, “3rd Generation Partnership Project, Technical Specification Group Services and System Aspects, Telecommunication Management, Energy Saving Management (ESM), Concepts and requirements” Release 17, April 2022
 M. Hoffmann and P. Kryszkiewicz, „Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching,” in IEEE Access, vol. 9, pp. 130329-130339, 2021, DOI: 10.1109/ACCESS.2021.3113461.
 H. Zhang, S. Huang, C. Jiang, K. Long, V. C. M. Leung, and H. V. Poor, „Energy-Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra-Dense Networks With Energy Harvesting Base Stations,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 9, pp. 1936-1947, Sept. 2017, DOI: 10.1109/JSAC.2017.2720898.
 Deutsche Telekom, Orange, Telefonica, TIM, and Vodafone “Open RAN Technical Priorities, Focus on Energy Efficiency”, June 2021, accessible at: O-RAN Ecosystem Resources — O-RAN ALLIANCE
Related Rimedo Labs Resources
- The O-RAN Whitepaper: Sign up for our newsletter to download: The O-RAN Whitepaper
- Blog post on green communications: Green Communication and Computing for 2030 – RIMEDO Labs
- O-RAN Blog posts: Other blog posts regarding the O-RAN: 1. Introduction to O-RAN: Concept and Entities, 2. O-RAN Architecture, Nodes, and Interfaces, 3. near-Real-Time RIC (RAN Intelligent Controller), 4. O-RAN Use Cases: Traffic Steering
- O-RAN website: Find out more about our O-RAN offering, services and materials O-RAN
- Webinar: A video recording from a webinar discussing O-RAN architecture, RIC internals, use cases, with a special focus on traffic steering: O-RAN Architecture and Use Cases – YouTube
- Training: Sign up for our O-RAN System Training (intelefy.com)
Marcin Hoffmann is an R&D engineer at Rimedo Labs working on O-RAN software development solutions and spectrum sharing-related projects. Marcin is a Graduate Student Member, at IEEE and received the M.Sc. degree (Hons.) in electronics and telecommunication from Poznań University of Technology, in 2019, where he is currently pursuing a Ph.D. degree with the Institute of Radiocommunications. He is gaining scientific experience by being involved in both national and international research projects. His research interests include the utilization of machine learning and location-dependent information for the purpose of network management. In addition to that Marcin works on massive MIMO and advanced beamforming techniques. His scientific articles are published in top journals like IEEE Transactions on Intelligent Transportation Systems or IEEE Access.