„The O-RAN Whitepaper 2023 – Energy Efficiency in O-RAN”

Similarly, to last year, we are pleased to introduce our latest The O-RAN Whitepaper 2023: Energy Efficiency. This post is dedicated to revealing some contents of the whitepaper.

Executive Summary

Energy efficiency is one of the key aspects within the 5G and beyond mobile networks. Specifically in the RAN, it is currently on the agenda of the industry and research activities. As the industry is targeting higher and higher throughputs within 5G and beyond, an increasing number of energy-consuming hardware must be deployed. From this perspective, energy-saving mechanisms are of crucial role. O-RAN ALLIANCE is currently providing means to support energy-saving topics as one of the key pillars for moving forward within the RAN domain. This whitepaper provides a technical discussion of one of the currently important aspects related to the Open Radio Access Network (Open RAN), namely Energy Efficiency (EE).

The first chapter provides an overview of the energy efficiency topic within wireless mobile networks along with the take on from the O-RAN perspective. It serves as a starting point for more details to be provided in the following parts.

Then, the whitepaper discusses the ways to save energy on different levels e.g, cell/base station switching off/on, Massive MIMO (M-MIMO) antenna configuration changes, user association, etc.

This is followed up by example algorithms utilizing ML schemes and their realization in the form of rApps based on O-RAN-defined use cases. Those include intelligent and ML-based cell switch off/on and RF channel switch off/on. The example implementation of the algorithms in the form of rApps is presented along with challenges including the need for coordination of those with other surrounding algorithms.

The whitepaper ends with a summary and conclusions section along with a glossary of the used terms.

Note: The general trend of disaggregated and opening radio networks is called Open RAN. O-RAN, in turn, refers to the O-RAN ALLIANCE-specified architecture and framework.

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Cite this: M. Hoffmann, M. Dryjanski, „The O-RAN Whitepaper 2023 – Energy Efficiency in O-RAN”, Whitepaper, Rimedo Labs, Feb 2023

The O-RAN Whitepaper Contents

1.0 Introduction to Energy Efficiency

Recently, within the RAN domain, EE has been emphasized as one of the key requirements for moving forward. It has to take the end-to-end approach involving all domains within the system. The overall objective for O-RAN is to gradually become more energy efficient than the regular RAN. This should not be at a cost of the key O-RAN concepts including cloudification and disaggregation [1]. Moreover, energy-efficient RAN must still offer high Quality of Service (QoS) to the network users. Being able to dynamically control the used resources and hardware by adapting its usage to the actual traffic demand, would bring significant benefits to the Mobile Network Operators (MNOs). This chapter provides an overview of energy efficiency and how it fits into the O-RAN context.

• 1.1 Energy Efficiency in Mobile Networks
• 1.2 Achieving High User Throughput vs Increased Energy Consumption
• 1.3 Network Energy Saving in O-RAN

Chapter 2.0 O-RAN as an Enabler for Energy Efficiency in 5G Networks

In this chapter, 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 ML techniques. We then put it in the context of O-RAN where the deployment of EE-focused aspects and algorithms is discussed.

• 2.1 Ways to Improve Energy Efficiency in Mobile Networks
• 2.2 Base Station On/Off Switching
• 2.3 Antenna Selection for M-MIMO
• 2.4 User Association and Power Allocation
• 2.5 O-RAN as an Enabler for Energy-Efficient 5G Networks

Chapter 3.0 O-RAN Network Energy Efficiency: Cell Switch On/Off

In this chapter, we describe an algorithm that can be realized as an rApp in the O-RAN architecture aimed at improving the network’s EE through intelligent cell switch-off/on. First, we provide a general framework for long-timescale EE optimization which can be done in Non-Real-Time RAN Intelligent Controller (Non-RT RIC) as defined in [12]. We then propose the implementation of the Cell Switch Off/On algorithm within the O-RAN framework.

• 3.1 Cell Switch Off/On Concept
• 3.2 Cell Switch Off/On in the O-RAN Architecture
• 3.3 Cell On/Off Switching rApp
• 3.4 rApp Implementation Challenges

Chapter 4.0 O-RAN Network Energy Efficiency: RF Channel Switching

In this chapter, we provide the description of an algorithm that can be realized as an rApp in the O-RAN architecture aimed at improving the network’s EE through a second use case addressed by O-RAN ALLIANCE, i.e., intelligent RF channel switching off/on. First, we provide a general framework for long-timescale EE optimization which can be done in Non-RT RIC. We then propose the implementation of the RF Channel Switching algorithm within the O-RAN framework.

• 4.1 Massive MIMO and Energy Consumption
• 4.2 RF Channel Switching
• 4.3 RF Channel Switching in the O-RAN Architecture
• 4.4 RF Channel Switching rApp
• 4.5 rApp Implementation Challenges

Summary and conclusions

Energy efficiency in the RAN is one of the key aspects within the 5G and beyond mobile networks that is currently on the agenda of the industry and research activities. It is usually defined as a ratio between the average user throughput and average power consumption. As the industry is targeting higher and higher throughputs within 5G and beyond, more BSs and related hardware must be deployed. As the installed hardware requires more and more power, the energy-saving mechanisms are of crucial role. Therefore, being able to dynamically control the used resources and hardware by adapting its usage to the actual traffic demand, would bring significant benefits to the MNOs. To address this issue, O-RAN ALLIANCE is currently providing means to support energy-saving topics as one of the key pillars for moving forward within the RAN domain. In this whitepaper, the authors provided an introduction to the topic of energy efficiency and how O-RAN fits into the picture.

In Chapter 1.0, we provide an overview of energy efficiency and how it fits into the O-RAN context. Using the O-RAN entities, namely: Non-RT RIC and Near-RT RIC, the EE of the mobile network can be effectively increased. The dedicated xApps (deployed at Near-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.

While academia has 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 real implementations in the 5G and beyond networks due to the native incorporation of intelligence and open interfaces.

Chapter 2.0 discusses the means to improve energy efficiency in mobile networks. Due to the dense network deployment and utilization of M-MIMO technology in 5G networks, optimization of EE becomes a crucial challenge for MNOs. This is also true concerning 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.

To provide significant network ES, dedicated case-dependent algorithms should be deployed. These are expected to utilize ML techniques to, e.g., switch on/off cells based on long-term characteristics of traffic load, or user mobility. To address this, Chapters 3.0 and 4.0 complement the topic with specific solutions developed as ML-supported rApps for O-RAN use cases.

The implementation of such ML-based algorithms requires both data collection and control actions in RAN. This can be achieved within the O-RAN architecture that provides unified interfaces and a standardized framework for improving the EE of mobile networks with the use of ML models hosted in Non-RT RIC. In Chapter 3.0, we described Cell On/Off Switching rApp adapted by Rimedo Labs from an academic idea to fit the O-RAN architecture. The EE improvement is also one of the key aspects when considering the BSs equipped with large antenna arrays, i.e., M-MIMO BSs. The key challenge is to balance the energy consumption introduced by the multiple RF channels, and the throughput gains achieved by the beamforming. The deployment of such intelligent algorithms is possible only in the O-RAN architecture where third-party software can interact with RAN components at the level of hardware reconfiguration, i.e., activation/deactivation of some part of the antenna array. This is addressed within Chapter 4.0.

The overall conclusions from this whitepaper are as follows:

• Energy efficiency is one of the key items to be addressed for the future of wireless networks and it has to be taken into account in an end-to-end fashion;
• O-RAN provides means to support this by open interfaces, xApps, rApps, ML framework, and energy-saving-related use cases;
• The initial considerations within O-RAN treat switching off/on cells, RF chains, etc.;
• ML-based schemes can significantly improve the operation of the switching off/on BS elements as they need to be based on trends and long-term statistics;
• When working with ES aspects it is important to take into account that the ES algorithms cannot work in a vacuum. The decision on switching off the whole or part of the base station needs to be coordinated with other features;
• When we speak about Open RAN systems and energy-saving features to be used, e.g. as rApps in Non-RT RIC, it would be good to provide e.g. Energy-Saving-Aware Traffic Steering mechanisms, which will do the obvious and move the users out of the cell before we switch it off. This requires coordination and a holistic approach to ES to avoid instability in the network;
• Scaling up/down O-Cloud resources concerning the traffic demand, vs saved energy is part of the equation.

References

[1] 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

[2] 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.

[3] M. Masoudi et al., „Green Mobile Networks for 5G and Beyond,” in IEEE Access, vol. 7, pp. 107270-107299, 2019, DOI: 10.1109/ACCESS.2019.2932777.

[4] 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

[5] M. Dryjanski, R. Lundberg, “The O-RAN Whitepaper; Overview, Architecture, and Traffic Steering Use Case”, 2021, https://rimedolabs.com/blog/the-o-ran-whitepaper/

[6] M. Dryjanski, M. Szydelko, „A unified traffic steering framework for LTE radio access network coordination,” in IEEE Communications Magazine, vol. 54, no. 7, pp. 84-92, July 2016, DOI: 10.1109/MCOM.2016.7509383.

[7] 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.

[8] M. Hoffmann, P. Kryszkiewicz and A. Kliks, „Increasing energy efficiency of the 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
[9] 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
[10] 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.
[11] 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.

[12] O-RAN Alliance, “O-RAN Working Group 1 Use Cases Detailed Specification” v09.00, October 2022
[13] O-RAN Alliance, “O-RAN Working Group 1 Use Cases Analysis Report” v09.00, October 2022

[14] M. Hoffmann, A. Kliks, P. Kryszkiewicz, and G. P. Koudouridis, “A Reinforcement Learning Approach for Base Station On/Off Switching in Heterogeneous M-MIMO Networks,” 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), 2020, pp. 170-172, DOI: 10.1109/WoWMoM49955.2020.00038.

[15] M. Hoffmann and P. Kryszkiewicz, „Radio Environment Map and Deep Q-Learning for 5G Dynamic Point Blanking,” 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2022, pp. 1-3, DOI: 10.23919/SoftCOM55329.2022.9911517.

[16] J. Hoydis, S. ten Brink and M. Debbah, „Massive MIMO: How many antennas do we need?,” 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011, pp. 545-550, DOI: 10.1109/Allerton.2011.6120214.

To download the Whitepaper, go to The O-RAN Whitepaper 2023: Energy Efficiency

Other Resources from Rimedo Labs

• The previous whitepapers: The O-RAN Whitepaper 2022 (RAN Intelligent Controller), The O-RAN Whitepaper 2021
• To check our blog posts on #AllThingsWireless, visit: Blog – RIMEDO Labs
• Webinar: A video recording from an O-RAN Webinar: O-RAN Architecture and Use Cases – YouTube
• To download the slides from the O-RAN Webinar, visit: slides on O-RAN Architecture and Use Cases
• Training: Sign up for our O-RAN System Overview Course.
• Poster (High quality, printer-friendly, A3): Subscribe to get your O-RAN Poster copy.

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