Energy Savings Dynamic in OpenRAN

Abstract

An important potential to improve energy efficiency (EE) in mobile communication networks is provided by Open Radio Access Networks (OpenRAN). This article examines many approaches and techniques to enhance EE in OpenRAN systems, emphasizing important areas like hardware selection, KPIs, and intelligent orchestration. We go over how to use power-efficient hardware, such as power amplifiers and transceivers, and how crucial KPIs are for controlling and monitoring energy use. The study also looks at how AI and machine learning may be used to optimize EE through traffic shaping and dynamic workload management.

Case studies show how these tactics are used in real-world situations. Examples include cell on/off switching and antenna selection for M-MIMO systems. Performance may be maintained while achieving considerable energy savings by utilizing OpenRAN’s open and modular design. This study offers a thorough examination of how OpenRAN might improve mobile network energy management and lead to more effective and environmentally friendly telecommunications infrastructures.

Introduction

There is a lot of pressure on the telecom sector to cut back on energy use and the carbon footprint that goes along with it. More energy-efficient network solutions are desperately needed as mobile network traffic keeps rising due to rising user demand and the proliferation of connected devices. [1] Despite their advances, traditional Radio Access Network (RAN) designs frequently fail to achieve these energy efficiency targets because of their inflexible and monolithic structures.

To overcome these issues, the Open Radio Access Network (OpenRAN) [3] emerges as a revolutionary solution. OpenRAN is distinguished by its modular design, decoupling of software and hardware components [2], and open interfaces—all of which promote flexibility, cost-effectiveness, and innovation. Improving energy efficiency throughout the whole network infrastructure while maintaining important ideas like cloudification and disaggregation is one of OpenRAN’s main goals [9].

OpenRAN’s energy efficiency depends on a number of important elements. First and foremost, selecting hardware that is power-efficient is crucial. Transceivers and power amplifiers, for example, need to be built with the least amount of energy consumption possible, especially while operating at low loads. Further reducing power consumption can be achieved by integrating commercial off-the-shelf (COTS) servers that are outfitted with low-voltage modules [9].

Furthermore, strong key performance indicators (KPIs) are necessary for controlling and keeping track of energy usage among various network components. With the help of these KPIs, power use can be measured and reported in real time, enabling proactive energy management. O-RU, O-CU, and O-DU specific KPIs and software platforms provide an all-encompassing approach to energy monitoring [10].

Moreover, OpenRAN energy efficiency optimization is greatly aided by intelligent orchestration and automation. Through the utilization of artificial intelligence (AI) and machine learning (ML) algorithms, network managers may dynamically oversee workloads, modify traffic routing, and execute energy-conserving strategies including antenna selection and cell on/off switching depicts in Fig 1. By allowing the network to instantly adjust to changing traffic needs, these technologies dramatically save energy consumption when usage is low [8].

This article explores the approaches and innovations in technology that make OpenRAN a viable way to improve energy efficiency in mobile networks. OpenRAN can effectively address the increasing need for telecommunications infrastructure that is both sustainable and efficient by including power-saving hardware, reliable KPIs, and intelligent orchestration. This study illustrates how OpenRAN has the potential to revolutionize energy management in the telecom sector through in-depth analysis and case studies [7].

Energy Efficient Hardware

• Optimizing hardware efficiency is essential for reducing OpenRAN network energy usage [11]. Important things to think about are:

Power Amplifiers (PAs): Energy consumption may be greatly decreased by designing power amplifiers with the capacity to turn off the majority of transmitter parts while the network is not in use. This entails the use of transceivers and power amplifiers that are energy-efficient and capable of turning off digital front ends when network activity is minimal [12].

Commercial-Off-The-Shelf (COTS) Servers: General Purpose Processors (GPP) are the foundation of COTS servers, which should be built using low-voltage modules and other power-efficient technology. This contributes to ensuring low power consumption while supporting the operations of distributed units (DUs) and centralized units (CUs) [13].

Accelerator Technology: It is essential to carefully choose accelerator technologies, such as embedded Application-Specific Integrated Circuits (eASICs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs). Power efficiency must be maintained while these technologies fulfill the compute-intensive needs for sophisticated radio operations like Massive MIMO [14].

Suggested energy efficiency benchmarks have been established globally for every piece of network hardware. This all-encompassing strategy guarantees that every element contributes to the overall decrease in energy usage [5].

Energy Efficiency Key Performance Indicators (KPIs)

• Robust KPIs that enable the monitoring and reporting of energy usage across various network parts are necessary for effective energy management in OpenRAN. Maintaining high energy efficiency requires proactive energy management and real-time metering, which these KPIs make possible [15].
•  

O-RU KPIs: Among these are the Radio Unit (O-RU) real-time power consumption measurements, which offer crucial information for controlling and maximizing energy use at the radio access layer [4].

O-CU/O-DU KPIs: For both distributed and centralized units, power consumption at the workload and hardware component levels must be measured and reported. This entails monitoring how much power is used by various parts, including network interface cards (NICs), CPUs, and accelerators [16].

O-Cloud KPIs: These comprise gathering energy, power, and environmental measurements as well as reporting them via standardized interfaces. To ensure thorough energy monitoring, the O-Cloud software platform should be able to offer specific KPIs at the workload and platform levels [17].

To ensure accuracy and consistency across OpenRAN interfaces, customized mechanisms for collecting and reporting KPIs are needed. For O-RUs, this includes hierarchical or hybrid management styles, which produce comparable KPI outcomes. Furthermore, in order to precisely monitor power consumption and artificially load the network, realistic load situations need the usage of RF load generators [18].

Features of Energy Efficiency

OpenRAN has a number of functions that may dynamically disable network parts during low demand, which can greatly increase energy efficiency.

Symbol Shutdown: By turning down power amplifiers and other transceiver components at the OFDM symbol granularity, this feature enables O-RUs to go into sleep mode. As a result, there are significant energy savings while the network is not in use [20].

Cell Switch-Off: To save energy, whole cells, or frequency layers, can be turned off, and the majority of RF and digital front-end components can be put in sleep mode. In Fig 2 this functionality may be turned on in a matter of seconds to several minutes, with little to no influence on the quality of the service [21].

RF Transceiver Switch-Off: This entails improving energy consumption under fluctuating traffic situations by lowering the number of MIMO layers and/or scheduling users within QoS limitations.

This means that by reducing the number of MIMO layers and/or scheduling users under QoS constraints, energy usage under varying traffic conditions can be improved [22].

Intelligent Orchestration

By utilizing AI and ML techniques, intelligent orchestration frameworks have the potential to greatly improve OpenRAN network energy efficiency. By allowing for the dynamic control of hardware resources and traffic redirection, these frameworks maximize network energy consumption [23].

Workload Optimization: Energy is not wasted on components that are not being used to their full potential thanks to the hardware resources’ dynamic adaptability to workload demands. This entails adjusting hardware resources in response to demand as it changes in real time [24].

Traffic Steering: By directing traffic to particular cells or frequency bands that use less energy, energy-aware traffic management enables the optimization of cell utilization and lowers power consumption [25].

Automation Framework: Power-saving techniques throughout the RAN infrastructure are automated through the use of RAN Intelligent Controllers (RIC) and Service Management and Orchestration (SMO). This involves using forecast methods to turn on and off enhanced sleep mode features in response to demand and traffic patterns [26].

To attain maximum energy efficiency, SMO-coordinated intelligent management and orchestration of O-CU/O-DU hardware and O-Cloud software resources are crucial. Optimal workload allocation and automated procedures that dynamically adjust active hardware resources to real workload demands are made possible by this framework, freeing up resources that may be turned off during idle periods [27].

Case Study: Cell On/Off Switching

The use of Reinforcement Learning (RL) to cell on/off switching is an illustration of intelligent energy efficiency optimization. This case study illustrates how AI and ML may be used in practice to improve energy efficiency in OpenRAN networks. Fig 3 shows the cell switching On/Off concept.

Scenario: Traffic volume determines whether to activate or deactivate pico base stations (BSs). More Pico-BSs are turned on to manage the load during heavy traffic times, while some are turned off to conserve energy during low traffic times [28].

Algorithm: To optimize energy efficiency, a set of active Pico-BSs is learned by an RL agent. The agent engages with the surroundings, gathering input on energy usage and traffic volume, and modifies its behavior to determine the most effective [29].

Implementation: The Non-Real-Time RAN Intelligent Controller is used to control this solution within the O-RAN architecture (non-RT RIC). The RL agent maintains the most energy-efficient configurations by making decisions based on real-time data from network elements. The Fig 4 shows the Cell On/Off switching in the O-RAN architecture.

The use of these clever optimization strategies shows that OpenRAN has the capacity to save a significant amount of energy while preserving great network performance [30].

Algorithm: Efficient Exponentiation

Input: A non-negative integer nnn and a number xxx.

Output: y=xny = x^ny=xn.

1. Initialize:
   • y←1y, 1y←1
   • X←xX, xX←x
   • N←nN, nN←n
2. While N≠0N, 0N=0:

2. If NNN is even then:
   • X←X×XX, XX←X×X
   • N←N2N, {N}{2}N←2N​
3. Else:
   • y←y×Xy,  Xy←y×X
   • N←N−1N, N – 1N←N−1
4. End If
5. End While

Conclusion

OpenRAN presents a novel approach to improve energy efficiency in mobile communication networks. By integrating complex orchestration frameworks, extensive KPIs, and power-efficient hardware, OpenRAN may achieve significant energy reductions without compromising performance. Installing advanced hardware, such as low-voltage modules and energy-efficient power amplifiers, is the first step towards lowering power usage. Furthermore, the creation of thorough KPIs allows real-time monitoring and proactive management of energy use across several network components.

Intelligent orchestration, powered by artificial intelligence (AI) and machine learning (ML), significantly improves energy efficiency. Techniques like traffic rerouting, cell on/off switching, and dynamic workload management demonstrate how they may be used in practical situations and save a lot of energy. These technologies enable networks to adapt to fluctuating traffic demands and efficiently use energy during periods of low activity.
The capacity of OpenRAN to radically revolutionize energy management in the telecom sector is highlighted in this study. Through in-depth analysis and practical case studies, we show how OpenRAN can help more sustainable and efficient communications infrastructures [6]. As the industry grows, adopting OpenRAN’s open and modular design will be essential to meeting future energy efficiency targets and reducing the environmental impact of mobile networks.

In order to enhance LNA performance, future research should focus on creating adaptive technologies that dynamically adjust to operating conditions and on further integrating state-of-the-art materials like GaN and InP. Exploring the possibilities of edge computing in conjunction with OpenRAN may also be able to maximize efficiency and reach even greater heights in terms of energy savings.

References

1. Gonzalez, G. (1997). Microwave Transistor Amplifiers: Analysis and Design. Prentice Hall.
2. Pozar, D.M. (2012). Microwave Engineering. John Wiley & Sons.
3. Babamir, S.M., & Razavi, B. (2020). A digital RF transmitter with background nonlinearity correction. IEEE Journal of Solid-State Circuits.
4. Lee, T.H. (2003). The Design of CMOS Radio-Frequency Integrated Circuits. Cambridge University Press.
5. Rodwell, M.J., Leuther, A., & Brown, E.R. (2001). High-speed electronics and optoelectronics: devices and circuits. IEEE Transactions on Microwave Theory and Techniques.
6. Cripps, S.C. (2006). RF Power Amplifiers for Wireless Communications. Artech House.
7. Edwards, T.C. (2006). Foundations for Microstrip Circuit Design. Wiley.
8. Rebeiz, G.M. (2003). RF MEMS: Theory, Design, and Technology. Wiley.
9. Habibzadeh, H., Soyata, T., Kantarci, B., Boukerche, A., & Kaptan, C. (2018). Sensing, communication and security planes: A new challenge for a smart city system design. Computer Networks, 144, 163-200.
10. Hojeij, H., Sharara, M., Hoteit, S., & Veque, V. (2023, September). Dynamic placement of O-CU and O-DU functionalities in open-ran architecture. In 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 330-338). IEEE.
11. Feng, D., Jiang, C., Lim, G., Cimini, L. J., Feng, G., & Li, G. Y. (2012). A survey of energy-efficient wireless communications. IEEE Communications Surveys & Tutorials, 15(1), 167-178.
12. Joung, J., Ho, C. K., Adachi, K., & Sun, S. (2014). A survey on power-amplifier-centric techniques for spectrum-and energy-efficient wireless communications. IEEE Communications Surveys & Tutorials, 17(1), 315-333.
13. Molineux, J. S. (2012). Integration of Wireless Sensor Networks Into a Commercial Off-the-Shelf (COTS) Multimedia Network (Doctoral dissertation, Monterey, California: Naval Postgraduate School).
14. Hempstead, M., Wei, G. Y., & Brooks, D. (2009, October). An accelerator-based wireless sensor network processor in 130nm CMOS. In Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems (pp. 215-222).
15. Gandhi, A. D., & Newbury, M. E. (2011). Evaluation of the energy efficiency metrics for wireless networks. Bell Labs Technical Journal, 16(1), 207-215.
16. OTE, L. B., & OTE, A. K. Deliverable D2. 1 Description and definition of targeted PoCs.
17. Puthenpura, S., Sloane, O. T., & Dryjański, O. M. SMaRT-5G Project.
18. Martín‐Pérez, J., Magoula, L., Antevski, K., Guimarães, C., Baranda, J., Fabiana Chiasserini, C., … & Zeydan, E. (2021). Self‐Managed 5G Networks. Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning, 69-100.
19. Mongay Batalla, J., Moshin, M., Mavromoustakis, C. X., Wesołowski, K., Mastorakis, G., & Krzykowska-Piotrowska, K. (2022). On deploying the internet of energy with 5g open ran technology including beamforming mechanism. Energies, 15(7), 2429.
20. Larsen, L. M., Christiansen, H. L., Ruepp, S., & Berger, M. S. (2023). Toward greener 5G and beyond radio access networks—A survey. IEEE Open Journal of the Communications Society, 4, 768-797.
21. Kooshki, F. (2023). Open cell-less network architecture and radio resource management for future wireless communication systems. https://patents. google. com/patent/US20220210794A1.
22. Haslhofer, F. (2020). OpenSafety for Wired and Wireless Connections over MQTT/submitted by Florian Haslhofer.
23. Bosch, P., De Schepper, T., Zeljković, E., Famaey, J., & Latré, S. (2020). Orchestration of heterogeneous wireless networks: State of the art and remaining challenges. Computer Communications, 149, 62-77.
24. Sun, C., Gao, X., & Ding, Z. (2022). Optimization of Workload Balancing and Power Allocation for Wireless Distributed Computing. IEEE Transactions on Wireless Communications, 21(9), 7682-7695.
25. Zhang, S., Zhang, N., Zhou, S., Niu, Z., & Shen, X. S. (2016). Wireless Traffic Steering for Green Cellular Networks. Springer.
26. Batra, I., Verma, S., Kavita, & Alazab, M. (2020). A lightweight IoT‐based security framework for inventory automation using wireless sensor network. International journal of communication systems, 33(4), e4228.
27. Bradac, Z., Fiedler, P., Cach, P., & Vrba, R. (2003, December). Wireless communication in automation. In 10th IEEE International Conference on Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 (Vol. 2, pp. 659-662). IEEE.
28. Oh, E., Son, K., & Krishnamachari, B. (2013). Dynamic base station switching-on/off strategies for green cellular networks. IEEE transactions on wireless communications, 12(5), 2126-2136.
29. Konishi, S. (2013). Comprehensive analysis of heterogeneous networks with pico cells in LTE-advanced systems. IEICE transactions on communications, 96(6), 1243-1255.
30. Niknam, S., Roy, A., Dhillon, H. S., Singh, S., Banerji, R., Reed, J. H., … & Yoon, S. (2022, December). Intelligent O-RAN for beyond 5G and 6G wireless networks. In 2022 IEEE Globecom Workshops (GC Wkshps) (pp. 215-220). IEEE.