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Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment

Xuanyu Liang, Ahmed Al-Tahmeesschi, Qiao Wang, Swarna Chetty, Chenrui Sun, Hamed Ahmadi

TL;DR

This work targets energy efficiency in dense 5G deployments by leveraging O-RAN's Near-RT RIC to deploy two xApps that control RC sleep states. The proposed methods optimize RC activity while maintaining QoS, using a Bjornson-based RC power model and a realistic UMa channel in a TeraVM RIC-tester environment. Results show substantial power savings (up to ~50%) at low UE densities and meaningful savings at higher loads, with xApp2 outperforming xApp1 as traffic increases. The approach demonstrates the practicality of VM-enabled, data-driven energy management in open RAN architectures and points to future improvements via mobility modeling and deep reinforcement learning.

Abstract

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions.

Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment

TL;DR

This work targets energy efficiency in dense 5G deployments by leveraging O-RAN's Near-RT RIC to deploy two xApps that control RC sleep states. The proposed methods optimize RC activity while maintaining QoS, using a Bjornson-based RC power model and a realistic UMa channel in a TeraVM RIC-tester environment. Results show substantial power savings (up to ~50%) at low UE densities and meaningful savings at higher loads, with xApp2 outperforming xApp1 as traffic increases. The approach demonstrates the practicality of VM-enabled, data-driven energy management in open RAN architectures and points to future improvements via mobility modeling and deep reinforcement learning.

Abstract

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions.
Paper Structure (10 sections, 5 equations, 4 figures, 1 table, 2 algorithms)

This paper contains 10 sections, 5 equations, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Key Innovations of O-RAN
  3. Related Works and Contributions
  4. System Model and Environment
  5. Problem Formulation
  6. RC Switching Algorithm
  7. Proposed xApp1
  8. Proposed xApp2
  9. Results
  10. Conclusions

Figures (4)

  • Figure 1: shows O-RAN Architecture, with components and interfaces from O-RAN and 3GPP. O-RAN interfaces are drawn as solid lines, 3GPP ones as dashed lines. The layout of the O-RU and the distribution of users is shown in the picture in the right section. Each O-RU consists of two RC operating on distinct frequencies, n77 and n78, represented by red and green circles, respectively. Due to the identical coverage areas of both RC, only the color of one RC is discernible in the figure. The color of the UE within the figure matches the RC to which they are connected, indicating their network association based on the respective RC's frequency. The proposed two xApps are anchored in Near-RT RIC
  • Figure 2: shows the flowgraph that how the python code based xApp interact with RIC-tester.
  • Figure 3: shows the power consumption in percentage among 10, 50 and 100 UE in three different scenarios. All on is always in 100% power consumption. xApp1 and xApp2 show a relative percentage of the All on scenario.
  • Figure 4: shows the average (over 10 trials) number of RCs are switched into sleep mode between 10, 50 and 100 UEs in three different scenarios