Adaptive Network Management in 6G O-RAN: A Framework for Dynamic User Demands
Abstract
6G networks demand unparalleled adaptability and energy efficiency. This work presents a dynamic O-RAN-based framework integrating the SMO, Near-RT RIC, and Non-RT RIC for real-time analytics, intelligent policy application, and adaptive energy management. By deploying rApps (e.g., Energy Savings) and xApps, the framework dynamically clusters and manages radio nodes, optimizing resource allocation while reducing power consumption. A prototype implementation shows substantial improvements over traditional static approaches, notably conserving energy during low-demand periods while sustaining high performance at peak times. Key components enable seamless integration across the network, demonstrating the framework’s capability to adapt to varying demands and establishing a new benchmark for 6G network management.
Keywords:
6G, O-RAN, Adaptive Network Management, Near-RT RIC, Non-RT RIC, rApp, xApp, Energy Efficiency, Dynamic Resource Allocation
References
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Vila, I. et al. (2022). On the implementation of a reinforcement learning-based capacity sharing algorithm in o-ran. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, pages 208–214. Institute of Electrical and Electronics Engineers Inc.
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Abedin, S. F. et al. (2022). Elastic o-ran slicing for industrial monitoring and control: A distributed matching game and deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 71:10808–10822.
Alavirad, M. et al. (2023). O-ran architecture, interfaces, and standardization: Study and application to user intelligent admission control. Frontiers in Communications and Networks, 4.
Almeida, G. M. et al. (2023). RIC-O Efficient placement of a disaggregated and distributed RAN Intelligent Controller with dynamic clustering of radio nodes. IEEE Journal on Selected Areas in Communications, pages 1–1.
Azariah, W. et al. (2022). A survey on open radio access networks: Challenges, research directions, and open source approaches. arXiv Preprint.
Bernardos, C. J. et al. (2019). Network virtualization research challenges. Technical report, IETF.
Beshley, M. et al. (2022). Energy-efficient qoe-driven radio resource management method for 5g and beyond networks. IEEE Access, 10:131691–131710.
Bruno, G. Z. et al. (2023a). Anomaly detection in cloudnative b5g systems using observability and machine learning cots solutions. Journal of Internet Services and Applications, 14:189–199.
Bruno, G. Z. et al. (2023b). RIC-O An Orchestrator for the Dynamic Placement of a Disaggregated RAN Intelligent Controller. In IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 1–2.
Bruno, G. Z. et al. (2024a). Evaluating the deployment of a disaggregated open ran controller on a distributed cloud infrastructure. IEEE Transactions on Network and Service Management, pages 1–1.
Bruno, G. Z. et al. (2024b). O-ran blueprints: Devops platform for automated development and testing environments. Presented at the O-RAN Global PlugFest Spring 2024.
de Lima, H. V. et al. (2022). Controle de admissao para network slicing ciente de recursos de rede e de processamento. In Anais de XL Simpósio Brasileiro de Telecomunicações. Sociedade Brasileira de Telecomunicações.
Hammami, N. and Nguyen, K. K. (2022). On-policy vs. off-policy deep reinforcement learning for resource allocation in open radio access network. In IEEE Wireless Communications and Networking Conference, WCNC, volume 2022-April, pages 1461–1466. Institute of Electrical and Electronics Engineers Inc.
Jaafari, M. E. and Chuberre, N. (2023). Guest editorial ijscn special issue on 3gpp ntn standards for future satellite communications. International Journal of Satellite Communications and Networking, 41:217–219.
Kalntis, M. and Iosifidis, G. (2022). Energy-aware scheduling of virtualized base stations in o-ran with online learning. In 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings, pages 6048–6054. Institute of Electrical and Elec- tronics Engineers Inc.
Kasuluru, V. et al. (2023). On the use of probabilistic forecasting for network analysis in open ran. In 2023 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023, pages 258–263. Institute of Electrical and Electronics Engineers Inc.
Kouchaki, M. et al. (2022). Actor-critic network for o-ran resource allocation: xapp design, deployment, and analysis. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, pages 968–973. Institute of Electrical and Electronics Engineers Inc.
Larsen, L. M. et al. (2019). A survey of the functional splits proposed for 5g mobile crosshaul networks. IEEE Communications Surveys and Tutorials, 21:146–172.
Macedo, C. J. et al. (2022). Improved support for uav-based computer vision applications in search and rescue operations via ran intelligent controllers. In Anais de XL Simpósio Brasileiro de Telecomunicações. Sociedade Brasileira de Telecomunicações.
Morais, F. Z. et al. (2023). Oplaceran - a placement orchestrator for virtualized next-generation of radio access network. IEEE Transactions on Network and Service Management, 20:3274–3288.
Mungari, F. (2021). An rl approach for radio resource management in the o-ran architecture. In Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, volume 2021-July. IEEE Computer Society.
O-RAN Alliance (2023a). O-ran architecture description - v09.00. White paper.
O-RAN Alliance (2023b). O-RAN E2 General Aspects and Principles. Technical Report O-RAN.WG3.E2GAP-R003, O-RAN Alliance.
O-RAN Alliance (2023c). O-ran massive mimo use cases. White Paper O-RAN.WG1.MMIMO-USE-CASES-TR-v01.00.
O-RAN Alliance (2023d). O-RAN Near-RT RIC Architecture 5.0. Technical Report O-RAN.WG3.RICARCH-R003-v05.00, O-RAN Alliance.
O-RAN Alliance (2023e). O-ran slicing architecture. White Paper O-RAN.WG1.Slicing-Architecture-R003-v11.00.
Orhan, O. et al. (2021). Connection management xapp for o-ran ric: A graph neural network and reinforcement learning approach. In Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, pages 936–941. Institute of Electrical and Electronics Engineers Inc.
Rodrigues, K. B. C. et al. (2022). Uma investigação empírica sobre observabilidade em sistemas 5g nativos de nuvem. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 252–265. SBC.
Schiavo, L. et al. (2024). Cloudric: Open radio access network (o-ran) virtualization with shared heterogeneous computing. In ACM International Conference on Mobile Computing and Networking.
Sohaib, R. et al. (2024). Green resource allocation in cloud-native o-ran enabled small cell networks. arXiv preprint arXiv:2407.11563.
Tataria, H. et al. (2021). 6g wireless systems: Vision, requirements, challenges, insights, and opportunities. Proceedings of the IEEE, 109:1166–1199.
Tomkos, I., Klonidis, D., Pikasis, E., and Theodoridis, S. (2020). Toward the 6g network era: Opportunities and challenges. IT Professional, 22:34–38.
Vila, I. et al. (2022). On the implementation of a reinforcement learning-based capacity sharing algorithm in o-ran. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, pages 208–214. Institute of Electrical and Electronics Engineers Inc.
Yoo, H. M., Rhee, J. S., Bang, S. Y., and Hong, E. K. (2022). Load balancing algorithm running on open ran ric. In International Conference on ICT Convergence, volume 2022-October, pages 1226–1228. IEEE Computer Society.
Published
2025-05-19
How to Cite
BRUNO, Gustavo Zanatta; HUFF, Alexandre; BOTH, Cristiano Bonato.
Adaptive Network Management in 6G O-RAN: A Framework for Dynamic User Demands. In: DISSERTATION DIGEST - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 232-241.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2025.6918.
