Enhancing the Handover Algorithm with an Intelligent Approach in the O-RAN Architecture
Resumo
O-RAN is an architecture that promotes interoperability and openness in 5G Radio Access Networks (RAN) using scheduling, disaggregation, and virtualization. RICs (RAN Intelligence Controllers) offer solutions such as Machine Learning (ML), traffic steering, anomaly detection, and QoS (Quality of Service) support. Novel intelligent handover strategies are critical to the success of 5G or even 6G O-RAN-based networks. This paper proposes and evaluates an intelligent handover algorithm for O-RAN environments. It leverages an LTE testbed featuring O-RAN architecture to assess downlink and uplink performance across various User Equipment (UE) scenarios. The proposed scheme was implemented and tested using ns-O-RAN, an O-RAN system integrated with the NS-3 simulator. Our simulator results demonstrate a throughput and delay enhancement compared to traditional handover methods across various scenarios involving 50 to 100 UEs.Referências
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Polese, M., Bonati, L., D’oro, S., Basagni, S., and Melodia, T. (2023). Understanding o-ran: Architecture, interfaces, algorithms, security, and research challenges. IEEE Communications Surveys & Tutorials.
Riccio, E. L., Mangipudi, P. K., and McNair, J. (2023). O-ran signaling optimizations for improved iot handover performance in 5g networks. In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, pages 454–455.
Riyanto, Q. T., Astuti, R. P., and Nashiruddin, M. I. (2023). Techno-economic analysis of 5g network using open ran technology at 2.3 ghz frequency for dense urban. In 2023 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), pages 132–137.
Sahbafard, A., Schmidt, R., Kaltenberger, F., Springer, A., and Bernhard, H.-P. (2023). On the performance of an indoor open-source 5g standalone deployment. In 2023 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6.
Sun, Y., Jiang, W., Feng, G., Klaine, P. V., Zhang, L., Imran, M. A., and Liang, Y.-C. (2020). Efficient handover mechanism for radio access network slicing by exploiting distributed learning. IEEE Transactions on Network and Service Management, 17(4):2620–2633.
Wang, L., Han, D., Zhang, M., Wang, D., and Zhang, Z. (2021). Deep reinforcement learning-based adaptive handover mechanism for vlc in a hybrid 6g network architecture. IEEE Access, 9:87241–87250.
Zangooei, M., Golkarifard, M., Rouili, M., Saha, N., and Boutaba, R. (2023). Flexible ran slicing in open ran with constrained multi-agent reinforcement learning. IEEE Journal on Selected Areas in Communications.
Zhang, H., Zhou, H., and Erol-Kantarci, M. (2022). Team learning-based resource allocation for open radio access network (o-ran). In IEEE International Conference on Communications (ICC), pages 4938–4943.
Baldesi, L., Restuccia, F., and Melodia, T. (2022). Charm: Nextg spectrum sharing through data-driven real-time o-ran dynamic control. In IEEE Conference on Computer Communications (INFOCOM 2022), pages 240–249.
Bonati, L., D’Oro, S., Polese, M., Basagni, S., and Melodia, T. (2021). Intelligence and learning in o-ran for data-driven nextg cellular networks. IEEE Communications Magazine, 59(10):21–27.
Bonati, L., Polese, M., D’Oro, S., Basagni, S., and Melodia, T. (2022). Openran gym: An open toolbox for data collection and experimentation with ai in o-ran. In 2022 IEEE Wireless Communications and Networking Conference (WCNC), pages 518–523. IEEE.
Brik, B., Chergui, H., Zanzi, L., Devoti, F., Ksentini, A., Siddiqui, M. S., Costa-Pérez, X., and Verikoukis, C. (2023). A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions. arXiv preprint arXiv:2307.00319.
Costa, A., Pacheco, L., Rosário, D., Villas, L., Loureiro, A. A., Sargento, S., and Cerqueira, E. (2020). Skipping-based handover algorithm for video distribution over ultra-dense vanet. Computer Networks, 176:107252.
de Oliveira, W., Batista Jr, J. O. R., Novais, T., Takashima, S. T., Stange, L. R., Martucci Jr, M., Cugnasca, C. E., and Bressan, G. (2023). Opencare5g: O-ran in private network for digital health applications. Sensors, 23(2):1047.
Garcia-Saavedra, A. and Costa-Perez, X. (2021). O-ran: Disrupting the virtualized ran ecosystem. IEEE Communications Standards Magazine, 5(4):96–103.
Gavrilovska, L., Rakovic, V., and Denkovski, D. (2020). From cloud ran to open ran. Wireless Personal Communications, 113:1523–1539.
Hamdan, M. Q., Lee, H., Triantafyllopoulou, D., Borralho, R., Kose, A., Amiri, E., Mulvey, D., Yu, W., Zitouni, R., Pozza, R., et al. (2023). Recent advances in machine learning for network automation in the o-ran. Sensors, 23(21):8792.
Lacava, A., Bordin, M., Polese, M., Sivaraj, R., Zugno, T., Cuomo, F., and Melodia, T. (2023a). ns-o-ran: Simulating o-ran 5g systems in ns-3. In Proceedings of the 2023 Workshop on ns-3, pages 35–44.
Lacava, A., Polese, M., Sivaraj, R., Soundrarajan, R., Bhati, B. S., Singh, T., Zugno, T., Cuomo, F., and Melodia, T. (2023b). Programmable and customized intelligence for traffic steering in 5g networks using open ran architectures. IEEE Transactions on Mobile Computing.
Linsalata, F., Moro, E., Gjeci, F., Magarini, M., Spagnolini, U., and Capone, A. (2024). Addressing control challenges in vehicular networks through o-ran: a novel architecture and simulation framework. IEEE Transactions on Vehicular Technology.
Musa, A. A., Hussaini, A., Qian, C., Guo, Y., and Yu, W. (2023). Open radio access networks for smart iot systems: State of art and future directions. Future Internet, 15(12):380.
O-RAN Alliance (2021). O-ran alliance introduces 48 new specifications released since july 2021.
Polese, M., Bonati, L., D’oro, S., Basagni, S., and Melodia, T. (2023). Understanding o-ran: Architecture, interfaces, algorithms, security, and research challenges. IEEE Communications Surveys & Tutorials.
Riccio, E. L., Mangipudi, P. K., and McNair, J. (2023). O-ran signaling optimizations for improved iot handover performance in 5g networks. In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, pages 454–455.
Riyanto, Q. T., Astuti, R. P., and Nashiruddin, M. I. (2023). Techno-economic analysis of 5g network using open ran technology at 2.3 ghz frequency for dense urban. In 2023 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), pages 132–137.
Sahbafard, A., Schmidt, R., Kaltenberger, F., Springer, A., and Bernhard, H.-P. (2023). On the performance of an indoor open-source 5g standalone deployment. In 2023 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6.
Sun, Y., Jiang, W., Feng, G., Klaine, P. V., Zhang, L., Imran, M. A., and Liang, Y.-C. (2020). Efficient handover mechanism for radio access network slicing by exploiting distributed learning. IEEE Transactions on Network and Service Management, 17(4):2620–2633.
Wang, L., Han, D., Zhang, M., Wang, D., and Zhang, Z. (2021). Deep reinforcement learning-based adaptive handover mechanism for vlc in a hybrid 6g network architecture. IEEE Access, 9:87241–87250.
Zangooei, M., Golkarifard, M., Rouili, M., Saha, N., and Boutaba, R. (2023). Flexible ran slicing in open ran with constrained multi-agent reinforcement learning. IEEE Journal on Selected Areas in Communications.
Zhang, H., Zhou, H., and Erol-Kantarci, M. (2022). Team learning-based resource allocation for open radio access network (o-ran). In IEEE International Conference on Communications (ICC), pages 4938–4943.
Publicado
24/05/2024
Como Citar
VILHENA, Kleber; ROCHA, Carlos; VEIGA, Rafael; BASTOS, Lucas; CERQUEIRA, Eduardo; ROSÁRIO, Denis.
Enhancing the Handover Algorithm with an Intelligent Approach in the O-RAN Architecture. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 29. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 98-111.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2024.3249.
