Design and Implementation of Cloud Radio Access Network (C-RAN) using OMNET++ Platform

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Sura Fawzi Ismail
Dheyaa Jasim Kadhim

Abstract

For fast data rates, commercial 5G wireless communication systems are in use. However, for 5G, the challenge lies in the expansive use of smart devices and the need for ultra-reliable and low-latency communication in Internet of Everything (IoE) services. The data rates achieved in 5G are too low for this exponential increase in traffic and, as such, there is the need for 6G network technologies that can enable this. Cloud Radio Access Network (Cloud-RAN) is one such network that has been proposed for B5G services. In this work, it is proposed to develop and test a C-RAN on the OMNET++ simulator. In this network setup, the BBUs are separated from the RRHs, and the baseband processing tasks are completed in the cloud for cost-effectiveness. Simulations were done on Simu5G for the RRH and user part, and iCanCloud for cloud-based BBUs. Results demonstrate that considerable improvements of up to 30% in energy consumption, 45% in the efficiency of use of resources, and low latency of less than 10ms, as well as stable throughput performance for different user loads, were achieved. Results validate that the implementation of C-RAN can improve efficiency and scalability. The conclusion drawn from this study is that the proposed C-RAN architecture is a viable and impactful solution to meet the growing demands of future wireless networks.

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“Design and Implementation of Cloud Radio Access Network (C-RAN) using OMNET++ Platform” (2026) Journal of Engineering, 32(2), pp. 13–39. doi:10.31026/j.eng.2026.02.02.

References

Alexiou, A., White Paper WWRF contributions towards IMT-2030.

Amiri, E., Wang, N., Shojafar, M., Hamdan, M.Q., Foh, C.H. and Tafazolli, R., 2023. Deep reinforcement learning for robust vnf reconfigurations in o-ran. IEEE Transactions on Network and Service Management, 21(1), pp. 1115-1128. https://doi.org.10.1109/TNSM.2023.3316074.

Beloglazov, A., Abawajy, J. and Buyya, R., 2012. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), pp. 755-768. https://doi.org/10.1016/j.future.2011.04.017.

Castane, G.G., Nunez, A. and Carretero, J., 2012, July. iCanCloud: A brief architecture overview. IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 853-854. IEEE. https://doi.org/10.1109/ISPA.2012.131.

Chabira, C., Shayea, I., Nurzhaubayeva, G., Aldasheva, L., Yedilkhan, D. and Amanzholova, S., 2025. AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks. Technologies, 13(7), P. 276. https://doi.org/10.3390/technologies13070276.

Chen, K., Cui, C., Huang, Y., Huang, B., Wu, J., Rangan, S. and Zhang, H., 2013. C-RAN: A green RAN framework. In Green Communications: Theoretical Fundamentals, Algorithms and Applications, pp. 279-304. CRC Press.

Chen, N., Rong, B., Zhang, X. and Kadoch, M., 2017. Scalable and flexible massive MIMO precoding for 5G H-CRAN. IEEE Wireless Communications, 24(1), pp. 46-52. https://doi.org/10.1109/MWC.2017.1600139WC.

Chughtai, N.A., Ali, M., Qaisar, S., Imran, M., Naeem, M. and Qamar, F., 2018. Energy efficient resource allocation for energy harvesting aided H-CRAN. IEEE Access, 6, pp. 43990-44001. https://doi.org/10.1109/ACCESS.2018.2862920.

Dahrouj, H., Douik, A., Dhifallah, O., Al-Naffouri, T.Y. and Alouini, M.S., 2015. Resource allocation in heterogeneous cloud radio access networks: Advances and challenges. IEEE Wireless Communications, 22(3), pp. 66-73. https://doi.org.10.1109/MWC.2015.7143328.

Ding, Z., Liu, Y., Choi, J., Sun, Q., Elkashlan, M., Chih-Lin, I. and Poor, H.V., 2017. Application of non-orthogonal multiple access in LTE and 5G networks. IEEE Communications Magazine, 55(2), pp. 185-191. https://doi.org.101109/MCOM.2017.1500657CM.

Ejaz, W., Sharma, S.K., Saadat, S., Naeem, M., Anpalagan, A. and Chughtai, N.A., 2020. A comprehensive survey on resource allocation for CRAN in 5G and beyond networks. Journal of Network and Computer Applications, 160, P. 102638. https://doi.org/10.1016/j.jnca.2020.102638.

Farhat, I., Awan, F.G., Rashid, U., Anwaar, H., Khezami, N., Boulkaibet, I., Neji, B. and Nzanywayingoma, F., 2024. Recent trends in cloud radio access networks. IEEE Access. https://doi.org.10.1109/ACCESS.2024.3437196.

Farooqui, M.F., Muqeem, M., Sultan, A., Nazeer, J. and Abdeljaber, H.A., 2023. A fuzzy logic based solution for network traffic problems in migrating parallel crawlers. International Journal of Advanced Computer Science and Applications, 14(2).

Hao, W., Muta, O. and Gacanin, H., 2018. Price-based resource allocation in massive MIMO H-CRANs with limited fronthaul capacity. IEEE Transactions on Wireless Communications, 17(11), pp. 7691-7703. https://doi.org.10.1109/TWC.2018.2869749.

Hossain, M.F., Mahin, A.U., Debnath, T., Mosharrof, F.B. and Islam, K.Z., 2019. Recent research in cloud radio access network (C-RAN) for 5G cellular systems-A survey. Journal of Network and Computer Applications, 139, pp. 31-48. https://doi.org/10.1016/j.jnca.2019.04.019.

Ismail, S.F. and Kadhim, D.J., 2024. Towards 6G technology: Insights into resource management for cloud RAN deployment. IoT, 5(2), pp. 409-448. https://doi.org/10.3390/iot5020020.

Ismail, S.F. and Kadhim, D.J., 2025. Adaptive BBU migration based on deep Q-Learning for Cloud radio access network. Applied Sciences, 15(7), P. 3494. https://doi.org/10.3390/app15073494.

Jahandar, S., Shayea, I., Gures, E., El-Saleh, A.A., Ergen, M. and Alnakhli, M., 2025. Handover decision with multi-access edge computing in 6G networks: A survey. Results in Engineering, P. 103934. https://doi.org/10.1016/j.rineng.2025.103934.

Jiang, D. and Liu, G., 2016. An overview of 5G requirements. 5G Mobile Communications, pp. 3-26.

Kalil, M., Al-Dweik, A., Sharkh, M.F.A., Shami, A. and Refaey, A., 2017. A framework for joint wireless network virtualization and cloud radio access networks for next generation wireless networks. IEEE Access, 5, pp. 20814-20827. https://doi.org.10.1109/ACCESS.2017.2746666.

Kaltenberger, F., Silva, A.P., Gosain, A., Wang, L. and Nguyen, T.T., 2020. OpenAirInterface: Democratizing innovation in the 5G Era. Computer Networks, 176, P. 107284. https://doi.org/10.1016/j.comnet.2020.107284

Kardaras, G. and Lanzani, C., 2009, September. Advanced multimode radio for wireless & mobile broadband communication. In 2009 European wireless technology conference, pp. 132-135. IEEE.

Khani, M., Jamali, S. and Sohrabi, M.K., 2023. An enhanced deep reinforcement learning-based slice acceptance control system (EDRL-SACS) for cloud–radio access network. Physical Communication, 61, P. 102188. https://doi.org/10.1016/j.phycom.2023.102188.

Kolawole, O.Y., Vuppala, S. and Ratnarajah, T., 2017. Multiuser millimeter wave cloud radio access networks with hybrid precoding. IEEE Systems Journal, 12(4), pp. 3661-3672. https://doi.org.10.1109/JSYST.2017.2713463.

Mai, Z., Chen, Y., Xie, Y. and Chen, G., 2023. An energy efficiency optimization jointing resource allocation for delay-aware traffic in fronthaul constrained C-RAN. Wireless Networks, 29(1), pp. 353-368. https://doi.org/10.1007/s11276-022-03118-2.

Meerja, K.A., Shami, A. and Refaey, A., 2015. Hailing cloud empowered radio access networks. IEEE Wireless Communications, 22(1), pp. 122-129. https://doi.org.10.1109/MWC.2015.7054727.

Moura, B.M., Schneider, G.B., Yamin, A.C., Santos, H., Reiser, R.H. and Bedregal, B., 2022. Interval-valued fuzzy logic approach for overloaded hosts in consolidation of virtual machines in cloud computing. Fuzzy Sets and Systems, 446, pp. 144-166. https://doi.org/10.1016/j.fss.2021.03.001.

Nardini, G., Sabella, D., Stea, G., Thakkar, P. and Virdis, A., 2020. Simu5g–an omnet++ library for end-to-end performance evaluation of 5G networks. IEEE Access, 8, pp. 181176-181191. https://doi.org.10.1109/ACCESS.2020.3028550.

Nikaein, N., 2015, September. Processing radio access network functions in the cloud: Critical issues and modeling. In Proceedings of the 6th international workshop on mobile cloud computing and services, pp. 36-43. https://doi.org/10.1145/2802130.28021.

Peng, M., Wang, C., Lau, V. and Poor, H.V., 2015. Fronthaul-constrained cloud radio access networks: Insights and challenges. IEEE Wireless Communications, 22(2), pp. 152-160. https://doi.org.10.1109/MWC.2015.7096298.

Perera, T.D.P., Jayakody, D.N.K., Sharma, S.K., Chatzinotas, S. and Li, J., 2017. Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges. IEEE Communications Surveys & Tutorials, 20(1), pp. 264-302. https://doi.org.10.1109/COMST.2017.2783901.

Perveen, A., Abozariba, R., Patwary, M. and Aneiba, A., 2023. Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks. Neural Computing and Applications, 35(33), pp. 23841-23859. https://doi.org/10.1007/s00521-021-06206-0.

Rangan, S., Rappaport, T.S. and Erkip, E., 2014. Millimeter-wave cellular wireless networks: Potentials and challenges. Proceedings of the IEEE, 102(3), pp. 366-385. https://doi.org.10.1109/JPROC.2014.2299397.

Rodoshi, R.T., Kim, T. and Choi, W., 2020. Resource management in cloud radio access network: Conventional and new approaches. Sensors, 20(9), p.2708. https://doi.org/10.3390/s20092708.

Saatchi, R., 2024. Fuzzy Logic Concepts, Developments and Implementation. Information, 15(10).

Schwarz, S., 2018, December. Dynamic distributed antenna systems: A transitional solution for CRAN implementation. In 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1-7. IEEE. https://doi.org.10.1109/GLOCOMW.2018.8644523.

Stephen, R.G. and Zhang, R., 2018. Uplink channel estimation and data transmission in millimeter-wave CRAN with lens antenna arrays. IEEE Transactions on Communications, 66(12), pp. 6542-6555. https://doi.org.10.1109/TCOMM.2018.2859996.

Wassie, S.F., Di Maio, A. and Braun, T., 2025, March. Deep reinforcement learning for context-aware online service function chain deployment and migration over 6G networks. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, pp. 1361-1370).https://doi.org/10.1145/3672608.3707975

Xia, N., Chen, H.H. and Yang, C.S., 2017. Radio resource management in machine-to-machine communications—A survey. IEEE Communications Surveys & Tutorials, 20(1), pp. 791-828. https://doi.org/10.1109/COMST.2017.2765344.

You, X., Wang, C.X., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J. and Zhu, M., 2021. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China Information Sciences, 64(1), P. 110301. https://doi.org/10.1007/s11432-020-2955-6.

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