雲端化無線存取網路 (Cloud Radio Access Network, C-­RAN)為第五代行動通訊系統 (5th Generation Mobile Communication System, 5G)系統提供寬頻服務的新型態網路架構，其將傳統基地台分成兩部分:基頻處理單元 (Baseband Units, BBU)與遠端單元 (Remote Radio Unit, RRU)，並將BBU從基地台 (Base Station, BS)分離並放置於雲端，以達成集中管理。本論文透過差分進化 (Differential Evolution, DE)演算法進行BBU和RRU的關聯，使服務品質 (Quality of Service, QoS)達到較佳的狀態，並且收斂到最佳值，再使用RRU聚合 (Aggregation)方法，將相鄰的RRU分配到相同扇區 (Sector)，減少BBU的交遞 (Handover, HO)。透過模擬分析重新關聯率，相較於文獻上之相關文獻，本論文所提方法可達到使用者裝置 (User Equipment, UE)交遞頻率優化25%以上減少使用者交遞率，且透過計算負載公平性，可實現本論文所提方法在扇區與BBU中可達到負載平衡 (Load Balancing)。

The cloud radio access network (C-RAN) is a new architecture to provide broadband services for the fifth-generation (5G) mobile communication system by separating the baseband unit (BBU) and the remote radio unit (RRU) and allowing BBUs to be placed in the cloud for the centralized operation and scalable deployment of light-weight RRUs. In this thesis, the differential evolution (DE) algorithm is employed to associate BBUs and RRUs so that the quality of service (QoS) can reach the better one and converge to the best one. Further applying the graph-based RRUs aggregation method, adjacent (neighboring) RRUs can be managed by the same sector, reducing the BBU switching accordingly. Via the re-associated rate obtained by simulations, the averaged handover (HO) rate reduction achieved by our proposed mechanism as compared to the closely related mechanisms in the literature can be more than 25%. Our proposed mechanism also achieves load balancing between sectors and BBUs in terms of the fairness index.

[1] R. T. Rodoshi, T. Kim, and W. Choi, “Resource management in cloud radio access network: Conventional and new approaches,” Sensors, vol. 20, no. 9, pp. 2708–2740, Apr. 2020.
[2] R. S. Alhumaima and H. S. Al-Raweshidy, “Optimising the BBU pool placement in cloud radio access networks based on power allocations,” in Proc. Computing Conference, pp. 1312–1316, Jul. 2017.
[3] O. Chabbouh, S. Ben Rejeb, N. Nasser, N. Agoulmine, and Z. Choukair, “Novel cloud-RRH architecture with radio resource management and QoS strategies for 5G HetNets,” IEEE Access, vol. 8, pp. 164815–164832, Sep. 2020.
[4] Z. H. Fakhri, M. Khan, F. Sabir, and H. S. Al-Raweshidy, “A resource allocation mechanism for cloud radio access network based on cell differentiation and integration concept,” IEEE Transactions on Network Science and Engineering, vol. 5, no. 4, pp. 261–275, Oct. 2017.
[5] L. Chen, T. Nguyen, D. Yang, M. Nogueira, C. Wang, and D. Zhang, “Data-driven C-RAN optimization exploiting traffic and mobility dynamics of mobile users,” IEEE Transactions on Mobile Computing, vol. 19, no. 1, pp. 1–16, May. 2020, doi : 10.1109/TMC.2020.2971470.
[6] G. Forecast, “Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022,” Cisco Public Information, vol. 1, no. 4, pp. 2022–2058, Feb. 2019.
[7] Tingting Duan, Min Zhang, Zhilong Wang, and Chuang Song, “Inter-BBU control mechanism for load balancing in C-RAN-based BBU pool,” in Proc. IEEE International Conference on Computer and Communications (ICCC), pp. 2960–2964, Oct. 2016.
[8] W. Al-Zubaedi, Planning a C-RAN Deployment for the Next Generation Cellular Networks. Brunel University London, 2019.
[9] M. Mitchell, An Introduction to Genetic Algorithms. MIT press, 1998.
[10] M. Khan, R. S. Alhumaima, and H. S. Al-Raweshidy, “Quality of service aware
dynamic BBU-RRH mapping in cloud radio access network,” in Proc. International Conference on Emerging Technologies (ICET), pp. 1–5, Dec. 2015.
[11] E. A. Ramos da Paixão, R. F. Vieira, W. V. Araújo, and D. L. Cardoso, “Optimized load balancing by dynamic BBU-RRH mapping in C-RAN architecture,” in Proc. Third International Conference on Fog and Mobile Edge Computing (FMEC), pp. 100–104, Apr. 2018.
[12] H. Holm, A. Checko, R. Al-obaidi, and H. Christiansen, “Optimal assignment of cells in C-RAN deployments with multiple BBU pools,” in Proc. European Conference on Networks and Communications (EuCNC), pp. 205–209, Jul. 2015.
[13] M. Mouawad, Z. Dziong, and K. Addali, “RRH selection and load balancing through dynamic BBU-RRH mapping in C-RAN,” in Proc. IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pp. 1–5, May. 2019.
[14] Yarpiz, Differential Evolution (DE). MATLAB Central File Exchange, 2020.
[15] K. Boulos, BBU-RRH Association Optimization in Cloud-Radio Access Networks. PhD Thesis, Jul. 2019.
[16] K. Boulos, M. El Helou, and S. Lahoud, “RRH clustering in cloud radio access networks,” in Proc. International Conference on Applied Research in Computer Science and Engineering (ICAR), pp. 1–6, Oct. 2015.
[17] K. Wang, W. Zhou, and S. Mao, “On joint BBU/RRH resource allocation in heterogeneous cloud-RANs,” IEEE Internet of Things Journal, vol. 4, no. 3, pp. 749–759, Jun. 2017.
[18] M. Y. Lyazidi, N. Aitsaadi, and R. Langar, “Dynamic resource allocation for cloud-RAN in LTE with real-time BBU/RRH assignment,” in Proc. IEEE international
conference on communications (ICC), pp. 1–6, Jul. 2016.
[19] D. Naboulsi, A. Mermouri, R. Stanica, H. Rivano, and M. Fiore, “On user mobility in dynamic cloud radio access networks,” in Proc. IEEE Conference on Computer Communications (INFOCOM), pp. 1583–1591, Apr. 2018.
[20] M. Khan, R. S. Alhumaima, and H. S. AlRaweshidy, “QoS-aware dynamic RRH
allocation in a self-optimized cloud radio access network with RRH proximity constraint,” IEEE Transactions on Network and Service Management, vol. 14, no. 3,
pp. 730–744, Jun. 2017.
[21] M. Khan, Z. H. Fakhri, and H. S. Al-Raweshidy, “Semistatic cell differentiation and integration with dynamic BBU-RRH mapping in cloud radio access network,” IEEE Transactions on Network and Service Management, vol. 15, no. 1, pp. 289–303, Nov. 2017.
[22] M. Khan, F. A. Sabir, and H. S. Al-Raweshidy, “Load balancing by dynamic BBU-RRH
mapping in a self-optimised cloud radio access network,” in Proc. International Conference on Telecommunications (ICT), pp. 1–5, May 2017.
[23] S. Warier, Engineering Optical Networks. Artech House, Dec. 2017.
[24] M. Khan and H. S. Al-Raweshidy, “Exploiting the capacity-routing ability of a cloud radio access network,” in Proc. International Conference on Information and Communication Technology Convergence (ICTC), pp. 448–453, Oct. 2018.
[25] M. Mouawad, Z. Dziong, and M. Khan, “Quality of service aware dynamic BBU-RRH
mapping based on load prediction using Markov model in C-RAN,” in Proc. IEEE International Conference on Internet of Things (iThings), pp. 1907–1912, Aug. 2018.
[26] M. Mouawad, Z. Dziong, and A. El-Ashmawy, “Load balancing in 5G C-RAN based
on dynamic BBU-RRH mapping supporting IoT communications,” in Proc. IEEE Global Conference on Internet of Things (GCIoT), pp. 1–6, Dec. 2018.
[27] S. Namba, T. Warabino, and S. Kaneko, “BBU-RRH switching schemes for centralized
RAN,” in Proc. International Conference on Communications and Networking in China, pp. 762–766, Aug. 2012.
[28] C. I, J. Huang, R. Duan, C. Cui, J. Jiang, and L. Li, “Recent progress on C-RAN centralization and cloudification,” IEEE Access, vol. 2, pp. 1030–1039, Aug. 2014.
[29] K. Boulos, M. E. Helou, K. Khawam, M. Ibrahim, S. Martin, and H. Sawaya, “RRH clustering in cloud radio access networks with re-association consideration,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, Apr. 2018.
[30] D. Zhang and B. Wei, “Comparison between differential evolution and particle swarm optimization algorithms,” in Proc. IEEE International Conference on Mechatronics and Automation, pp. 239–244, Aug. 2014.
[31] V. Kachitvichyanukul, “Comparison of three evolutionary algorithms: GA, PSO, and DE,” Industrial Engineering and Management Systems, vol. 11, no. 3, pp. 215–223, Sep. 2012.
[32] S. Sheikh, R. Wolhuter, and H. A. Engelbrecht, “An adaptive congestion control and fairness scheduling strategy for wireless mesh networks,” in Proc. IEEE Symposium Series on Computational Intelligence, pp. 1174–1181, Dec. 2015.