近年來，第五代行動通訊系統(5G)逐漸成為一項熱門議題被廣泛地討論。5G網路將為異質性網路(Heterogeneity Network, HetNet)架構，以便向下相容更多網路環境，諸如:密集性網路(Ultra Dense Network, UDN)、蜂巢式網路(Cellular Network)以及機器對機器(Machine to Machine，M2M)溝通等，上述所提之通訊技術容易使得網路資源難於管理。同時，資源分配於無線網路環境中一直為重要議題，儘管於5G網路中所使用頻率更高和更大的頻寬。考慮到頻譜仍是有限資源，但多數使用者皆希望能夠取得最佳服務品質，故該如何進行頻譜資源的分配及管理，成為5G網路環境裡重大問題。
雲端無線接取網路(Cloud Radio Access Network，C-RAN)或許是解決此項問題有效方法之一，雲端無線電接取網路有別於傳統網路架構，於此架構下之基頻單元 (Baseband Unit, BBU)與無線寬頻頭端設備(Remote Radio Head ,RRH)會從基地台(Macro Base Station, MBS)中獨立出來，除此之外，多個基頻單元將會構成基頻單元池(Baseband Unit pool, BBU pool)，藉由基頻單元池能夠達成資源集中化，使整體網路資源更加便於管理。
為能夠有效解決資源分配問題使其頻譜能夠達到最大利用率，並且使整體網路能源使用率能夠最佳。有鑑於在5G網路環境中，行動裝置的增加以及流量的迅速成長，本文將針對使用者所發出之任務請求進行排程，並以馬可夫鏈(Markov Chain)對頻道狀態進行初始化排列，最後，為有效進行任務排程以達到目標最小化能源效率，將利用修改後之粒子群演算法針對任務排程進行最佳化。為驗證本研究所提出方法之效果，本文利用模擬進行實驗驗證，並與隨機排程及先進先出演算法進行比較。由結果能夠得知，利用所提出方法確實能夠對所請求之任務進行有效地排程；同時，也能夠有效降低整體能源消耗。

Recently, the 5th generation mobile networks systems 5G is a popular issue and discussed widely. The architecture of 5G is heterogeneity network (HetNet), and it can support more network environment, like the ultra-dense network (UDN), traditional cellular network and Machine to Machine (M2M) communication. Therefore, the network resource management will be difficult. Resource allocation is an important issue at the wireless network, although the high frequency and bigger bandwidth are used. Consider the spectrum resource is limited, but almost user with the hope that equipment (UEs) can get the better quality of services (QoS), how to manage the spectrum resource and allocation is a big problem in 5G.
The cloud radio access network (C-RAN) maybe one of the promising solutions for this problem and it is different from the traditional wireless mobile network. The baseband unit (BBU) and radio remote head (RRH) will be independent on the base station (BS). However, when many BBU are integrated it can be a BBU pool, and by doing this, BBU pool can easily manage the network resource and improve the energy-efficiency. According to fast grown-up devices and traffic, we will focus on task scheduling with UEs.
To solve the problem of resource allocation and maximum the energy-efficiency, we propose the use of Markov chain to predict the channel state. On the other hand, the task scheduling the modified particle swarm optimization (MPSO) will be used in this thesis to find the best scheduling. In this thesis, we will use the simulation to verify the performance for our proposed mechanism and compare with random and first in first out. The result shows our mechanism can be scheduled the task efficiently; at the same time, it can reduce total power consumption too.

第一章 緒論 1
第二章 背景知識 6
第三章 問題定義 12
第四章 基於馬可夫鏈頻道狀態預測結合MPSO之任務排程 18
第五章 實驗模擬結果 25
第六章 結論 37

[1]“Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update”, 2015–2020, San Jose, CA, USA, Cisco, White Paper, 3 February, 2016. [Online].Available: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html
[2] RWS-150073, “RAN workshop on 5G: Chairman Summary,” 3GPP RAN workshop, September, 2015.
[3] A. Gupta, R. K. Jha, “A survey of 5G network: Architecture and Emerging Technologies,” IEEE Access, Vol. 3, pp.1206-1232, July, 2015.
[4] S. C. Hung, H. Hsu, S. Y. Lien, K. C. Chen, “ Architecture Harmonization between Cloud Radio Access Networks and Fog Networks, ” IEEE Access, Vol.3, pp.3019-3034, December, 2015.
[5] C. Mouradian, D. Naboulsi, S. Yangui, R. H. Glitho, M. J. Morrow, P. A. Polakos, “A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges,” IEEE Communications Surveys & Tutorials, Vol.20, No.1, pp.416-464, November, 2017.
[6] T. Li, C. S. Magurawalage, K. Wang, K. Xu, K. Yang, H. Wang,“ On Efficient Offloading Control in Cloud Radio Access Network with Mobile Edge Computing,” IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA, 5-8 June, 2017.
[7] Y. Wang, Z. Ren, H. Zhang, X. Hou, Y. Xiao, “ Combat Cloud-Fog” Network Architecture for Internet of Battlefield Things and Load Balancing Technology,” IEEE International Conference on Smart Internet of Things, Xi'an, China, 17-19 August, 2018.
[8] B. V. Natesha, G. Ram Mohana Reddy,“ Heuristic-Based IoT Application Modules Placement in the Fog-Cloud Computing Environment,” IEEE/ACM International Conference on Utility and Cloud Computing Companion, Zurich, Switzerland, 17-20 December, 2018.
[9] S. Kallam, M. R. Babu, C. Y. Chen, R. Patan, D. Cheelu,“ Low Energy Aware Communication Process in IoT Using The Green Computing Approach,” Institution of Engineering and Technology Networks, Vol. 7, No. 4, pp. 258-264, July, 2018.
[10] A. A. Salem, S. El-Rabaie, M. Shokair, “Energy Efficient Ultra-dense Networks based on Multi-objective Optimisation Framework,” Institution of Engineering and Technology Networks, Vol.7, No.6, pp. 398-405, August, 2018.
[11] R. Bassoli, M. Di Renzo, F. Granelli, “Analytical Energy-efficient Planning of 5G Cloud Radio Access Network,” IEEE International Conference on Communications (ICC), Paris, France, 21-25 May, 2017.
[12] Q. Liu, T. Han, N. Ansari, “Energy-Efficient On-Demand Cloud Radio Access Networks Virtualization,” IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab, 9-13 December, 2018.
[13] R. S. Alhumaima, R. K. Ahmed, H. S. Al-Raweshidy,“ Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines,” IEEE Transactions on Green Communications and Networking, Vol. 2, No. 4, pp. 992-1001, December, 2018.
[14] W. Xia, J. Zhang, T. Q. S. Quek, S. Jin, H. Zhu, “Energy-efficient Task Scheduling and Resource Allocation in Downlink C-RAN,” IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15-18 April, 2018.
[15] X. Wang, K. Wang, S. Wu, S. Di, H. Jin, K. Yang, S. Ou,“ Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network,” IEEE Transactions on Parallel and Distributed Systems, Vol.29, No.11, pp.2429-2445, May, 2018.
[16] W. Xia, J. Zhang, T. Q. S. Quek, S. Jin, H. Zhu,“ Power Minimization-Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN,” IEEE Transactions on Wireless Communications, Vol.17, No.11, pp.7268-7280, November, 2018.
[17] U. Rugwiro, C. Gu, W. Ding,“ Task Scheduling and Resource Allocation Based on Ant-Colony Optimization and Deep Reinforcement Learning,” Journal of Internet Technology, Vol.20, No.5, pp.1463-1475, September, 2019.
[18] P. H. Huang, H. Kao, W. Liao, “Hierarchical Cooperation in Heterogeneous Cloud Radio Access Networks,” IEEE International Conference on Communications Workshops (ICC Workshops), Kuala Lumpur, Malaysia, 22-27 May, 2016.
[19] A. Gupta, R. K. Jha, “A Survey of 5G Network: Architecture and Emerging Technologies,” IEEE Access, Vol.3, pp. 1206-1232, July, 2015.
[20] J. Song, T. Yoo, P. J. Song,“ Mobility Level Management for 5G Network,” International Conference on Information and Communication Technology Convergence (ICTC), pp. 940-943, Jeju Island, Korea, 19-21 October, 2016.
[21] X. Wang, C. Cavdar, L. Wang, M. Tornatore, H. S. Chung, H. H. Lee, S. M. Park, B. Mukherjee,“ Virtualized Cloud Radio Access Network for 5G Transport,” IEEE Communications Magazine, Vol.55, No. 9, pp.202-209, September, 2017.
[22] S. Xu, S. Wang, “Baseband Unit Pool Planning for Cloud Radio Access Networks: An Approximation Algorithm,” IEEE Communications Letters, Vol.21, No.2, pp.358-361, October, 2016.
[23] M. A. Habibi, M. Nasimi, B. Han, H. D. Schotten, “A Comprehensive Survey of RAN Architectures towards 5G Mobile Communication System,” IEEE Access, Vol. 7, pp. 70371-70421, May, 2019.
[24] M. Khan, F. A. Sabir, H. S. Al-Raweshidy,“ Load Balancing by Dynamic BBU-RRH Mapping in a Self-optimized Cloud Radio Access Network,” 24th International Conference on Telecommunications (ICT), Limassol, Cyprus, 3-5 May, 2017.
[25] R. Eberhart, J. S. Kennedy,“ A New Optimizer Using Particle Swarm Theory,” Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4-6 October, 1995.
[26] 林大為，「結合模擬退火之改良粒子群演算法於結構最佳化設計的研究」，國立中央大學，碩士論文，民國九十八年。
[27] S. Rajeev, C. S. Krishnamoorthy, “Genetic Algorithm-based Methodologies for Design Optimization of Trusses,” Journal of Structural Engineering, Vol.123, No.3, pp.350-358, August, 1998.
[28] J. Yang, C. K. Soh,“ Structural Optimization by Genetic Algorithms with Tournament Selection,” Journal of Computing in Civil Engineering, Vol. 11, No. 3, pp. 195-200, July, 1997.
[29] Y. Shi, B. Gireesha, “Empirical Study of Particle Swarm Optimization,” Congress on Evolutionary Computation-CEC99, Vol.3, February 1999.
[30] Z. Zhao, M. Peng, Z. Ding, W. Wang, H. V. Poor, “ Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks,” IEEE Journal on Selected Areas in Communications, Vol.34, No.5, pp.1207-1221, March 2016.
[31] T. X. Tran, D. V. Le, G. Yue, D. Pompili,“ Cooperative Hierarchical Caching and Request Scheduling in a Cloud Radio Access Network,” IEEE Transactions on Mobile Computing, Vol.17, No. 12, pp. 2729-2743, December, 2018.
[32] M. Peng, K. Zhang, J. Jiang, J. Wang, W. Wang,“ Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks,” IEEE Transactions on Vehicular Technology, Vol. 64, No. 11, pp. 5275-5287, November, 2014.
[33] Z. Deze, J. Zhang, L. Gu, S. Guo, J. Luo,“ Energy-Efficient Coordinated Multipoint Scheduling in Green Cloud Radio Access Network,” IEEE Transactions on Vehicular Technology, Vol.67, No.10, pp.9922-9930, August, 2018.
[34] M. Awais, A. Ahmed, M. Naeem, M. Iqbal, W. Ejaz, A. Anpalagan, H. S. Kim,“ Efficient Joint User Association and Resource Allocation for Cloud Radio Access Networks,” IEEE Access, Vol. 5, pp. 1439-1448, February, 2017.
[35] O. Chabbouh, S. B. Rejeb, N. Agoulmine, Z. Choukair,“ Service Scheduling Scheme based Load Balancing for 5G/HetNets Cloud Ran,” IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, Taiwan, 27-29 March, 2017.
[36] W. Xia, T. Q. S. Quek, J. Zhang, S. Jin, H. Zhu,“ Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation,” IEEE Transactions on Wireless Communications, Vol. 18, No. 3, pp. 2003-2016, March, 2019.
[37] U. Karneyenka, K. Mohta, M. Moh, “Location and Mobility Aware Resource Management for 5G Cloud Radio Access Networks,” International Conference on High Performance Computing & Simulation (HPCS), Genoa, Italy, 17-21 July, 2017.
[38] D. Chen, Z. Zhao, Z. Mao, M. Peng, “Channel Matrix Sparsity with Imperfect Channel State Information in Cloud Radio Access Networks,” IEEE Transactions on Vehicular Technology, Vol. 67, No. 2, pp. 1363-1374, February 2018.
[39] I. Koutsopoulos, “Optimal Functional Split Selection and Scheduling Policies in 5G Radio Access Networks,” IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France, 21-25 May, 2017.
[40] F. Y. S. Lin, C. H. Hsiao, Y. F. Wen, S. T. Kuo,“ Markov Decision Process to Achieve Near-Optimal Admission Control Mechanism for 5G Cloud Radio Networks,” Journal of Internet Technology, Vol. 20, No. 5, pp. 1561-1573, 2019.
[41] C. Yang, D. Simon,“ A new particle swarm optimization technique,” 18th International Conference on Systems Engineering (ICSEng'05), Las Vegas, NV, USA, 16-18 August, 2005.