Review of 5G C-RAN Resource Allocation
DOI:
https://doi.org/10.4108/eetmca.v7i4.3263Keywords:
ARPU, C-RAN, CAPEX, OPEX, Energy HarvestingAbstract
The Fifth Generation (5G) Network will bring different types of services, namely: EMBB, mMTC and URLLC and as more devices connect to the network, each user device request for data capacity will continue to grow. The increase in the number of devices and capacity request for each device will require an increase in network capacity, which will also need an increase in the number of Base Stations in the network. More Base Stations will increase the the Mobile Network Operator’s capital investment and operation costs. However, this increase in CAPEX and OPEX will not provide a corresponding increase in ARPU as users tend to be less willing to pay more as their capacity request increases. Mobile vendors and Mobile Network Operators will face the challenge of providing higher capacity for the same or less ARPU for their customers to maintain their customer base and maintain the business profitability. C-RAN was identified as a new and promising paradigm to help Mobile Network Operators reduce their CAPEX and OPEX while delivering higher capacity to their customers and maintaining business profitability. How C-RAN resources are allocated and managed within the 5G network will determine how efficient and profitable this optimisation process will be. In this article, we provide a high-level review of the crucial enabling 5G Technologies and an exhaustive review of C-RAN resource allocation algorithms for 5G networks with emphasis on resource allocation metrics/parameters. The main resource allocation metrics considered in this work include BBU computational/processing resource, capacity, Power/ energy consumption, wavelength, UE-RRH mapping and RRH-BBU mapping. Furthermore, Energy Harvesting in 5G C-RAN is discussed, including its architecture, categorised taxonomy and requirements. Lastly, future research directions and open research issues for efficient C-RAN resource allocation are highlighted.
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References
Aleksandra Checko, Henrik L. Christiansen, Ying Yan, Lara Scolari, Georgios Kardaras, Michael S. Berger, and Lars Dittmann. “Cloud RAN for Mobile Networks – A Technological overview”. IEEE Communication Surveys and Tutorials, Q1 2015, Vol. 17, No. 1, pp. 405-426
Cisco “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022” San Jose, CA, USA, Feb. 2019
Waleed Ejaz, Shree K. Sharma, Salman Saadat, Muhammad Naeem, Alagan Anpalagan, and N. A. Chughtai. “A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks”. JNCA 2020 Vol. 160.
Rehenuma Tasnim Rodoshi, Taewoon Kim and Wooyeol Choi. “Resource Management in Cloud Radio Access Network: Conventional and New Approach”. Sensors 2020, 20, 2708;
Sanaa Oulaourf, Abdelfatteh Haidine, Abdelhak Aqqal and Hassan Ouahmane. “Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory”. IJCNIS, Vol. 9, No. 1, April 2017, pp 117-156
Yongjun Xu; Guan Gui;Haris Gacanin and Fumiyuki Adachi. “A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends and Challenges”. IEEE Communication Surveys and Tutorials, 2021, Vol 23, Issue No. 2.
FENGYU TIAN, PENG ZHANG, AND ZHENG YAN. “A Survey on C-RAN Security”, IEEE Access, Vol. 5.
Praveen Kumar “Network security Roadmap” arXiv:2211.05278v1 [cs.CR]. 10 November 2022
K. O. Shobowale, Z. Mukhtar, B. Yahaya, Y. Ibrahim, M. O. Momoh. “Latest Advances on Security Architecture for 5GTechnology and Services” International Journal of Software Engineering and Computer Systems │ Vol. 9, Issue 1, (2023)
Muhammad Tahir Zaman, Maryam Rani1, Sidra Maqbool. “Security Challenges, Threat and Solutions for 5G Network for IoT. Pakistan Journal of Emerging Science and Technologies that (PJEST). 2(2) 16. December 2021.
-MOHAMMAD ASIF HABIBI, MEYSAM NASIMI, BIN HAN AND HANS D. SCHOTTEN. “A Comprehensive Survey of RAN Architectures Toward 5G Mobile Communication System”, IEEE Access, vol. 7
Vuyo S Pana, Oluaseyi P.Babalola, Vipil Balyan.”5G Radio access networks: A Survey”Elsevier Inc Arrayb 14 2022.
Jun Wu, Zhifeng Zhang, Yu Hong, and Yonggang Wen. “Cloud Radio Access Network (C-RAN): A Primer”. IEEE Network, January/February 2015, vol. 29, no. 1, pp. 35–41.
Venu Balaji Vinnakota, Kirtan Gopal Panda, Debarati Sen and Sandip chakraborty. “A Fronthaul Design in Cloud Radio Access Networks: A Survey”. ResearchGate,
Aleksandra Checko, Andrijana P. Avramova, Michael S. Berger, and Henrik L. Christiansen. “Evaluating C-RAN Fronthaul Functional Splits in Terms of Network Level Energy and Cost Savings”. JCN, April 2016, Vol. 18, No. 2.
Chathurika Ranaweera, Elaine Wong, Ampalavanapillai Nirmalathas, Chamil Jayasundara, and Christina Lim. “5G C-RAN Architecture: A Comparison of Multiple Optical Fronthaul Networks”. 2017 International Conference on Optical Network Design and Modeling (ONDM) 15-18 May 2017, Budapest, Hungary.IEEE Xplore 2017.
Ebude Carine Awasume, Stephen Musyoski, Vitalice Kalecha Oduol. “Cloud radio access network fronthaul solution using optimized dynamic bandwidth allocation algorithm”. IJECE, Vol. 11, No. 2, April 2021, 1395 - 1404
Chih-Lin I, Yannan Yuan, Jinri Huang, Shijia Ma, Chunfeng Cui, and Ran Duan. “Rethink Fronthaul for Soft RAN”. IEEE Communications Magazine, September 2015. 0163-6804/15/25.00. pp 82-88.
Manuel Eugenio Morocho Cayamcela, Wansu Lim. “Artificial Intelligence in 5G Technology: A Survey”. ResearchGate, 2018,
Youness Arjoune. and Saleh Faruque. “Artificial Intelligence for 5G Wireless Systems - Opportunities Challenges and Future Research Directions”. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) 06-08 January 2020. Las Vegas, NV, USA. IEEE Xplore:12 March 2020
Paulo Valente Klaine, Muhammad Ali Imran, Oluwakayode Onireti, and Richard Demo Souza. “A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks”. IEEE Communications Surveys & Tutorials, Vol.19, issu.4, pp. 2392- 2431
REHENUMA TASNIM RODOSHI, and WOOYEOL CHOI. “A Survey on Applications of Deep Learning in Cloud Radio Access Network”. IEEE Access, 2021,
Fatima Hussain, Syed Ali Hassan, Rasheed Hussain, and Ekram Hossain. “Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges.ResearchGate, IEEE Communications Surveys and Tutorials, 2020,
SHIWEN HE, SHAOWEN XIONG, YEYU OU, JIAN ZHANG, JIAHENG WANG, YONGMING HUANG, YAOXUE ZHANG. “An Overview on the Application of Graph Neural Networks in Wireless Networks”. IEEE Open Journal of the Communication Society. December 2021.
Weiwei Jiang. “Graph-based Deep Learning for Communication Networks: A Survey”. arViv.2106.02533v2 [cs.NI].22 December 2021.
Jose Suarez-Varela, Paul Almasan, Miquel Ferriol-Galm´es, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, and Pere Barlet-Ros. “Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities”. arXiv:2112.14792v2. [cs.NI] 27 Jul 2022
Mengyuan Lee, Guanding Yu, Huaiyu Dai, Geoffrey Ye Li. “Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions. arXiv:2212.04047v1 [cs.IT] 8 Dec 2022
Olabode Idowu-Bismark, Okokpujie Kennedy, Ryan Husbands, Michael Adedokun. “5G Wireless Communication Network Architecture and Its Key Enabling Technologies”. IREASE, 2019 Vol. 12, N. 2.
Sanae El Hassani, Abdelfatteh Haidine, and Hayat Jebbar. “Road to 5G: Key Enabling Technologies”. JC-Vol.14 No.11 2019
Achintya Kumar and Raj Jain. “A Survey of Self-Organizing Networks”. http://www.cse.wustl.edu/%7Ejain/cse574-16/ftp/son/index.html. 2016
Muhammad Imran, Latif U. Khan, Ibrar Yaqoob, Ejaz Ahmed, Muhammad Ahsan Qureshi and Arif Ahmed. “Energy Harvesting in 5G Networks: Taxonomy, Requirements, Challenges and Future Directions”. CS.NI 2019.
Sanae El Hassani, Hind El Hassani, Noureddine Boutammachte. “Overview on 5G Radio Frequency Energy Harvesting” ResearchGate/ASTESJ, 2019, Vol.4 No.4, pp.328-346 (2019).
Rubina Aktar “ENERGY EFFICIENT HYBRID POWERED C-RAN ARCHITECTURES WITH DYNAMIC USER
ASSOCIATION”. 2019.
Xumin Huang Rong Yu, Jiawen Kang, Yue Gao, Sabita Marhajan, Stein Gjessing. and Yan Zhang, “Software Defined Energy Harvesting Networking for 5G Green Communications”. IEEE Wireless Communication, Vol.24 No.4 2017, pp.38-45.
MST. RUBINA AKTAR, MD. SHAMIM, ANOWE, MD. ZAHURUL ISLAM SARKAR, ABU SADAT MD. SAYEM, MD. RASHEDUL ISLAM, ALLAMA IQBAL AKASH, MST. RUMANA AKTER RUME, GHOLAMHOSEIN MOLOUDIAN, ALI LALBAKHSH. “Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G”. IEEE Access. January 2023
Voore Subba Rao, A. Prashanth Rao. ”Dynamically Energy-Efficient Resource Allocation in 5G CRAN Using Intelligence Algorithm”. EMITTER International Journal of Engineering Technology. Vol. 10, No. 1, June 2022, pp. 217~230
Jing Gao, Shuyue Zhang, Xiao Meng “Resource Allocation Optimization Based on Energy Efficiency in Green Cloud Radio Access Network”. Hindawi Wireless Communications and Mobile Computing Volume 2022, Article ID 8932961, 18 pages.
Mojtaba Vaezi and Ying Zhang “Radio Access Network Evolution”. ResearchGate/Springer International Publishing AG 2017, pp.67-86,
Mugen Peng, Yaohua Sun, Xuelong Li, Zhendong Mao, and Chonggang Wang. “Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues”. IEEE Communications Surveys and Tutorial, Vol.18 Issue:3, 2016 pp. 2282- 2308.
Mesud Hadzialic, Branko Dosenovicy, Merim Dzaferagic and Jasmin Musovic. “Cloud-RAN Innovative radio access network architecture”. ResearchGate Conference Paper 25-27 September 2013, Zadar, Croatia. Proceedings ELMAR-2013. IEEE xplore, 11 November 2013.
“Different types of RAN Architectures”. httpe://www03g4g.co.uk.
M.Khan, R.S.Alhumaima, H.S. Al-Raweshidy. “Quality of Service Aware Dynamic BBU-RRH Mapping in Cloud Radio Access Network”. 2015 International Conference on Emerging Technologies (ICET), 19-20 December 2015, Peshawar, Pakistan. IEEE Xplore: 21 January 2016.
Rui Wang, Honglin Hu and Xiumei Yang. “Potentials and Challenges of C-RAN Supporting Multi-RATs towards 5G Mobile Networks”. IEEE Access 2014, Vol.2 pp. 1187- 1195
MINA BAGHANI, SAEEDEH PARSAEEFARD. AND THO LE-NGOC. “Multi-Objective Resource Allocation in Density-Aware Design of C-RAN in 5G”. IEEE Access, Vol.6, 2018, pp 45177- 45190.
CHIH-LIN, JINRI HUANG, RAN DUAN, CHUNFENG CUI, JESSE JIAND.AND LEI LI. Recent Progress on C-RAN Centralization and Cloudification”. IEEE Access 2014 Vol.2, pp 1030- 1039.
Wei-Che Chien, Chin-Feng Lai, and Han-Chieh Chao. “Dynamic Resource Prediction and Allocation in C-RAN with Edge Artificial Intelligence”. IEEE Transactions on Industrial Informatics Vol.15,Issue: 7, 2019, pp. 4306- 4314,
Rehenuma Tasnim Rodoshi, Taewoon Kim. and Wooyeol Choi. “Deep Reinforcement Learning Based Dynamic Resource Allocation in Cloud Radio Access Networks”. ResearchGate/ 2020 International Conference on Information and Communication Technology Convergence (ICTC), 21-23 October 2020, Jeju, Korea (South). IEEE Xplore, 21 December 2020,
Jin Xua, Zbigniew Dziong., Yan Luxin., Zhe Huang, Ping Xu. and Adnane Cabani. “Intelligent multi-agent-based C-RAN architecture for 5G radio resource management”. COMPNW 107418, 2020, Vol 180,
Mehdi Setayesh, Shahab Bahrami, and Vincent W.S. Wong “Joint PRB and Power Allocation for Slicing eMBB and URLLC Services in 5G C-RAN”. GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 07-11 December 2020, Taipei, Taiwan, IEEE Xplore: 25 January 2021.
Mohammed Yazid Lyazidi, Nadjib Aitsaadi∗ and Rami Langar. “Dynamic Resource Allocation for C-RAN in LTE with Real-Time BBU/RRH mapping”. 2016 IEEE International Conference on Communications (ICC), 22-27 May 2016, Kuala Lumpur, Malaysia, IEEE Xplore:14 July 2016.
Shruti SHARMA, Wonsik YOON. “Multiobjective Reinforcement Learning Based Energy Consumption in C-RAN Enabled Massive MIMO”. RADIOENGINEERING, VOL. 31, NO. 1, APRIL 2022.
Muhammad Sulaiman, Arash Moayyedi, Mohammad A. Salahuddin, Raouf Boutaba, Aladdin Saleh, “Multi-Agent Deep Reinforcement Learning for Slicing and Admission Control in 5G C-RAN”.2022 IEEE
M. Khan, R. S. Alhumaima and H.S. Al-Raweshidy. “Reducing Energy Consumption by Dynamic Resource Allocation In C-RAN”. 2015 European Conference on Networks and Communications (EuCNC), 29 June 2015 - 02 July 2015, Paris, France, IEEE Xplore: 13 2015,
Yi Zhang, Federico Barusso, Diarmuid Collins, Marco Ruffini and Luiz A. DaSilva. “Dynamic Allocation of Processing Resources in C-RAN for a Virtualized 5G Mobile Network”. 2018 26th European Signal Processing Conference (EUSIPCO), 03-07 September 2018, Rome-Italy, IEEE Xplore:02 December 2018,
DEUSSOM DJOMADJI Eric Michel, NDJE BIHOLONG Evelyne Noelle and TONYE Emmanuel 2022. “Artificial Bee Colonies Solution for Load Sharing in a Cloud RAN”. European Journal of Applied Sciences. Vol.10, No.2 (Mar. 2022), 33–50.
Nuo Yu, Zhaohui Song, Hongwei Du, Hejiao Huang, Xiaohua Jia. “Multi-Resource Allocation in Cloud Radio Access”. 2017 IEEE International Conference on Communications (ICC), 21-25 May 2017, Paris, France, IEEE Xplore: 31 July 2017,
ROLANDO GUERRA-GÓMEZ, SILVIA RUIZ-BOQUÉ, MARIO GARCÍA-LOZANO AND JOAN OLMOS BONAFE “Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN”. IEEE Access Vol. 8 2015 10.1109/ACCESS.2020.2994258
Zainab H. Fakhri, M. Khan, Firas Sabir, 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, Iss.4, 2018, pp.261–275,
Lei Feng, Wenjing Li, Peng Yu, and Xuesong Qiu. “An Enhanced OFDM Resource Allocation Algorithm in C-RAN Based 5G Public Safety Network”. Hindawi, 2016,
Muhammad Rehan Raza, Matteo Fiorani, Ahmad Rostami, Peter Öhlen, Lena Wosinska and Paolo Monti. “Demonstration of Dynamic Resource Sharing Benefits in an Optical C-RAN”. JOCN, Vol.8, Issue 8,
Ado Adamou Abba Ari, Abdelhak Gueroui, Chafiq Titouna, Ousmane Thiare, Zibouda Aliouat. “Resource Allocation Scheme, for 5G C-RAN: A Swarm Intelligence Based approach”. COMPNW, Vol.165, 2019,
Sahar Imtiaz., Hadi Ghauch, M. Mahboob Ur Rahman, George Koudouridis and James Gross. “Learning-Based Resource Allocation Scheme for TDD-Based CRAN System”. MSWiM '16: 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile System, November 13 – 17 2016, Malta, CS.NI, 2016, pp.176–185.
Ramkumar Jayaraman, Baskar Manickam, Suresh Annamalai, Manoj Kumar, Ashutosh Mishra, Rakesh Shrestha. “ Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network” MDP Electronics 2023, 12, 451
Ali Kashif Bashir., Rajakumar Arul, Shakila Basheer, Gunasekaran Raja, Ramkumar Jayaraman, Nawab Muhammad Faseeh Qureshi. “An Optimal Multi-tier Resource Allocation of Cloud RAN in 5G using Machine Learning”. ETT, 2019,
Mohamad Kenan Al-Hares, Philippos Assimakopoulos, Daniel Muench and Nathan J. Gomes. “Modelling Time Aware Shaping in an Ethernet Fronthaul”. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 04-08 December 2017, Singapore. IEEE Xplore:15 January 2018,
Ahmed Abdelhadi and T. Charles Clancy. “Optimal Context-Aware Resource Allocation in Cellular Networks”. 2016 International Conference on Computing, Networking and Communications (ICNC), 15-18 February 2016, Kauai, HI, USA. IEEE Xplore: 24 March 2016,
Gen Liang, Guoxi Sun, Jingcheng Fang, Xiaoxue Guo and Hewei Yu. “An Access Selection Algorithm for Heterogeneous Wireless Networks Based on Optimal Resource Allocation”. Hindawi/WCMC, 2020,
SHER ALI, AMIR HAIDER, MUHIBUR RAHMAN, ., MUHAMMAD SOHAIL AND YOUSAF BIN ZIKRIA. “Deep Learning (DL) Based Joint Resource Allocation and RRH Association in 5G-Multi-Tier Networks”. IEEE Access, Vol.9, 2021, pp.118357-118366,
Muhammad Ahsan, Ashfaq Ahmed, Arafat Al-Dweik, Arsalan Ahmad. “Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network”. IEEE Systems Journal · March 2022
Mountaser Ghizlane. “Cloud-Radio Access Network Functional Split over Ethernet-based Fronthaul: analysis and performance improvement for fronthaul”. https://kclpure.kcl.ac.uk/portal.
Markus Schacherbauer and Anubhab Banerjee. “Using Self-Organizing Networks in 5G”. Network Architectures and Services,2020, pp.97-100.
Anders Lund., Bomin Li and Thomas Norgaard. ”Comcores Radio over Ethernet Gateway for Future Fronthaul Networks”.
Alexander Engels, Michael Reyer, Xiang Xu, Rudolf Mathar, Jietao Zhang, and Hongcheng Zhuang. “Autonomous Self-Optimization of Coverage and Capacity in LTE Cellular Networks”. IEEE Transactions on Vehicular Technology, Vol.62, Issue 5, 2013. Pp.1989-2004.
Fatma Marzouk, João Paulo Barraca, Ayman Radwan. “Interference and QoS-Aware Resource Allocation Considering DAS Behavior for C-RAN Power Minimization”. IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, VOL. 45, NO. 4, FALL 2022
Nikolay D andanov, Hussein Al-Shatri., Anja Klein. and Vladimir Poulkov. “Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks”. WPC, vol.92, pp.251–278 2017.
Plamen Semov. Hussein Al-Shatri, Krasimir Tonchev Vladimir Poulkov and Anja Klein. “Implementation of Machine Learning for Autonomic Capabilities in Self-Organizing Heterogeneous Networks”. WPC, Vol.92, pp.49–168 2017,
Yajuang Tang et al. “Energy-efficient and high-Spectrum-efficiency wireless transmission”. EAI endorsed Transaction on Mobile Communications and Applications 08 2022 Vol 7, issue 2
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