{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:40:33Z","timestamp":1760229633003,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A2C2014333"],"award-info":[{"award-number":["2021R1A2C2014333"]}]},{"name":"Korea government (MSIT)","award":["2021R1A2C2014333"],"award-info":[{"award-number":["2021R1A2C2014333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent\u2019s goal is to maximize every action\u2019s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.<\/jats:p>","DOI":"10.3390\/network2030023","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"370-388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8577-5662","authenticated-orcid":false,"given":"Ralph Voltaire J.","family":"Dayot","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Kunsan National University, Gunsan 54150, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-1116","authenticated-orcid":false,"given":"In-Ho","family":"Ra","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Kunsan National University, Gunsan 54150, Korea"}]},{"given":"Hyung-Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of IT Applied System Engineering, Jeonbuk National University, Jeonju 54896, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42828","DOI":"10.1109\/ACCESS.2020.2977406","article-title":"5G is Real: Evaluating the Compliance of the 3GPP 5G New Radio System with the ITU IMT-2020 Requirements","volume":"8","author":"Henry","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108063","DOI":"10.1016\/j.comnet.2021.108063","article-title":"User association and resource allocation in 5G (AURA-5G): A joint optimization framework","volume":"192","author":"Jain","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TVT.2019.2963462","article-title":"When Network Slicing Meets Prospect Theory: A Service Provider Revenue Maximization Framework","volume":"69","author":"Fantacci","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"53997","DOI":"10.1109\/ACCESS.2020.2980392","article-title":"Software Defined Network-Based Management for Enhanced 5G Network Services","volume":"8","author":"Tadros","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/JCN.2020.000026","article-title":"5G network slices embedding with sharable virtual network functions","volume":"22","author":"Mei","year":"2020","journal-title":"J. Commun. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.comcom.2019.10.024","article-title":"On 5G network slice modelling: Service, resource, or deployment-driven?","volume":"149","author":"Papageorgiou","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13638-021-01983-7","article-title":"Network slicing: A next generation 5G perspective","volume":"2021","author":"Subedi","year":"2021","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kourtis, M.-A., Sarlas, T., Xilouris, G., Batistatos, M.C., Zarakovitis, C.C., Chochliouros, I.P., and Koumaras, H. (2021). Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-off. Energies, 14.","DOI":"10.3390\/en14216876"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chagdali, A., Elayoubi, S., and Masucci, A. (2021). Slice Function Placement Impact on the Performance of URLLC with Multi-Connectivity. Computers, 10.","DOI":"10.3390\/computers10050067"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.procs.2021.02.006","article-title":"Survey on Network Slice Isolation in 5G Networks: Fundamental Challenges","volume":"182","author":"Alotaibi","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sohaib, R., Onireti, O., Sambo, Y., and Imran, M. (2021). Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case. Electronics, 10.","DOI":"10.3390\/electronics10091090"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14977","DOI":"10.1109\/ACCESS.2020.2967626","article-title":"A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network","volume":"8","author":"Ojijo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MVT.2018.2809473","article-title":"End-to-End Quality of Service in 5G Networks: Examining the Effectiveness of a Network Slicing Framework","volume":"13","author":"Ye","year":"2018","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thantharate, A., Paropkari, R., Walunj, V., and Beard, C. (2019, January 10\u201312). DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019, New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993066"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Thantharate, A., Paropkari, R., Walunj, V., Beard, C., and Kankariya, P. (2020, January 6\u20138). Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond. Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020, Las Vegas, NV, USA.","DOI":"10.1109\/CCWC47524.2020.9031158"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Abbas, K., Afaq, M., Khan, T.A., Mehmood, A., and Song, W.-C. (2020, January 22\u201325). IBNSlicing: Intent-Based Network Slicing Framework for 5G Networks using Deep Learning. Proceedings of the APNOMS 2020\u20132020 21st Asia-Pacific Network Operations and Management Symposium: Towards Service and Networking Intelligence for Humanity, Daegu, Korea.","DOI":"10.23919\/APNOMS50412.2020.9237008"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101415","DOI":"10.1016\/j.phycom.2021.101415","article-title":"Resource allocation trends for ultra-dense networks in 5G and beyond networks: A classification and comprehensive survey","volume":"48","author":"Sharma","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102638","DOI":"10.1016\/j.jnca.2020.102638","article-title":"A comprehensive survey on resource allocation for CRAN in 5G and beyond networks","volume":"160","author":"Ejaz","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pereira, R., Lieira, D., Silva, M., Pimenta, A., Da Costa, J., Ros\u00e1rio, D., Villas, L., and Meneguette, R. (2020). RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing. Sensors, 20.","DOI":"10.3390\/s20195449"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"19691","DOI":"10.1109\/ACCESS.2018.2822398","article-title":"A service-oriented deployment policy of end-to-end network slicing based on complex network theory","volume":"6","author":"Guan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TWC.2021.3094116","article-title":"ONETS: Online Network Slice Broker From Theory to Practice","volume":"21","author":"Sciancalepore","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103518","DOI":"10.1016\/j.csi.2021.103518","article-title":"Optimal 5G network slicing using machine learning and deep learning concepts","volume":"76","author":"Abidi","year":"2021","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"150517","DOI":"10.1109\/ACCESS.2019.2947454","article-title":"Efficient and Secure 5G Core Network Slice Provisioning Based on VIKOR Approach","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TNET.2020.2979667","article-title":"Multi-Resource Allocation for Network Slicing","volume":"28","author":"Fossati","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1109\/TMC.2020.3000657","article-title":"Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation","volume":"20","author":"Sun","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"122229","DOI":"10.1109\/ACCESS.2020.3006502","article-title":"An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"Jungnickel, D. (2005). Graphs, Networks, and Algorithms, Springer. [2nd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Guo, C., Xu, J., Gupta, L., and Jain, R. (2019). Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes. Appl. Sci., 9.","DOI":"10.3390\/app9204361"},{"key":"ref_29","unstructured":"Van Steen, M. (2010). Graph Theory and Complex Networks: An Introduction, University of Twente."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Slivkins, A. (2019). Introduction to Multi-Armed Bandits. arXiv, Available online: http:\/\/arxiv.org\/abs\/1904.07272.","DOI":"10.1561\/9781680836219"},{"key":"ref_31","unstructured":"Sutton, R.S., and Barto, A.G. (2017). Reinforcement Learning: An Introduction, The MIT Press. [2nd ed.]."},{"key":"ref_32","first-page":"1","article-title":"Reinforcement Learning Based Empirical Comparison of UCB, Epsilon-Greedy, and Thompson Sampling","volume":"12","author":"Singh","year":"2021","journal-title":"Int. J. Aquat. Sci."}],"container-title":["Network"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-8732\/2\/3\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:04Z","timestamp":1760139484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-8732\/2\/3\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["network2030023"],"URL":"https:\/\/doi.org\/10.3390\/network2030023","relation":{},"ISSN":["2673-8732"],"issn-type":[{"type":"electronic","value":"2673-8732"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}