f Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm
FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-120316

Research Article |

Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm

Author(s): Geoffrey Okindo*, Prof. George Kamucha and Dr. Nicholas Oyie

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 3

Publisher : FOREX Publication

Published : 30 July 2024

e-ISSN : 2347-470X

Page(s) : 849-862

DOI: https://doi.org/10.37391/IJEER.120316

Abstract

This study presents a comprehensive framework for optimizing 5G network slices using metaheuristic algorithms, focusing on Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communications (mMTC) scenarios. The initial setup involves a MATLAB-based 5G New Radio (NR) Physical Downlink Shared Channel (PDSCH) simulation and OpenAir-Interface (OAI) 5G network testbed, utilizing Ubuntu 22.04 Long Term Support (LTS), MicroStack, Open-Source MANO (OSM), and k3OS to create a versatile testing environment. Key network parameters are identified for optimization, including power control settings, signal-to-noise ratio targets, and resource block allocation, to address the unique requirements of different 5G use cases. Metaheuristic algorithms, specifically the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), are employed to optimize these parameters. The algorithms are assessed based on their ability to enhance throughput, reduce latency, and minimize jitter within the network slices and the MATLAB simulation model. Each algorithm’s performance is evaluated through iterative testing, with improvements measured against established pre-optimization benchmarks. The results demonstrate significant enhancements in network performance post-optimization. For eMBB, the GA shows the most substantial increase in throughput, while PSO is most effective in reducing latency for URLLC applications. In mMTC scenarios, GA achieves the most notable reduction in jitter, illustrating the potential of metaheuristic algorithms in fine-tuning 5G networks to meet diverse service requirements. The study concludes that the strategic application of these algorithms can significantly improve the efficiency and reliability of 5G network slices, offering a scalable approach to managing the complex dynamics of next-generation wireless networks.

Keywords: WOA, 5G, PSO, Genetic Algorithm, MATLAB, eMBB, URLLC, mMTC.




Geoffrey Okindo*, Student, Pan African University Institute for Basic Sciences, Technology, and Innovation, Nairobi; Email: eoffreymogendi7@gmail.com

Prof. George Kamucha, Prof, University of Nairobi, Nairobi; Email: gkamucha@uonbi.ac.ke

Dr. Nicholas Oyie, Dr, Murang’a University of Technology, Nairobi; Email: noyie@mut.ac.ke

    [1] P. Popovski, K. F. Trillingsgaard, O. Simeone, and G. Durisi, “5G wireless network slicing for eMBB, URLLC, and mMTC: A communication-theoretic view,” Ieee Access, vol. 6, pp. 55765–55779, 2018.
    [2] L. A. Freitas et al., “Slicing and Allocation of Transformable Resources for the Deployment of Multiple Virtualized Infrastructure Managers (VIMs),” in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), 2018, pp. 424–432. doi: 10.1109/NETSOFT.2018.8459990.
    [3] H. Zhang, N. Liu, X. Chu, K. Long, A.-H. Aghvami, and V. C. M. Leung, “Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges,” IEEE Communications Magazine, vol. 55, no. 8, pp. 138–145, 2017.
    [4] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51–67, 2016.
    [5] H. Qu, B. Zhang, Z. Duan, and Y. Zhang, “Network Slice Resource Mapping Method Based on Discrete Binary Particle Swarm Optimization Algorithm,” in 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), 2020, pp. 412–416.
    [6] X. Qi, S. Khattak, A. Zaib, and I. Khan, “Energy efficient resource allocation for 5G heterogeneous networks using genetic algorithm,” IEEE Access, vol. 9, pp. 160510–160520, 2021.
    [7] D. Boughaci, “Solving optimization problems in the fifth generation of cellular networks by using meta-heuristics approaches,” Procedia Comput Sci, vol. 182, pp. 56–62, 2021.
    [8] R. Gomes, D. Vieira, and M. F. de Castro, “Application of Meta-Heuristics in 5G Network Slicing: A Systematic Review of the Literature,” Sensors, vol. 22, no. 18, p. 6724, 2022.
    [9] H. Ganame, L. Yingzhuang, H. Ghazzai, and D. Kamissoko, “5G base station deployment perspectives in millimeter wave frequencies using meta-heuristic algorithms,” Electronics (Basel), vol. 8, no. 11, p. 1318, 2019.
    [10] R. Arshad, M. Farooq-i-Azam, R. Muzzammel, A. Ghani, and C. H. See, “Energy efficiency and throughput optimization in 5g heterogeneous networks,” Electronics (Basel), vol. 12, no. 9, p. 2031, 2023.
    [11] M. Turhan, G. Scopelliti, C. Baumann, E. Truyen, J. T. Muehlberg, and M. Petik, “The Trust Model for Multi-tenant 5G Telecom Systems Running Virtualized Multi-Component Services,” 2021.
    [12] R. Botez, A.-G. Pasca, and V. Dobrota, “Kubernetes-Based Network Functions Orchestration for 5G Core Networks with Open-Source MANO,” in 2022 International Symposium on Electronics and Telecommunications (ISETC), 2022, pp. 1–4.
    [13] C. Novaes, C. Nahum, I. Trindade, D. Cederholm, G. Patra, and A. Klautau, “Virtualized c-ran orchestration with docker, Kubernetes, and openairinterface,” arXiv preprint arXiv:2001.08992, 2020.
    [14] S. Barrachina-Muñoz, M. Payaró, and J. Mangues-Bafalluy, “Cloud-native 5G experimental platform with over-the-air transmissions and end-to-end monitoring,” in 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), 2022, pp. 692–697.
    [15] J. Sancho, “Design and Testing of a Transmitter-Channel-Receiver Model Using Matlab 5G Toolset,” 2019.
    [16] M. Polese, M. Giordani, and M. Zorzi, “3GPP NR: the standard for 5G cellular networks,” 5G Italy White eBook: from Research to Market, 2018.
    [17] A. Spelman and others, “System modeling for 5G integrated digital transmitters,” 2021.
    [18] F. Schaich, T. Wild, and R. Ahmed, “Subcarrier spacing-how to make use of this degree of freedom,” in 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016, pp. 1–6.
    [19] X. Lin, Z. Lin, S. E. Löwenmark, J. Rune, R. Karlsson, and others, “Doppler shift estimation in 5G new radio non-terrestrial networks,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1–6.
    [20] T. Alam, S. Qamar, A. Dixit, and M. Benaida, “Genetic algorithm: Reviews, implementations, and applications,” arXiv preprint arXiv:2007.12673, 2020.

Geoffrey Okindo, George Kamucha and Dr. Nicholas Oyie (2024), Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm. IJEER 12(3), 849-862. DOI: 10.37391/IJEER.120316.