Single Linkage Weighted Steepest Gradient Adaboost Cluster-Based D2D in 5G Networks

• Autorzy: Varadala Sridhar , Roslin S. Emalda

Identyfikatory

DOI

10.26636/jtit.2023.167222

• Języki publikacji: EN

Abstrakty

EN

Efficiency of data transmissions with minimum latency levels and better resource utilization is a challenging issue in 5 G device-to-device (D 2D) environments. A novel technique referred to as single linkage steepest gradient gentle AdaBoost cluster-based device (SLSGAC) is introduced to improve device-to-device communications with minimum latency. The proposed technique uses the ensemble clustering approach to group mobile devices by constructing a set of weak clusters, based on the Minkowski single linkage clustering technique. In the weak clustering process, residual energy, bandwidth and SINR are estimated, and mobile devices are grouped based on the Minkowski distance measure. Results of the weak clustering process are combined to provide the final ensemble’s clustering output by applying the steepest gradient function to minimize the error rate. For each cluster, a head is selected from among the group members to improve the data transmission rate and minimize latency. Simulations are conducted comparing the proposed technique with the existing methods based on such metrics as energy efficiency, data delivery ratio, packet loss rate, throughput and latency.

Słowa kluczowe

EN

5G networks; D2D communication; Minkowski single linkage clustering

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2023
• Tom: nr 1
• Strony: 60--65
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Varadala Sridhar

• Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India

• https://orcid.org/0000-0003-3715-8197

autor

Roslin S. Emalda

• Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India

• https://orcid.org/0000-0001-8699-9510

Bibliografia

• [1] R. Zhang, S. Jia, Y. Ma, and Ch. Xu, “Social-aware D 2D video delivery method based on mobility similarity measurement in 5G ultra-dense network”, IEEE Access, vol. 8, pp. 52413– 52427, 2020 (https://doi.org/10.1109/ACCESS.2020.2980865).
• [2] A.A. Algedir and H.H. Refai, “Energy efficiency optimization and dynamic mode selection algorithms for D2D communication under HetNet in downlink reuse”, IEEE Access, vol. 8, pp. 95251– 95265, 2020 (https://doi.org/10.1109/ACCESS.2020.2995833).
• [3] G. Mesbahi and A.G. Rahbar, “Cluster-based architecture capable for device-to-device millimeter-wave communications in 5G cellular networks”, Arabian Journal for Science and Engineering, vol. 44, pp. 9719–9733 , 2019 (https://doi.org/10.1007/s13369-019-03830-w).
• [4] Q. Liu, Ch.F. Kwong, S. Zhang, L. Li, and J. Wang, “A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks”, Wireless Network, vol. 28, pp. 965– 978, 2022 (https://doi.org/10.1007/s11276-019-02130-3).
• [5] S. Aslam, F. Alam, S.F. Hasan, and M. Rashid, “A novel weighted clustering algorithm supported through a distributed architecture for D2D enabled content-centric networks”, Sensors, vol. 20, no. 19, Article number 5509, 2020 (https://doi.org/10.3390/s20195509).
• [6] M. Gharbieh, A. Bader, H. ElSawy, H.-Ch. Yang, M.-S. Alouini, and A. Adinoyi, “Self-organized scheduling request for uplink 5G networks: A D2D clustering approach”, IEEE Transactions on Communications, vol. 67, no. 2, pp. 1197– 1209, 2019 (https://doi.org/10.1109/TCOMM.2018.2876008).
• [7] X. Xiao, M. Ahmed, X. Chen, Y. Zhao, Y. Li, and Z. Han, “Accelerating content delivery via efficient resource allocation for network coding aided D 2D communications”, IEEE Access, vol. 7, pp. 115783 –115796 , 2019 (https://doi.org/10.1109/ACCESS.2019.2930728).
• [8] W. Chen, B. Zhang, X. Yang, W. Fang, W. Zhang, and X. Jiang, “C-EEUC: A cluster routing protocol for coal mine wireless sensor network based on fog computing and 5G”, Mobile Networks and Applications, vol. 27, no. 5, pp. 1853– 1866, 2022 (https://doi.org/10.1007/s11036-019-01401-9).
• [9] M.H. Adnan and Z.A. Zuckarnain, “Device-to-device communication in 5G environment: Issues, solutions, and challenges”, Symmetry, vol. 12, no. 11, 1762, 2020 (https://doi.org/10.3390/sym12111762).
• [10] S. Song, Ch. Lee, H. Cho, G. Lim, and J.-M. Chung, “Clustered virtualized network functions resource allocation based on context-aware grouping in 5G edge networks”, IEEE Transactions on Mobile Computing, vol. 19, no. 5, pp. 1072–1083, 2020 (https://doi.org/10.1109/TMC.2019.2907593).
• [11] J. Huang, Ch.-X. Wang, L. Bai, J. Sun, Y. Yang, J. Li, O. Tirkkonen, and M.-T. Zhou, “A big data enabled channel model for 5G wireless communication systems”, IEEE Transactions on Big Data, vol. 6, no. 2, pp. 211– 222, 2020 (https://doi.org/10.1109/TBDATA.2018.2884489).
• [12] B.B. Haile, E. Mutafungwa, and J. Hämäläinen, “A data-driven multi-objective optimization framework for hyperdense 5G network planning”, IEEE Access, vol. 8, pp. 169423– 169443, 2020 (https://doi.org/10.1109/ACCESS.2020.3023452).
• [13] S. Solaiman, A. Nassef, and E. Fadel, “User clustering and optimized power allocation for D 2 D communications at mm wave underlaying MIMO-NOMA cellular networks”, IEEE Access, vol. 9, pp. 57726– 57742, 2021 (https://doi.org/10.1109/ACCESS.2021.3071992).
• [14] H.H. Hussein, H.A. Elsayed, and S.M. Abd El-kade, “Intensive benchmarking of D2 D communication over 5G cellular networks: Prototype, integrated features, challenges, and main applications”, Wireless Networks, vol. 26 , pp. 3183 –3202, 2020 (https://doi.org/10.1007/s11276-019-02131-2).
• [15] A.G. Sreedevi and T.R. Rao, “Reinforcement learning algorithm for 5 G indoor device-to-device communications”, Transaction on Emerging Telecommunication Technologies, vol. 30, no. 9, pp. 1–10, 2019 (https://doi.org/10.1002/ett.3670).
• [16] I. Ioannou, V. Vassiliou, Ch. Christophorou, and A. Pitsillides, “Distributed artificial intelligence solution for D2D communication in 5G networks”, IEEE Systems Journal, vol. 14, no. 3, pp. 4232–4241, 2020 (https://doi.org/10.1109/JSYST.2020.2979044).
• [17] S. Sevgican, M. Turan, K. Gökarslan, H.B. Yilmaz, and T. Tugcu, “Intelligent network data analytics function in 5G cellular networks using machine learning”, Journal of Communications and Networks, vol. 22 , no. 3, pp. 269– 280, 2020 (https://doi.org/10.1109/JCN.2020.000019).
• [18] A. Sultana, I. Woungang, A. Anpalagan, L. Zhao, and L. Ferdouse, “Efficient resource allocation in SCMA-enabled device-to-device communication for 5G networks”, IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5343–5354 , 2020 (https://doi.org/10.1109/TVT.2020.2983569).
• [19] P.K. Barik, A. Shukla, R. Datta, and Ch. Singhal, “A resource sharing scheme for intercell D 2D communication in cellular networks: A repeated game theoretic approach”, IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7806 –7820, 2020 (https://doi.org/10.1109/TVT.2020.2991476).

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).