Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms
Tóm tắt
In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively.
Từ khóa
Tài liệu tham khảo
Moltchanov, D., Sopin, E., Begishev, V., Samuylov, A., Koucheryavy, Y., Samouylov, K.: A tutorial on mathematical modeling of 5g/6g millimeter wave and terahertz cellular systems. IEEE Commun. Surv. Tutor. 24(2), 1072–1116 (2022)
Sabuj, S.R., Rubaiat, M., Iqbal, M., Mobashera, M., Malik, A., Ahmed, I., Matin, M.A.: Machine-type communications in noma-based terahertz wireless networks. Int. J. Intell. Netw. 3, 31–47 (2022)
Gaurav, A.K., Sahu, N., Dash, A.P., Chalapathi, G., Chamola, V.: A survey on computation resource allocation in IOT enabled vehicular edge computing. Complex & Intell. Syst. 8(5), 3683–3705 (2022)
Baig, I., Farooq, U., Hasan, N.U., Zghaibeh, M., Jeoti, V.: A multi-carrier waveform design for 5g and beyond communication systems. Mathematics 8(9), 1466 (2020)
Song, F., Li, J., Ma, C., Zhang, Y., Shi, L., Jayakody, D.N.K.: Dynamic virtual resource allocation for 5g and beyond network slicing. IEEE Open J. Veh. Technol. 1, 215–226 (2020)
Shitharth, S., Manoharan, H., Alsowail, R.A., Shankar, A., Pandiaraj, S., Maple, C.: Qos enhancement in wireless ad hoc networks using resource commutable clustering and scheduling. Wirel. Netw. (2023). https://doi.org/10.1007/s11276-023-03499-y
Manoharan, H., Selvarajan, S., Aluvalu, R., Abdelhaq, M., Alsaqour, R., Uddin, M.: Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation. PeerJ Comput. Sci. 9, 1709 (2023)
Munir, R., Wei, Y., Ma, C., Yang, B., et al.: Dynamically resource allocation in beyond 5g (b5g) network ran slicing using deep deterministic policy gradient. Wirel. Commun. Mobile Comput. (2022). https://doi.org/10.1155/2022/9958786
Samir, R., El-Hennawy, H., Elbadawy, H.: Cluster-based multi-user multi-server caching mechanism in beyond 5g/6g mec. Sensors 23(2), 996 (2023)
Bartsiokas, I.A., Gkonis, P.K., Kaklamani, D.I., Venieris, I.S.: Ml-based radio resource management in 5g and beyond networks: a survey. IEEE Access 10, 83507–83528 (2022)
Ma, T., Zhang, Y., Han, Z., Li, C.: Heterogeneous ran slicing resource allocation using mathematical program with equilibrium constraints. IET Commun. 16(15), 1772–1786 (2022)
Selvarajan, S., Manoharan, H., Goel, S., Akili, C.P., Murugesan, S., Joshi, V.: Scmc: Smart city measurement and control process for data security with data mining algorithms. Meas.: Sens. 31, 100980 (2024)
Sarah, A., Nencioni, G., Khan, M.M.I.: Resource allocation in multi-access edge computing for 5g-and-beyond networks. Comput. Netw. 227, 109720 (2023)
Yu, Z., Gu, F., Liu, H., Lai, Y.: 5g multi-slices bi-level resource allocation by reinforcement learning. Mathematics 11(3), 760 (2023)
Iannacci, J., Tagliapietra, G., Bucciarelli, A.: Exploitation of response surface method for the optimization of rf-mems reconfigurable devices in view of future beyond-5g, 6g and super-iot applications. Sci. Rep. 12(1), 3543 (2022)
Dilli, R.: Design and feasibility verification of 6g wireless communication systems with state of the art technologies. Int. J. Wirel. Inf. Netw. 29(1), 93–117 (2022)
Yin, Y., Zheng, W.: An efficient recommendation algorithm based on heterogeneous information network. Complexity 2021, 1–18 (2021)
Peng, Q., Wang, S.: Masa: multi-application scheduling algorithm for heterogeneous resource platform. Electronics 12(19), 4056 (2023)
Gachhadar, A., Maharjan, R.K., Shrestha, S., Adhikari, N.B., Qamar, F., Kazmi, S.H.A., Nguyen, Q.N.: Power optimization in multi-tier heterogeneous networks using genetic algorithm. Electronics 12(8), 1795 (2023)
Inga, E., Inga, J., Hincapié, R.: Maximizing resource efficiency in wireless networks through virtualization and opportunistic channel allocation. Sensors 23(8), 3949 (2023)
Shitharth, S., Manoharan, H., Shankar, A., Alsowail, R.A., Pandiaraj, S., Edalatpanah, S.A., Viriyasitavat, W.: Federated learning optimization: a computational blockchain process with offloading analysis to enhance security. Egypt. Inf. J. 24(4), 100406 (2023)
Shitharth, S., Manoharan, H., Alsowail, R.A., Shankar, A., Pandiaraj, S., Maple, C., Jeon, G.: Development of edge computing and classification using the internet of things with incremental learning for object detection. Intern. Things 23, 100852 (2023)
Al-Ani, A.K., Laghari, Ul Arfeen, S., Manoharan, H., Selvarajan, S., Uddin, M.: Improved transportation model with internet of things using artificial intelligence algorithm. Comput. Mater. Continua (2023). https://doi.org/10.32604/cmc.2023.038534
Inga, E., Hincapie, R., Cespedes, S.: Capacitated multicommodity flow problem for heterogeneous smart electricity metering communications using column generation. Energies 13(1), 97 (2019)