Grey Wolf Optimization for Resource Allocation in RAN Slicing for Heterogeneous Requirements

Authors

  • Dhanashree Kulkarni Department of Electronics and Telecommunication Engineering, (Dr. D.Y.Patil Institute of Technology, Pimpri, Pune)
  • Mithra Venkatesan Department of Electronics and Telecommunication Engineering, (Dr. D.Y.Patil Institute of Technology, Pimpri, Pune)
  • Anju V. Kulkarni Department of Electronics and Telecommunication Engineering, (Dayananda Sagar College of Engineering, Banglore,India)
  • Radhika Menon Department of Mathematics, (Dr. D.Y.Patil Institute of Technology, Pimpri, Pune)

Keywords:

5G RAN Slicing, Resource Allocation, Heterogeneous Requirements, Linear Programming Model, Grey Wolf Optimization

Abstract

Dealing with the rapid growth of the devices in 5G scenario is where the 5G NR helps to cope up with the heterogeneous requirements. Network slicing technique is the solution to work with the different use cases with various Quality of Service (QoS) requirements like Ultra Reliable Low Latency Communication (uRLLC), Enhanced Mobile Broadband (eMBB) and Massive Machine Type Communication (mMTC) devices. In network slicing latency plays a vital role in fulfilling the expectations at required rate. Thus, we have considered the latency parameter in the designing of the slice. In this paper, we have developed a Linear Programming Model (LP) to allocate the required resources to the various applications under uRLLC, eMBB and mMTC. In all these use cases latency plays a very vital role, hence in this regard we have created latency sensitive slices for all three use cases. Further we have extended the work with Grey Wolf Optimization (GWO) for allocation of resources to the slices. In this paper, we have investigated the model by (i) varying number of applications (ii) number of resources keeping the resource blocks constant. Numerical results shows the average percentage (%) satisfaction of resource allocation for 5,7,9 and 11 applications. With LP model we could gain average 79.05% satisfaction of allocation of resources. In the extended work with GWO the average satisfaction percentage increased to 96.42%. The comparative results of both the models show that GWO could gain 25.39% of increase in the average satisfaction for allotment of resources than the LP model.

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Published

04.11.2023

How to Cite

Kulkarni, D. ., Venkatesan, M. ., Kulkarni, A. V. ., & Menon, R. . (2023). Grey Wolf Optimization for Resource Allocation in RAN Slicing for Heterogeneous Requirements. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 230–241. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3701

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

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