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Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

M. R. Sivagar1,*, N. Prabakaran2
1 Department of Computer Science and Engineering, Sathyabama Institute of Science & Technology, Chennai, 600119, India
2 Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522502, Andhra Pradesh, India
* Corresponding Author: M. R. Sivagar. Email:

Computers, Materials & Continua 2022, 71(3), 5765-5781. https://doi.org/10.32604/cmc.2022.024273

Received 11 October 2021; Accepted 22 November 2021; Issue published 14 January 2022

Abstract

In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model, load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm. The simulation results show that the proposed model provides better results than exiting works in terms of efficiency, packet delivery ratio, Call Dropping Ratio (CDR) and Call Blocking Ratio (CBR).

Keywords

Spider monkey optimization; load balancing; long term evolution; optimal cell selection; handover; LTE networks; QoS

Cite This Article

M. R. Sivagar and N. Prabakaran, "Elite opposition based metaheuristic framework for load balancing in lte network," Computers, Materials & Continua, vol. 71, no.3, pp. 5765–5781, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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