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Greedy-Genetic Algorithm Based Video Data Scheduling Over 5G Networks

E. Elamaran1,*, B. Sudhakar2
1 Department of ECE, SRM Institute of Science and Technology, Chennai, 603203, India
2 Department of ECE, Annamalai University, Chidambaram, 608002, India
* Corresponding Author: E. Elamaran. Email:

Intelligent Automation & Soft Computing 2022, 32(3), 1467-1477. https://doi.org/10.32604/iasc.2022.020625

Received 31 May 2021; Accepted 31 August 2021; Issue published 09 December 2021

Abstract

Essential components in wireless systems are schedulin and resource allocation. The problems in scheduling refers to inactive users in a given time slot and in terms of resource allocation it refers to the issues in the allocation of physical layer resources such as power and bandwidth among the active users. In the Long Time Evolution (LTE) downlink scheduling the optimized problem refers to the flow deadlines that incorporate the formulation in the surveyed scheduling algorithm for achieving enhanced performance levels. The major challenges appear in the areas of quality and bandwidth constrains in the video processing sectors in 5G. The proposed work focuses on video processing through the new radio (NR) scheduler. Initially, a Priority scheduling Technique based on the greedy and genetic algorithm is proposed for offering better QOS in reduced time durations. Moreover, a greedy algorithm is used for both Optimizing the individual steps and for finding out the optimal solutions for resolving the entire problem. Genetic Algorithm is used for Optimizing the identified problems by means of relying on the selection attributes. Finally, the proposed algorithm proves to be efficient after the execution of a series of experiments.

Keywords

Greedy-genetic algorithm; energy efficiency; acclimation machine coding; multi input multi output; spectrum efficiency; zero-forcing reception/zero-forcing transmission; channel state information; radio resource management; poison point activity; resource block; acclimation machine coding; sub carrier succeeding; SRC-source UE; smaller base station

Cite This Article

E. Elamaran and B. Sudhakar, "Greedy-genetic algorithm based video data scheduling over 5g networks," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1467–1477, 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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