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Nowadays, the cellular communication has become worldwide with its advancements. Still, there are some major drawbacks: poor resource allocation and less data transmission range. Hence, the poor allocation behavior has maximized the dropped links rate. So, the current research work has aimed to model a novel Krill Herd Resource Allocation Strategy (KHRAS) for 5G cellular communication systems (CCSs). Moreover, the communication medium is designed by Rayleigh fading parameters, then the user needs and the required resource of each user are estimated by the fitness of the krill herd function. Hence, the fitness solution of the krill herd is upgraded in the dense frame of the recurrent architecture; it provides the finest prediction and resource estimation results. In addition, the planned model is executed in the MATLAB environment, and the successive score of the designed model is validated by gaining a high data transmission rate, throughput, energy efficiency (EE), and less processing time.

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Wireless Cellular Communications for Efficient Resource Allocation

  • 会议论文

  • 发表日期：

    2025年

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  • J. Jude Moses Anto Devakanth,
  • J. Kavitha,
  • L. Sharmila,
  • R. Roselinkiruba,
  • M. Misba,
  • V. Velmurugan

摘要

Nowadays, the cellular communication has become worldwide with its advancements. Still, there are some major drawbacks: poor resource allocation and less data transmission range. Hence, the poor allocation behavior has maximized the dropped links rate. So, the current research work has aimed to model a novel Krill Herd Resource Allocation Strategy (KHRAS) for 5G cellular communication systems (CCSs). Moreover, the communication medium is designed by Rayleigh fading parameters, then the user needs and the required resource of each user are estimated by the fitness of the krill herd function. Hence, the fitness solution of the krill herd is upgraded in the dense frame of the recurrent architecture; it provides the finest prediction and resource estimation results. In addition, the planned model is executed in the MATLAB environment, and the successive score of the designed model is validated by gaining a high data transmission rate, throughput, energy efficiency (EE), and less processing time.