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Optimal and Energy Effective Power Allocation Using Multi-Scale Resource GOA-DC-EM in DAS

J. Rajalakshmi*, S. Siva Ranjani

Sethu Institute of Technology, Kariyapatti, Madurai, Tamil Nadu, India

* Corresponding Author: J. Rajalakshmi. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1049-1063.


Recently many algorithms for allocation of power approaches have been suggested to increase the Energy Efficiency (EE) and Spectral Efficiency (EE) in the Distributed Antenna System (DAS). In addition, the method of conservation developed for the allocation of power is challenging for the enhancement because of their high complication during estimation. With the intention of increasing the EE and SE, the optimization of allocation of power is done on the basis of capacity of the antenna. The main goal is for the optimization of the power allocation to improve the spectral and energy efficiency with the increased capacity of the cable with the help of an efficient optimal method with the model of clustering. In order to attain optimized allocation of power and for antenna optimization, the algorithm of Multi-scale Resource Grasshopper Optimization (MSR-GOA) is implemented. Besides, the clustering process is carried out using the algorithm for clustering namely Discriminative cluster-based Expectation Maximization (DC-EM) so as to minimize the rate of interference and computing complication. The analysis of performance is employed for evaluating the projected performance in various scenarios. The existing approach of investigation and comparison is made with the suggested system (DAS with MSR-GOA-DC-EM) with respect to EE and SE. From the analysis, it was apparent that the method projected here is highly efficient to provide high spectral and energy efficiency than the already available techniques.


Cite This Article

J. Rajalakshmi and S. Siva Ranjani, "Optimal and energy effective power allocation using multi-scale resource goa-dc-em in das," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1049–1063, 2022.

cc 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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