Energy Competence of Base Station in cellular Network
J. Premalatha1, Sahaya Anselin Nisha2
1J. Premalatha, Department of Electronics and Communication, Sathyabama Institute of Science and Technology, Chennai, India.
2Dr. Sahaya Anselin Nisha, Department of Electronics and Communication, Sathyabama Institute of Science and Technology, Chennai, India.
Manuscript received on 16 March 2019 | Revised Manuscript received on 21 March 2019 | Manuscript published on 30 July 2019 | PP: 2020-2023 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2089078219/19©BEIESP | DOI: 10.35940/ijrte.B2089.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Energy efficiency is the key concept of wireless communication to achieve green network. Green networking is the process that reduces consumption of energy as well for conserving bandwidth and also for any other process that will ultimately reduce energy use and, indirectly, the expense. With the rapid growth of technologies in wireless network and rapid increase of mobile users the problem of spectrum usage as well as energy consumption plays a vital role. As there is anexponential increase in the deployment of base station every year the power consumed by base station is the significant theme of intrigue. The increase in the number of base stations also leads to environment impact of CO2 emission which is normally due to powering up the base station which is located in remote areas as these off-grid sites are powered by diesel generators. It is been predicted that if this trend continues then the energy consumed by cellular network in future will lead to a serious problem. Thus, there has to be a tradeoff between the quantity of subscribers and the quantity of base station or otherwise it will affect the system throughput. In this paper a brief review of methods that have been used recently to improve the energy consumed by the base station is analyzed.
Keywords: Energy Efficiency, Base Station, CO2, Sleep Mode, Optimization, Genetic Algorithm
Scope of the Article: Discrete Optimization