期刊论文详细信息
Emitter: International Journal of Engineering Technology
Analysis of Energy Consumption Using Sequential to Better Signal (SBS) Scheme for Green Celluler Network
Haniah Mahmudah1  Yetik Dwi Kusumaningrum2  Ari Wijayanti2  Nur Adi Siswandari2  Okkie Puspitorini2 
[1] PENS;Politeknik Elektronika Negeri Surabaya;
关键词: Energy Consumption;    SINR;    Sequential to Better Signal (SBS);    Green Cellular Network;   
DOI  :  10.24003/emitter.v8i1.481
来源: DOAJ
【 摘 要 】

Over time, cellular communication technology developed significantly from year to year. This is due to increasing the number of users and the higher needed. To overcome this problem, many providers increase the number of new base station installations to fill up the customer's needed. The increase number of base stations does not take into account the amount of power consumption produced, where in the cellular network Base Stations (BS) are the most dominant energy consuming equipment estimated at 60% - 80% of the total energy consumption in the cellular industry. In addition, energy waste often occurs in the BS where the emission power will always remain even if the number of users is small. Power consumption and energy savings are important issues at this time because they will affect CO2 emissions in the air. This paper proposes to save energy consumption from BS by turning off BS (sleep mode) if the number of users is small and distributed to other BS (neighboring BS) which is called cell zooming technique. The cell size can zoom out when the load traffic is high and zoom in when the load traffic is low. To determine the central BS and neighboring BS, a sequential to better signal (SBS) scheme is used where this scheme sorts neighboring BS based on the SINR value received (user). The results of this research, base station can be able to save energy 29.12% and reduce CO2 emission around 3580 kg/year.  It means saving energy consumption which is also reducing air pollution occurs and this term can be named as green cellular network.

【 授权许可】

Unknown   

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