Power consumption of cellular communication is growing at a very high rate due to the mass deployment of Base Stations (BSs). When traffic increases, the power consumption also increases, however this scenario differs in micro and macro BSs. Therefore intelligent energy management system as per traffic generated is very essential. The available models have not considered the impact of traffic load on energy consumption. These variations are analysed through regression models among power consumption and traffic load. Linear models have been proposed based on the measurements performed for ten consecutive days on three micro and three macro BSs. The results revealed that the proposed linear models fit better for macro BS than for micro BS. Energy consumption is observed to change along with the traffic load during high traffic, but during low traffic, energy consumption does not change. A macro BS is found to be more energy efficient than a micro BS due to its higher coverage range. On the contrary, a macro BS consumes about double power than that of a micro BS. Hence, micro BSs are suitable for areas with higher concentration of users where high data rates are required, whereas macro BSs are suitable to provide coverage only.
Base Station is the main contributor of energy consumption in cellular mobile communication. The traffic of base station varies over time and space. Therefore, it is important to quantify the influence of traffic generated on the power consumption. This paper investigates changes in the power consumption of base stations according to their respective traffic and develops a model for the power consumption as per traffic generated aiming to highlight the power consumption model development and address the power saving capabilities. The primary data in terms of power consumption and traffic load have been measured hourly on fully loaded 10 base stations for 10 days. The regression analysis shows the existence of a direct relationship between power consumption and traffic generated. A linear equation is developed is Y = 1.713×X + 1.274, where Y is power consumption and X is traffic generated, which shows that the power consumption of base stations linearly depends on the traffic generated. One noticeable point is that when the traffic is high, the developed linear equation indicates that the data fits well in the model but on the other hand, when the traffic is low, the developed linear equation explains less variability of the response around its mean. This paper also gives an overview of energy efficiency improvement possibility in base station.
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