Energy Conservation through Site Optimization for Mobile Cellular ...

Epistemics in Science, Engineering and Technology, Vol.x, No.y, 2012, xx-yy

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Epistemics in Science, Engineering and Technology

Energy Conservation through Site Optimization for Mobile Cellular Systems (Base Transceivers Station Optimization) 1

Nasir Faruk*, 2Mujahid Y. Muhammad, 3Olayiwola W. Bello, 4Abubakar Abdulkarim 5 Agbakoba John 6Mohammed I. Gumel 1,2 Department of Telecommunication Science, University of Ilorin, Nigeria 3 Department of Information and Communication Science, University of Ilorin, Nigeria 4 Department of Electrical Engineering, University of Ilorin, Nigeria 5 RF Department, MTN, Nigeria 6 RF Department, Huawei Technologies, Nigeria

Abstract Mobile communication systems have been firmly established as a key and convenient means of communication that enables efficient and effective business operation. The power consumption of wireless access networks has become a major economic and environmental issue. This paper investigates energy consumption of base transceivers stations (BTS), schemes that could potentially increase energy efficiency were described and the potential of reusing the conserved energy highlighted. Analysis of results shows that by optimizing 60 BTS’s, the energy consumption drops by 36.83% with 127.4MWh per month reserve, this correspond to powering 35 extra BTS’s with demand of 3.64224MWh per month or 69 houses for 10 hours daily power supply with energy demand of 1.8452MWh per month and 440 houses with total power demand of 289.8KWh per month. It was found that, number of sites optimized, duration of power consumption and its demand has significant effects on the number of extra BTS’s / houses powered. Keywords: Base Transceivers Station (BTS), energy conservation, power consumption, access network

1 Introduction Mobile communication systems have been firmly established as a key and convenient means of communication that enables efficient and effective business operation thereby making them central to business and daily life development. There are more than five (5) billion mobile cellular users across the world (ITU, 2010) and the demand of the wireless technologies and services increases rapidly every year. Wireless communication system is one of the most important technologies for contributing to social and economic development around the world. Studies have pointed to the significant contribution of mobile communications to GDP growth as a key to sustainability. In Nigeria, the penetration of mobile communication in the market has created job opportunities which contribute to the economic development. (Josiah et. al., 2007). At microeconomic level, the sector contribution to GDP increased by 53% in 2003 making it the third highest contributor ahead of the financial sector which has been in operation for about 100 years. In respect of employment, over 135, 000 persons have been directly or indirectly employed by the operators (Josiah et. al., 2007). In Sunday Newspaper of 17 July, 2011 a new report by the GSM Association (GSMA) disclosed that Nigeria stands to gain an additional N862 billion by 2015 from mobile broadband. Protecting the environment, combating global climate changes and the need to reduce energy consumption are major issues currently challenging mankind. Research outcomes have shown that, 3% of worldwide energy is consumed by information and communications technology (ICT) infrastructure which causes about 2% of world-wide CO2 emissions. In comparison, airplanes contributed one quarter of world-wide emission (Vadgama, 2009). In (Ericsson, 2007), the annual CO2 footprint of the average mobile subscriber is around 25kg which is comparable to driving car on the motorway for one hour, or running a 5W lamp for a year. Today’s typical wireless access networks consumes more than 50% of the total power consumption of mobile communications networks which excludes the power consumed by the mobile stations (user terminals) whose more than 50% of energy consumption is directly attributed to the base station (BTS) equipments. There are divergent opinions as regards the percentage of energy consumed by typical wireless network. However, the consumption ranges from 50 to 90% (Louhi, 2007, Richter et al., 2009, Vadgama, 2009). Mobile networks have considerable share (10% - Koutitas, 2009) in the overall energy consumption of the ICT (Information and Communication Technology) sector, which itself is though responsible for a very little share of the world energy consumption (3% - Vadgama, 2009). However, reduction in energy consumption of mobile networks is of great importance from economical (cost reduction), environmental (decreased CO2 emissions) and efficiency perspectives. Also, with anecdotal evidence suggesting that production of energy and its consumption are directly proportional to the sources of greenhouse gas and carbon based emissions, therefore


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whatever power is consumed by mobile networks calls for concerned and concerted efforts towards its conservation. Hence, both reduction in energy consumption and CO2 emission are key drivers in issues relating to modern ICT sectors. In a recent report by the International Telecommunications Union (ITU) and Alliance for Telecommunications Industry Solutions (ATIS), a number of energy efficient practices and methods for consideration by companies seeking to achieve greater efficiencies within their wireless networks have been outlined. There are active research works on energy consumption, reduction and efficiency in wireless access networks, but issue relating to the reusability of the reserved energy has not been explicitly addressed. This paper investigates energy consumption of base transceivers stations (BTS), schemes that could potentially increase energy consumption and distribution efficiency were described and the potential of reusing the conserved energy without compromising quality of service (Qos) of the network explored. The paper is organised as follows, section 2 describes the related work, gives extensive modelling of the energy consumption in section 3 and finally concludes in section 6. 2 Related Works In an attempt to obtain conservation of energy in mobile networks, Richter et al (2009), investigated the impact of deployment strategies on the power consumption of mobile radio networks. They considered layouts featuring varying numbers of micro-base stations per cell in addition to conventional macro-sites. By optimizing individual sites, e.g., through the use of more efficient and load adaptive hardware components as well as software modules and improved deployment strategies that will effectively lower the number of sites required in the network to fulfill certain performance metrics such as coverage and spectral efficiency. Louhi, (2007) proposed some methods that are capable of achieving energy efficiency by improving transmitter efficiency, system level features to use air cooling or to use alternative energy source (wind, solar etc.). In a similar vein, Emerson, (2008) opined that energy consumption in wireless networks should be varied depending on traffic load. They proposed that the reduction of number of active devices during off-peak period is capable of considerably saving energy consumption. Lubrittoa et al. (2011) showed the role played by air conditioning and transmission equipments while plotting the best areas of intervention for saving energy and improving environmental impact. Kumar et al. (2011) discussed the implementation of some important techniques like sleep scheduling, power saving algorithms for dynamic base stations as a means of achieving energy optimization and sustainability in wireless mobile networks. 3 Energy Efficiency and Conservation Energy conservation can be achieved through increased efficient energy use and innovative deployment strategies that have the potential of reducing energy consumption in access networks. The relative power rating of some of the various components at a BTS site is shown in Table 1 below. The goal of energy efficiency is to reduce the energy consumption without compromising the quality of service of the network. Energy efficiency in base stations can be achieved by addressing three major issues: Base Transceiver Station (BTS) optimization, Site optimization, and Radio Frequency (RF) network optimization. In this paper, optimization of BTS was considered. Table1- Power Consumption of Different Components of the BTS EQUIPMENTS POWER VALUE Digital signal processing PDP 100 W Power amplifier PAmp 100W Radio Unit (Transceiver) PRU 200 W AC-DC converter PCov 100 W Air conditioner (AC) PAC 1170 W Incandescent Bulb PLB 60W 3.1 Modelling Energy Consumption of the BTS The total power consumption (PBTS) in typical macro cell base station can be evaluated in equation (1) below by adding power consumption of each component of the BT listed in Table 1 above n

m

PBTS  PDP  PAmp  PRU  PCov   PACi   PLB j i

j

(1)


n

PBTS, PDP, PAmp, PRU, PCov ,

 PACi PACi and i

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m

P

LB j

are the total power consumed by the base transceiver

j

station, digital signal processing unit, power amplifier, radio unit, AC-DC converter, air conditions and incandescent bulbs respectively. Where n, m represents the total number of AC’s and bulbs in the site. The total energy consumption (EBTS) for the BTS is given in equation (2) EBTS=PBTS*t

(2)

where, t is the total time of usage (i.e. duration of power supply). Let ECB be the energy consumed before optimization and ECA the energy consumed after optimization then, the energy reserved (ER) can be evaluated from the relation in equation (3) ER=ECB - ECA

E CBavr  ECAavr Percentage Decrease = x100% E CBavr where E CBavr

,

(3) (4)

ECAavr are the average monthly energy consumption before and after optimization respectively.

3.2 Realistic energy consumption for bts There are several factors that could affect the BTS power consumption, this may include the traffic load which varies as a function of time due to variation in demand of the services and the statistical population of an area. This indicates that, the power consumption of individual BTS may vary with location area. Considering realistic power consumption for typical mobile operator in Nigeria BTS, this includes both the transceivers and the microwave radio unit for 2G network with 9 transceivers in three sector cells all operating in 900MHz spectrum band and 36 transceivers operating in 1800MHz for capacity in the network. In the event of network upgrade, 3G network is usually installed in the same BTS to minimize cost. This is as shown in Table 2 below Table 2- Realistic Energy Consumption of BTS Configuration/equipments Power (watts) 2G 9TRX 900BAND, 36TRX 1800BAND 5760 2G 6TRX 900BAND 36TRX 1800BAND + 3G 6240 AC, 1.5HP X 2 2340 AC, 1HP X 2 1480 Total power consumption for 2G with two 1.5HP AC 8100 Total power consumption for 2G with two 1HP AC 7240 Total power consumption for 2G and 3G with two 1.5HP AC 8580 Total power consumption for 2G and 3G with two 1HP AC 7720 Typical power consumption for 1 BTS is presented. In densely populated states like Kano and Lagos in Nigeria, and to provide Qos in the network, there could be over 130 BTS’s in clusters within each of the states. Each of these BTS’s running for 24 hrs service on daily basis consuming enormous amount of energy. By implementing minor energy efficiency (EE) strategies in each BTS significant savings in energy could translate to cost reduction or conservation of energy for alternative use. 3.3 Base transceiver station site optimization One way energy efficiency can be improved is through the Main Remote solution also known as tower topmounted radios (Ericson, 2007). This can reduce energy consumption by two-thirds. In the traditional network deployment, all the radio base stations (RBS) equipments are located in a shelter or in an outdoor on the ground as shown in Figure 1 Alt 1. The radio units are connected to the antennas using feeder cables, which can be several tens of meters long. Typically half of the emitted power from the radio transmitters is lost in the feeders (Ericson, 2007). To improve the energy efficiency in the BTS, the Main Unit could now, as an alternative, be housed in an outdoor casing adjacent to the Remote Radio Unit(s) on the tower as shown in Figure 1 Alt 2 this means that, either the input power can be halved, or the output power can be doubled for the same input. In addition, both site planning and Installations are simplified, as the RBS has virtually no footprint. Cooling


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systems such as the air conditioner units used to secure the battery lifetime are also eliminated, as the Main Unit can be cooled through natural convection.

Figure 1. Main Remote configurations Source: Ericson 4 Simulation Results The average monthly energy consumption for 120 BTS’s operating at 24 hrs on a daily basis was evaluated and the typical power consumption in Table 2 was used. The average monthly energy consumption, before and after optimization was evaluated and analysed. The results are shown in Figures 2 and 3 below. 2G 9TRX + Microwave with two 1HP AC 2G 9TRX + Microwave with two 1.5HP AC 2G 6TRX+3G + Microwave with two 1.5HP AC 2G 6TRX+3G + Microwave with two 1HP AC 700 650

Average Monthly

Energy Consumption (MWh)

600 550 500 450 400 350 300 250 200 150 100 50 0 10

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Number of Sites Deployed

Fig.2. Average monthly energy consumption for 2G, and 2G+3G network with number of sites deployed


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The bar chart in Fig 2 above indicates the energy consumption for 2G BTS’s and 2G+3G BTS’s using 1HP and 1.5HP air conditioner system based on the assumption that the power consumption in each of the BTS is constant. An increase in energy consumption as the number of BTS’s in the sites increases was observed

Before Optimization After Optimization Reserved Energy for reuse 700 650

EnergyConsum ption(M W h)

AverageM onthly

600 550 500 450 400 350 300 250 200 150 100 50 0 10

20

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Number of Sites Deployed

Fig.3. Average Monthly Energy Consumption (MWh) with Number of Sites The Average Monthly Energy Consumption in Figure 3 was estimated based on 2G 6TRX 900BAND 36TRX 1800BAND + 3G with two 1.5HP AC. It was also assumed that the site is using one AC-DC converter, and four incandescent bulbs. After optimization, no AC’s, bulbs and AC-DC converter used, this reduces the total power consumption to 5420W and the energy consumption for 10 BTS’s dropped from 57.66MWh per month to 36.422MWh per month representing 36.83% decrease. It should be however noted that the energy loss from the cable feeder is not included. Using equation (3), the reserve energy was evaluated as a function of number of sites deployed in the network. Fig 3 shows the energy before optimization, after optimization and reserved energy which is equivalent to about 21.23MWh per month reserve when utilizing 10 BTS’s. This increases to 127.4MWh per month when using 60 BTS’s representing 83.33%, and 91.67% for 120 BTS’s. Conclusively, the reserve energy increases as the number of sites increase. 5.0 Appropriating the Conserved Energy Energy has been conserved and can be alternatively used. From the analysis in Fig 3 above, the reserved energy obtained when 60 BTS’s is optimized could power extra 35 more BTS’s each consuming 3.64224MWh per month. This will therefore reduce the total cost of providing power to the BTS’s. In a situation of reusing the reserved energy to power household, Table 3 below illustrate typical power consumption demands for a household. Table3- Typical Power Consumption Demands for household Components Power Rating Quan Hrs/ Watts x Qty x hrs/ Average Monthly (Watts) tity day day Energy Wh/month (Qty) Medium size deep freezer 130 1 24 3120 87360 Washing Machine 280 1 24 6720 188160 Microwave Oven 1000 1 24 24000 672000 Electric pressing iron 1000 1 24 24000 672000 Air-Conditioner 1170 1 24 28080 786240 Refrigerator 500 1 24 12000 336000 Ceiling fan 100 5 24 12000 336000 Incandescent Bulb 60 23 24 33120 927360 21" TV 100 1 24 2400 67200 14" Television 80 1 24 1920 53760


Sony Music System DSTV Receiver DVD Player Computer printer computer PC Computer Laptop Total

100 50 50 100 115 35 4870W

1 1 1 1 1 1

24 24 24 24 24 24

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2400 1200 1200 2400 2760 840 158.160KWh/day

67200 33600 33600 67200 77280 23520 4.42848MWh/month

The consumption stated in Table 3 above will vary depending on the class of the household and the availability of the power supply (number of hours) which we categorised as lower, middle and upper class. Table 3 gives typical power consumption for an upper class household. In the case of lower and middle class categories, some of the components such as air-conditioner systems, washing machine, microwave oven and refrigerator may not be available. In this paper, realistic energy consumption demands and availability of power supply for a typical Nigerian household are considered. Figure 4 below illustrates the average monthly energy consumption as a function of class category and number of consumption hours.

Average Monthly Energy Consumption (MWh)

Lower Class Category Middle Class Category Upper Class Category 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 8

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Time (Hours)

Fig. 4. Variation of average energy consumption with class category In Fig 4 above, the energy consumption varies with class category and this increases as the consumption hours increases. The least is lower class category where the major power consumption components are not available. Based on the analysis on the number of houses that could be powered using the reserved energy in Fig 3, depending on the energy consumption of each of the class category and the consumption hours in Fig 4, it was found that the number of houses powered per month increases with increase in the number of optimized sites as shown in Fig 5 below

Number of houses powered

per month on 10 hrs daily supply


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Upper Class Middle Class Lower Class

900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0 10

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Number of sites deployed

Fig .5. Numbers of houses powered per month for 10 hours daily power supply with class category Optimization of 60 BTS’s would produce a reserve energy that would power 69 houses per month for 10 hours daily power supply in the upper class category with total power demand of 1.8452MWh per month and this increases to 440 for lower class category houses with total power demand of 289.8KWh per month representing 84.3% increase. This varies with the duration of the power supply, as the daily hours power supply increases, the total power demand increases which in turn decrease the number of houses that can be powered. 6 Conclusions In this paper, we have examined the energy consumption in wireless access networks particularly base transceiver stations (BTS) which is one of the major energy consuming components of the network. Energy efficiency optimization technique has been deployed to reduce the energy consumption in 120 BTS’s and provide reserve for reuse. Analysis of results when 60BTS’s were deployed showed that, this reserved energy could be used to power extra 35 BTS’s or several households for domestic use. References Amit K. L. Yunfei. S. Tanvir, and . S. K. Sawtantar. 2011. Sustainable Energy Optimization Techniques in Wireless Mobile Communication Networks. The First International Conference on Interdisciplinary Research and Development, 31 May - 1 June 2011, Thailand ATIS Report on Wireless Network Energy Efficiency January 2010 Bashir Y. and J. Ben-Othman .2008. An Energy Efficient Hybrid Medium Access Control Scheme for - IEEE. IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2008 proceedings. 978-1-4244-2324-8/08. Blume, O. D. Zeller and U. Barth. 2010. Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium. Emerson Network Power, Energy Logic for Telecommunications’’ 2008 A white paper from the experts in Business –critical continuity, Ericsson White Paper on Sustainable energy use in mobile Communications, August 2007, International Telecommunication Union (ITU), http://www.itu.int/newsroom/press_releases/2010/06.html visited 20/07/2010 Josiah O. A., O. A. Emmanuel and I. W. James. 2007. Stakeholders’ Perceptions of the Impact of GSM on Nigeria Rural Economy: Implication for an Emerging Communication Industry Journal of Information Technology Impact, Vol. 7, No. 2, pp. 131-144 Koutitas, G., and P. Demestichas. 2009 A Review of Energy Efficiency in Telecommunication Networks, 17thTelecommunications forum TELFOR 2009, Serbia, Belgrade, November 24-26, 2009, pp. 1-4


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Louhi Jyrki T 2007 “Energy efficiency of modern cellular base stations”, INTELEC 2007, Rome, Italy. Lubrittoa C, A. Petragliaa,*, C. Vetromilea, S. Curcurutob, M. Logorellib, G. Marsicob, A. D'Onofrioa. (2011) Energy and environmental aspects of mobile communication systems Energy, Volume 36, Issue 2, February 2011, Pages 1109-1114. Richter F, Albrecht J. Fehske, Gerhard Fettweis. Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks. In Proceedings of Vehicular Technology Fall (VTC 2009-Fall), 2009 IEEE 70th. pp.1~5 Sunday Newspaper on 17 July, 2011 Vadgama Sunil .2009. Trends in Green Wireless Access. Fujitsu Science. Tech. J., Vol.45, No.4, October, pp. 404-408