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Full Paper Proc. of Int. Conf. on Advances in Signal Processing and Communication 2012

Green Cellular Techniques for Energy Saving Jyotbir KaurLitt 1Ameeta Seehra

Research Student,GNDEC,Ludhiana Punjab ,India .Email:birjyot@yahoo.in 2 Associate Professor, GNDEC, Ludhiana Punjab,IndiaEmail:am_seehra@yahoo.co.in Abstract: Energy-efficiency has become very important due to increased awareness of environmental and economic issues for network operators. In this paper, the impacts of increased energy consumption are discussed and then possible solutions for an energy efficient cellular network are suggested. The techniques of cell zooming are studied and results evaluated on real network data using BS sleeping algorithm. Keywords: BS-Cooperation,, BS-Sleeping, BS -blossoming, BS-wilting, Cell-zooming GREEN, OPEX, Relaying, TANGO

I. INTRODUCTION

Figure 1 Distribution of Power Consumption For Conventional Base Station

Traditionally, mobile communication networks have been designed for maximum throughput and maximum spectral efficiency while promising QoS(Quality of service) to the user. With advent of new technologies like 4G LTE(Fourth Generation Long Term Evolution) , speed and efficiency were enhanced. Consequently, the growth rate of data traffic on mobile networks has been approximately 400% p.a, which is expected to grow at the same rate in coming years. Indeed, the number of new services being offered and the increase in the volume of data traffic follow Moore’s law, doubling every 18 months.[1]This growth demands a much higher energy consumption than it is today. There have been several reasons for the growing awareness of energy-efficient wireless networks in the telecommunication community. Increasing energy prices and an increasing share of base stations that are not connected to the electricity grid (off-grid sites are typically diesel powered, where fuel is costly and distribution often unreliable for distant sites that are difficult to access) imply that electricity bills and fuel-operated sites have become a significant cost factor for mobile operators i.e. operational expenditure (OPEX). The radio access part of the cellular network is a major energy killer, which accounts for up to more than 70% of the total energy bill for a number of mobile operators[2]. Because the base station accounts for most of the energy consumption by mobile operators, improving the energy efficiency of base station key components, such as power amplifiers and air conditioners, is of great importance, as shown in Fig.1 [3] Besides this operator cost issue, the rising energy consumption of mobile networks also contributes to the global emission of greenhouse gases and to global warming. Mobile network infrastructure (without mobile devices) emitted 64 Megatons of CO2 in 2002 and increases are projected through 2020 to 178 Megatons.[4] By deploying energy-efficient base © 2012 ACEEE DOI: 02.SPC.2012.01. 6

stations, operators can reduce the CO2 emission from their network. In order to dramatically reduce energy consumption, several solutions have been suggested. II. POSSIBLE SOLUTIONS A. GREEN: (Globally Resource- optimized & Energy-Efficient Networks) is one of the major initiatives taken in field of energy saving. Green radio has been a key enabler for cellular growth while guarding against increased environmental impact. In NEC’s Green Radio approach for next-generation mobile infrastructure solutions, energy-efficient operation is part of the overall design. The base station platform consists of energy-efficient key devices and enables operation without an air conditioner. In a conventional base station, a cooling fan consumes 10–20% of the overall energy. Using efficient heat transfer packaging in BS, allowed NEC to move to fanless operation. It also provided intra- and inter-base station control mechanisms to adapt to changing demand for capacity at different locations and times in its resource usage, network coverage and energy consumption.[3] B.TANGO: Traffic Aware Network Planning and Green Operation.[5] This technique has been adapted to traffic volume (daily and weekly), traffic characteristics (unicast, multicast, broadcast) and QoS requirements (realtime, non realtime). The network resources need only be ‘always available’ rather than ‘always on’ if the coverage can be guaranteed. The traffic dynamics can, in fact, provide some opportunities for energy savings. As shown in Fig. 2, one can trace the traffic variation and adapt the radio resources (including transmitting power and other equipment’s power) in a cell or the whole cellular network to it, hence a great amount of energy could be saved. 111

Full Paper Proc. of Int. Conf. on Advances in Signal Processing and Communication 2012 service. In addition, the current BSs are also designed to continuously listen to the radio environment in order to detect incoming mobile users. Continuous emission and reception are always-running processes, that consume energy even when no user is using or requesting the BS service (e.g., at night). Thus, the application of sleep modes to BSs constitutes a promising approach to improve energy efficiency. Sleep modes allow turning off BSs when and where they are not necessary, especially in low-load periods, such as nights, weekends, holidays. Up to 20-40% of energy can be saved with this approach.[7,8]  Sleep mode entrance Sleep mode entrance procedure must address two aspects: (a) detect when a BS can enter sleep mode (b) describe how the transition from active to sleep state is implemented. Once the sleep mode trigger is confirmed, the transition from active to sleep state can start. This transition must take place in such a way that any interruption of ongoing calls/ sessions is prevented. Call drops can be avoided by adopting a slow reduction of the BS transmit power. This technique is commonly named BS wilting or progressive switch-off.(Fig.4)

Figure 2 Conceptual figure of the framework TANGO [5]

C.A New SON(Self Organizing Network) Scheme: Cell Zooming Cell Zooming is one of the methods of efficient energy usage. It adaptively adjusts the cell size according to traffic conditions and it has the potential to balance the traffic load and reduce the energy consumption.[6] There is a cell zooming server (CS), which controls the procedure of cell zooming. The CS is a virtual decision making entity in the network. If a cell needs to zoom in or zoom out, it will coordinate with its neighbor cells with the help of CS. Then these cells will either zoom in or zoom out by network operations such as physical adjustment, BS cooperation and relaying.  BS Sleeping is one of the cell-zooming techniques. When a BS is working in sleep mode, the air-conditioner and other energy consuming equipments can be switched off, thereby reducing the energy consumption of cellular network. In this case, the cell with BS working in sleep mode zooms in to 0, and its neighbor cells will zoom out to guarantee the coverage as shown in Fig.3

Figure 4 Example of BS wilting procedure.[9]

 Sleep mode exit Sleep mode exit (BS wake-up) should be triggered when the data traffic conditions imply a high risk of overload in neighbouring active BSs, or an unacceptable quality of service for end users. An obvious solution is thus to allow the active BSs to alert sleeping neighbours as soon as their load exceeds a given threshold, close to saturation. The transition from sleep to active mode must be implemented so that denial of service and call drops are prevented. This can be achieved through a progressive switch-on process, known as BS blossoming, during which the power of the BS is slowly raised to its nominal target value. Other techniques to implement cell zooming are given :-

Figure 3 Base station cooperation & BS sleeping [6]

Unused BSs have been the main sources of energy waste in current wireless networks because BSs need to permanently signal their presence and the availability of the cellular © 2012 ACEEE DOI: 02.SPC.2012.01. 6

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Full Paper Proc. of Int. Conf. on Advances in Signal Processing and Communication 2012  Physical Adjustment: In this technique adjustment of physical parameters of network deployment helps to implement cell zooming. Cells zoom out by increasing the transmit power of BS, and vice versa. Furthermore, antenna height and antenna tilt of BSs can be adjusted mechanically, for cells to zoom in or zoom out.  BS Cooperation: BS cooperation means multiple BSs form a cluster, and cooperatively transmit to or receive from mobile user. The new formed cluster is a new cell from user’ perspective, whose cell size is the sum of the original size of the BSs in cooperation. The size can be even larger, as BS cooperation can reduce inter-cell interference. In this case, cells zoom out to improve the coverage.  Relaying: Relay stations (RSs) are deployed in cellular networks to improve the performance of cell-edge MUs. RSs can also be deployed near the boundary of two neighboring cells. In this case, RSs can relay the traffic from the cell under heavy load to the cell under light load. The former cell zooms in, and the latter cell zooms out. Benefits of using above schemes are reduced power consumption. There will be reduction in OPEX (operational expenditure) of the cellular network. Environmental concerns are also addressed such as lowered energy production and reduced CO2 emissions.

Where Tj : Traffic Load/site Tj =[ Bij]/Bt (2) Bt : Total system Bandwidth (7MHz) Performance evaluation graphs have been shown below.

Figure [5] Real Traffic Profile

The traffic profile above in fig.[5] shows a regular pattern over the day (0-24)hrs with low traffic periods during night hours (after 11pm) and a peak during evening hours(around 8pm) when people are at their leisure using their mobile devices for phone calls and for surfing the Internet. During daytime, the so-called peak traffic period is observed at about 11am.Generally speaking, holiday or weekend traffic is lower than weekday traffic, and night-time traffic is much lower than that during daytime. In Fig.6, it has been shown that for the same traffic profile for 10BSs, power saving can be achieved by BS sleeping technique. In Fig.6, straight line marked by dots depicts power consumed by 10 BSs without applying the energy saving technique.

III. CASE STUDY Cell-Zooming Technique based on Real Traffic Data[10] A densely deployed cellular network is considered in which the coverage of BSs(Base Station) overlaps and traffic load fluctuates over time . Assume there are M=10 BSs with same energy consumption. There are two working modes for each BS: active mode with energy consumption Pa=1200W and sleeping mode with power consumption Ps=200W.MUs(Mobile Users) follow a Poisson process for their arrival, and each MU will be associated with one BS upon its arrival. Each MU exhibits an exponential distribution for its sojourn time. The rate requirement for each MU(mobile user) is fixed, denoted by Ri for MU i. The spectral efficiency is fij when MU i is associated with BS j. Therefore the bandwidth needed is given by Bij = Ri/fij. We assume the spectral efficiency is independent of the associations among other BSs and MUs. The total bandwidth for BS j is Bt . When a new MU arrives, if there is not enough bandwidth to be allocated, the MU will be blocked. In order to minimize the number of active BSs, traffic load should be concentrated to a few BSs so the remaining BSs under light load can be switched off. The centralized algorithm has been taken up and implemented on real traffic data collected from actual network operators.

Figure [6] Power savings by BS sleeping

The varying curve marked by triangles depicts modified reduced power consumption pattern when BS sleeping algorithm (Centralized) has been implemented. BSs going in sleep mode consume less power(10W) than active BSs (400W)

IV. PERFORMANCE EVALUATION We take sample data traffic in Erlangs/site for 10 BSs .System bandwidth is 7MHz and user rate is 75Kbps.Each user’s bandwidth is calculated as given by Equations 1,2: Bij=Tj*Bt © 2012 ACEEE DOI: 02.SPC.2012.01. 6

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Full Paper Proc. of Int. Conf. on Advances in Signal Processing and Communication 2012 REFERENCES [1] Raffaele Bolla and Franco Davoli, “The Potential Impact of Green Technologies in Next-Generation Wireline Networks: Is There Room for Energy Saving Optimization?”, National Inter-University Consortium for Telecommunications. [2] Yan Chen,S.Zhang,S.Xu,G.Y.Li et al. “Fundamental Tradeoffs on Green Wireless Networks”. Jan 2011 Huawei Publications [3] White paper NEC FEB2010 - Green Radio “NEC’s Approach towards Energy-efficient Radio Access Networks” [4] Oliver Blume, Harald Eckhardt, Siegfried Klein, Edgar Kuehn, andWieslawa M. Wajda et al.”Energy Savings in Mobile Networks Based on Adaptation to Traffic Statistics” Bell Labs Technical Journal 15(2), 77–94 (2010) © 2010 Alcatel-Lucent [5] Zhisheng Niu .” TANGO: Traffic-Aware Network Planning And Green Operation ’’IEEE Wireless Communications • October 2011 pg.25-29 [6] Zhisheng Niu, J.Gong,Z.Yang,W.Yu.., “Cell Zooming for CostEfficient Green Cellular Networks,” IEEE Commun. Mag., Nov. 2010. International symposium on personal, indoor and mobile radio comm. pg 1665-1670 [7] M. Ajmone Marsan, L. Chiaraviglio, D. Ciullo, M. Meo, Optimal Energy Savings in Cellular Access Networks, GreenComm’09 - 1st International Workshop on Green Communications, Dresden, Germany, June 2009. [8] M. Ajmone Marsan, L. Chiaraviglio, D. Ciullo, M. Meo, Energy- Efficient Management of UMTS Access Networks, ITC 21 - 21st International Teletraffic Congress, Paris, France, September 2009 [9] Luca Chiaraviglio et al. “ Cell Wilting and Blossoming for Energy Efficiency” Alcatel-Lucent Bell Labs, Villarceaux, France , Electronics Department, Politecnico di Torino, Italy ,Institute IMDEA Networks, Madrid,Spain [10] Network operator Ericsson : Source of traffic load data

Figure [7] Traffic & power variations of 10BSs

A particular case is taken using centralized algorithm on 10BSs .The proportion of reserved bandwidth is assumed, a=[0.9 .56 .89 .96 .98 .99 .96 .56 .36 1] for all the BSs randomly. Dotted Bars(light) in Fig7. show traffic load of each BS & lined(black) bars depict power of each BS in accordance whether BS is asleep or active. Thus Fig 8. hints towards energy saving by BS sleeping. V. CONCLUSIONS This paper provides an overview of the latest Green cellular techniques available, focused on improving the energy efficiency of cellular access networks. These techniques for energy saving were discussed & cell zooming algorithm was implemented on real traffic data. It is revealed that a considerable amount of energy saving is obtained by BS sleeping technique for low traffic load, while maintaining coverage. Thus it is concluded that BS sleeping is an energy conserving technique for BSs. Energy efficiency is an important aspect that is used in the cellular networks. The techniques discussed in this paper could be profitably used by cellular network operators for considerable energy savings. Other cell zooming techniques can be investigated for future research.

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