Green Cellular Networks: A Survey, Some Research Issues and Challenges

1 Green Cellular Netw orks: A Surv e y , Some Research Issues and Challenges Ziaul Hasan, Student Member , IEEE, Hamidreza Boostanimehr , Student Member , IEEE, and V ijay K. Bharg av a, F ellow , IEEE Abstract —Energy efﬁciency in cellular networks is a gr owing concern for cellular operators to not only maintain proﬁtability , but also to r educe the overall envir onment effects. This emerging trend of achieving energy efﬁciency in cellular networks is motivating the standardization authorities and network operators to continuously explore futur e technologies in order to bring impro vements in the entir e network infrastructur e. In this article, we present a brief survey of methods to impro ve the power efﬁciency of cellular networks, explore some r esearch issues and challenges and suggest some techniques to enable an energy efﬁcient or “green” cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will ﬁrst pro vide a comprehensi ve surv ey on techniques to obtain energy sa vings in base stations. Next, we discuss how heterogenous network deployment based on micro, pico and femtocells can be used to achieve this goal. Since cognitive radio and cooperati ve r elaying ar e undisputed futur e technologies in this regard, we propose a resear ch vision to make these technologies more energy efﬁcient. Lastly , we explore some broader perspectives in realizing a “green” cellular network technology . Index T erms —Gr een communication, energy efﬁcient net- works, efﬁciency metrics, microcells, picocells, femtocells, cog- nitive radio, cooperative relaying. I . I N T RO D U C T I O N During the last decade, there has been tremendous growth in cellular networks market. The number of subscribers and the demand for cellular trafﬁc has escalated astronomically . With the introduction of Android and iPhone de vices, use of ebook readers such as iPad and Kindle and the success of social networking giants such as Facebook, the demand for cellular data trafﬁc has also grown signiﬁcantly in recent years. Hence, mobile operators ﬁnd meeting these ne w demands in wireless cellular netw orks ine vitable, while they have to keep their costs minimum. Such unprecedented growth in cellular industry has pushed the limits of energy consumption in wireless networks. There are currently more than 4 million base stations (BSs) serving mobile users, each consuming an av erage of 25MWh per year . The number of BSs in dev eloping regions are expected to almost double by 2012 as sho wn in Fig. 1. Information and Communication T echnology (ICT) already represents around 2% of total carbon emissions (of which mobile networks represent about 0.2%), and this is e xpected to increase e very year . In addition to the environmental aspects, energy costs also represent a signiﬁcant portion of network operators’ ov erall expenditures (OPEX). While the BSs connected to electrical grid may cost approximately 3000$ per year to t h e s u p p l y o f p o w e r t o b a s e s t a t i o n s . T h i s d e f a u l t i s n o w s h i f t i n g , a n d t h e G S M A h a s e s t a b l i s h e d t h e G r e e n P o w e r f o r M o b i l e p r o g r a m m e ( G P M ) t o a d v a n c e t h e u s e o f r e n e w a b l e e n e r g y s o u r c e s b y t h e m o b i l e i n d u s t r y t o p o w e r 1 1 8 , 0 0 0 n e w a n d e x i s t i n g o f f - g r i d b a s e s t a t i o n s i n d e v e l o p i n g c o u n t r i e s b y 2 0 1 2 . T o p r o v i d e c o v e r a g e f o r t h e e x p a n d i n g s u b s c r i b e r b a s e i n d e v e l o p i n g w o r l d m a r k e t s , m o b i l e o p e r a t o r s a r e d e p l o y i n g v a s t q u a n t i t i e s o f b a s e s t a t i o n s . B a s e d o n a v a i l a b l e d a t a a n d f o r e c a s t i n g , t h e G S M A p r o j e c t s t h a t t h e n u m b e r o f o f f - g r i d b a s e s t a t i o n s i n t h e d e v e l o p i n g w o r l d w i l l i n c r e a s e f r o m 2 8 8 , 0 0 0 i n 2 0 0 7 t o 6 3 9 , 0 0 0 i n 2 0 1 2 . T h e s e b a s e s t a t i o n s a r e a l w a y s l o c a t e d c l o s e t o u r b a n o r r u r a l c o m m u n i t i e s a s i t i s n e c e s s a r y f o r t h e s u b s c r i b e r s t o b e w i t h i n r a n g e o f a b a s e s t a t i o n ’ s c o v e r a g e . 1 . 2 . T h e C o m m u n i t y P o w e r O p p o r t u n i t y T h e m o b i l e p h o n e i n d u s t r y h a s s e e n p h e n o m e n a l g r o w t h o v e r t h e p a s t t w o d e c a d e s . G l o b a l l y , t h e n u m b e r o f m o b i l e p h o n e c o n n e c t i o n s i s n o w 4 . 5 b i l l i o n a n d w i l l r e a c h 6 . 2 b i l l i o n b y 2 0 1 3 5 . T h e m a j o r i t y o f f u t u r e g r o w t h i n c o n n e c t i o n s w i l l c o m e f r o m d e v e l o p i n g w o r l d m a r k e t s a s m o s t d e v e l o p e d w o r l d m a r k e t s a r e c l o s e t o 1 0 0 % p e n e t r a t i o n . T h e g e o g r a p h i c e x p a n s i o n o f m o b i l e n e t w o r k s t o p r o v i d e c o v e r a g e t o t h e g l o b a l p o p u l a t i o n r e l i e s o n r a d i o t o w e r s , o r “ b a s e s t a t i o n s ” , t h a t c o n v e r t e l e c t r i c i t y i n t o r a d i o w a v e s . I n d e v e l o p e d a r e a s , b a s e s t a t i o n s a r e e a s i l y c o n n e c t e d t o a n e l e c t r i c i t y g r i d f o r a r e l i a b l e e n e r g y s u p p l y . H o w e v e r , i n d e v e l o p i n g a r e a s , w h e r e g r i d e l e c t r i c i t y i s u n r e l i a b l e o r a b s e n t , o p e r a t o r s h a v e l a r g e l y r e l i e d o n d i e s e l - p o w e r e d g e n e r a t o r s f o r G r e e n P o w e r f o r M o b i l e C o m m u n i t y P o w e r F i g u r e 4 : G r o w t h i n B a s e S t a t i o n s i n D e v e l o p i n g R e g i o n s 2 0 0 7 - 2 0 1 2 S o u r c e : G S M A R e s e a r c h 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 2007 2012 2007 2012 2007 2012 2007 2012 2007 2012 2007 2012 2007 2012 T otal Middle East/North Africa Latin America and Carabbean Sub- Saharan Africa Eastern Europe and Central Asia South Asia East Asia and Pacific On-grid Base Stations Off-grid Base Stations 5 W i r e l e s s I n t e l l i g e n c e ( h t t p s : / / w w w . w i r e l e s s i n t e l l i g e n c e . c o m ) G S M A s s o c i a t i o n 2 0 1 0 0 9 Fig. 1. Growth in base stations in developing regions 2007-2012 (GSMA Research) [1] operate, the off-grid BSs in remote areas generally run on diesel power generators and may cost ten times more. The rising energy costs and carbon footprint of operating cellular networks hav e led to an emerging trend of addressing energy-ef ﬁciency amongst the network operators and regu- latory bodies such as 3GPP and ITU [4], [5]. This trend has stimulated the interest of researchers in an innov ativ e new research area called “green cellular networks”. In this regard, the European Commission has recently started new projects within its sev enth Framew ork Programme to address the energy efﬁcienc y of mobile communication systems, viz. “Energy A ware Radio and NeT work T ecHnologies (EAR TH)”, “T owards Real Energy-ef ﬁcient Network Design (TREND)” and “Cognitiv e Radio and Cooperativ e strategies for Power saving in multi-standard wireless devices (C2POWER)” [6], [7], [8]. “Green radio” is a vast research discipline that needs to cover all the layers of the protocol stack and various system architectures and it is important to identify the fundamental trade-offs linked with energy ef ﬁciency and the overall per- formance [9]. Figures 2(a) and 2(b) show a breakdo wn of power consumption in a typical cellular network and giv es us an insight into the possible research av enues for reducing energy consumption in wireless communications. In [9], the authors hav e identiﬁed four key trade-offs of energy efﬁcienc y with network performance; deployment ef ﬁciency (balancing deployment cost, throughput), spectrum ef ﬁcienc y (balancing achiev able rate), bandwidth (balancing the bandwidth utilized) and delay (balancing average end-to-end service delay). T o address the challenge of increasing power efﬁciency in future 2 Cellular Network Power Consumption Retail Data Center Core T ransmission Mobile Switching Base Station 0% 10% 20% 30% 40% 50% 60% Power Usage (%) (a) Power consumption of a typical wireless cellular network [2](ref. therein) Power ampliﬁer incl. feeder 50-80% (65%) Air conditioning 10-25% (17.5%) Signal Processing (analogue+digital) 5-15% (10%) Power Supply 5-10% (7.5%) (b) Power consumption distribution in radio base stations [3](ref. therein) Fig. 2. Breakdown of power consumption in a typical cellular network and corresponding base stations wireless networks and thereby to maintain proﬁtability , it is crucial to consider various paradigm-shifting technologies, such as energy efﬁcient wireless architectures and protocols, efﬁcient BS redesign, smart grids, opportunistic network ac- cess or cognitive radio, cooperativ e relaying and heterogenous network deployment based on smaller cells. Among all the promising energy saving techniques, cogni- tiv e radio and cooperati ve relaying, although already getting matured in many aspects, but still are in their infancy when it comes to the deployment issues in cellular networks. There- fore, it is crucial to promote the potentials of these techniques in cellular wireless networks. Moreover , it is necessary to be aware that still many energy concerns in cognitiv e and cooperativ e networks have remained as unanswered chal- lenges, which raises the importance of further exploring these concerns. In this paper , we provide a brief survey on some of the work that has already been done to achiev e po wer efﬁcienc y in cellular networks, discuss some research issues and challenges and suggest some techniques to enable an energy efﬁcient or “green” cellular network. W e also put a special emphasis on cogniti ve and cooperativ e techniques, in order to bring attention to the beneﬁts cellular systems can gain through employing such techniques, and also highlight the research av enues in making these techniques green. A taxonomy graph of our approach tow ards the design of green cellular networks is gi ven in Fig. 3. As shown in the ﬁgure, we identify four im- portant aspects of a green networking where we would like to focus: deﬁning green metrics, bringing architectural changes in base stations, netw ork planning, and efﬁcient system design. In addition, some broader perspectiv es must also be considered. In the following sections we elaborate on each such aspect and discuss the related issues and challenges. W e begin with a brief discussion on energy efﬁciency metrics in section II. Since BSs consume the major chunk of input energy , we discuss the energy efﬁcienc y of BSs more at the component le vel in sec- tion III. Here, we study how to minimize energy consumption of BS employing improvements in po wer ampliﬁer , designing power saving protocols, implementing cooperative BS power management, using rene wable energy resources and bringing some simple architectural changes. Section IV addresses the energy ef ﬁciency from a network planning perspecti ve where we discuss how different types of network deployments based on smaller cells can be used to increase the energy efﬁcienc y of a wireless system. Regarding the system design, we ﬁrst explain the use of modern communication technologies such as cognitive radio and cooperative relays to enable green communication in cellular systems in section V and we expand this idea further in section VI from a different perspecti ve, where we discuss ho w the future wireless systems based on both cogniti ve and cooperative concepts can be made more energy efﬁcient at the system lev el. T echniques such as low energy spectrum sensing, ener gy-aw are medium access control and routing, efﬁcient resource management, cross-layer design and addressing uncertainty issues hav e been examined in this context. Some broader perspectiv es ha ve been discussed in VII and conclusions are drawn in VIII. I I . M E A S U R I N G G R E E N N E S S : T H E M E T R I C S Before starting any discussion on “green” networks, the ﬁrst question naturally comes to mind is that what actually is “green”? How do we measure and deﬁne the degree of “greenness” in telecommunication networks? Although carbon footprint or CO 2 emissions would naturally be considered a measure of “greenness”, b ut the share of carbon emissions for telecommunication networks is fairly low (less than 1%). Howe ver , please note that other motiv ations to obtain “green” wireless technology also include economic beneﬁts (lo wer energy costs) and better practical usage (increased battery life in mobile de vices), hence ev aluation of energy savings or measuring energy efﬁcienc y seems to be a more apt choice for measuring “greenness”. Thus, the notion of “green” technology in wireless systems can be made meaningful with a comprehensiv e ev aluation of energy savings and performance in a practical system. This is where energy efﬁcienc y metrics play an important role. These metrics provide information in order to directly compare and assess the energy consumption of v arious components and the overall network. In addition, 3 Green Cellular Networks • Minimizing BS energy consumption o Improvements in Power Ampliﬁer o Power Saving Protocols • Energy-A ware Cooperative BSs o Network self-organizing techniques o Cell zooming • Using Renewable Energy Resources o Sustainable biofuels o Solar ener gy o W ind ener gy • Other ways to reduce BS power usage o Reducing the number of BSs o Ar chitectural changes in BSs Energy Savings in Base Stations Ar chitectur e • Green Comm. via Cognitive Radio • Green Comm. via Cooperative Relays o Fixed relays o User cooperation Enabling T echnologies • Low Energy Spectrum Sensing • Energy-A ware MAC & Green Routing • Energy-Efﬁcient Resource Management • Cross-Layer Design & Optimization • Uncertainty Issues Energy Efﬁciency in Futur e Generation W ireless Systems System Design • Statistical Power Proﬁles • Smart Grids • Embodied Energy vs. Operating Energy Br oader Perspectives • Facility-level Metrics • Equipment-level Metrics • Network-level Metrics Measuring Greenness Gr een Metrics • Macro-cells • Micro-cells • Pico-cells Network Planning Heterogenous Networks Fig. 3. T echnical roadmap for Green Cellular Networks: A taxonomy graph they also help us to set long term research goals of reducing energy consumption. W ith the increase in research activities pertaining to green communications and hence in number of div erse energy ef ﬁciency metrics, standards organizations such as European T echnical Standards Institute (ETSI) and Alliance for T elecommunications Industry Solutions (A TIS) are currently making efforts to deﬁne ener gy ef ﬁciency metrics for wireless networks [10], [11]. Generally speaking, energy efﬁcienc y metrics of telecom- munication systems can be classiﬁed into three main cate- gories: facility-level , equipment-level and network-level met- rics [12], [13]. Facility-le v el metrics relates to high-lev el systems where equipment is deployed (such as datacenters, ISP networks etc.), equipment level metrics are deﬁned to ev aluate performance of an individual equipment, and network lev el metrics assess the performance of equipments while also considering features and properties related to capacity and cov erage of the network. The Green Grid (TGG) association of IT professionals ﬁrst proposed facility-le v el ef ﬁciency metrics called PUE (Power Usage Efﬁciency) and its reciprocal DCE (Data Center Ef ﬁ- ciency) in [14] to ev aluate the performance of power hogging datacenters. PUE which is deﬁned as the ratio of total facility power consumption to total equipment power consumption, is although a good metric to quickly assess the performance of datacenters at a macro lev el, it fails to account for energy efﬁcienc y of individual equipments. Therefore, in order to quantify efﬁcienc y at the equipment lev el, ratio of energy consumption to some performance measure of a communica- tion system would be more appropriate. Ho we ver , grading the performance of a communication system is more challenging than it actually ﬁrst appears, because the performance comes in a v ariety of different forms (spectral efﬁciency , number of calls supported in block of time, etc.) and each such performance measure af fects this efﬁciency metric very differently . Some suggested metrics including power per user (ratio of total facility power to number of users) measured in [W att/user], and energy consumption rating (ECR) which is the ratio of normalized energy consumption to effecti v e full-duplex throughput and is measured in [W att/Gbps] [15]. While po wer per user can be a useful metric for a network provider to ev aluate economic tradeoffs, network planning etc., metrics such as ECR provide the manufacturers a better insight into performance of hardware components. Howe ver , even the busiest netw orks do not always operate on full load conditions, therefore it would be useful to complement metrics such as ECR to incorporate the dynamic network conditions such as energy consumption under full-load, half-load and idle cases. In this regard, other metrics such as ECR W (ECR-weighted), ECR-VL (energy efﬁciency metric over a variable-load cycle), ECR-EX (energy efﬁcienc y metric over extended-idle load cycle), telecommunications energy efﬁcienc y ratio (TEER) by A TIS, T elecommunication Equipment Energy Efﬁcienc y Rating (TEEER) by V erizons Networks and Building Systems consider total energy consumption as weighted sum of energy consumption of the equipment at different load conditions [15], [16], [17], [18]. As an example for TEEER, the total power consumption P total is calculated by the following for - 4 mula: P total = 0 . 35 P max + 0 . 4 P 50 + 0 . 25 P sleep , (1) where P max , P 50 and P sleep are power consumption at full rate, half-rate and sleep mode, respectively , and the weights are obtained statistically . Howe v er , these metrics such as ECR, TEER, TEEER etc. are unable to capture all the properties of a system and research work is still activ e to suggest different types of metrics. Park er et al. recently proposed an absolute energy efﬁcienc y metric (measured in dB ε ) in [19], giv en by: dB ε = 10 log 10  Power/Bit Rate k T ln 2  , (2) where k is the Boltzmann constant and T is the absolute temperature of medium. The authors suggest that the inclu- sion of temperature aspect of the system is logical since classical thermodynamics is based on absolute temperature of the system under analysis. Using dif ferent examples, the authors contend that this metric is highly versatile and can be univ ersally applied to any ICT system, subsystem and component. While the energy efﬁcienc y metrics at the component and equipment lev el are fairly straightforward to deﬁne, it is more challenging to deﬁne metrics at a system or network le vel [13]. Just by including the area aspect of the network, a natural choice of a metric may at ﬁrst seem to be [W att/Gbps/km 2 ], but a careful analysis can explain that it can work counter to a “green” objecti ve [20]. Using a simple example of a typical network scenario, it has been shown in [20] that due to the path loss, such a metric can only be valid when applied to networks with similar number of sites in a giv en area. In [11], ETSI proposes two network lev el metrics for GSM systems based on load conditions. In rural areas, which are generally under low load conditions, the objecti ve is to reduce power consumption in a coverage region, hence the metric is given by: P I rural = T otal coverage area Power consumed at the site , (3) where P I rural bears the unit of [km 2 /W att], and denotes the network performance indicator in rural areas. Urban areas on the hand have higher traf ﬁc demand than rural areas, hence capacity is considered instead of coverage area. A common metric under such full load conditions is therefore given by: P I urban = N busyhour Power consumed at the site , (4) where, N busyhour is the number of users based on average busy hour trafﬁc demand by users and average BS busy hour trafﬁc, and P I urban (users/W att) is the network performance indicator in urban areas. T o summarize the discussion above, a non-exhaustiv e list of energy metrics is giv en in T able I. Interested readers can ﬁnd a more comprehensiv e taxonomy of green metrics in [12]. Due to the intrinsic difference and relev ance of various com- munication systems and performance measures, it is doubtful that one single metric can sufﬁce. Howe v er , in future, the “green” metrics must also consider deployment costs such as site construction and backhaul, and QoS requirements such as transmission delay etc. along with spectral efﬁcienc y in order to assess the true “greenness” of the system. Once a large consensus is reached on a small set of standard energy metrics in future, it will not only accelerate the research acti vities in green communications, but also help pav e the way to wards standardization. I I I . A R C H I T E C T U R E : E N E R G Y S A V I N G S I N B A S E S TA T I O N S Due to the rapidly gro wing demand for mobile commu- nication technology , the number of worldwide cellular BSs has increased from a few hundred thousands to many millions within a last couple of years. Such a substantial jump in the number of BSs that power a cellular network accounts for the sudden increase in greenhouse gases and pollution, in addition to higher energy costs to operate them. W ith the advent of data intensive cellular standards, power -consumption for each BS can increase upto 1,400 watts and energy costs per BS can reach to $3,200 per annum with a carbon footprint of 11 tons of CO 2 [21]. The radio network itself adds up to 80% of an operator’ s entire energy consumption. Therefore, BS equipment manufacturers hav e begun to offer a number of eco and cost friendly solutions to reduce power demands of BSs and to support off-grid BSs with rene wable energy resources. Nokia Siemens Networks Flexi Multiradio Base Station, Huawei Green Base Station and Flexenclosure E- site solutions are examples of such recent efforts [22], [23], [24]. In [25], the authors present various methods dealing with impro ved transmitter ef ﬁciency , system features, fresh air- cooling, renewable energy sources and energy saving during low trafﬁc. A typical cellular network consists of three main elements; a core network that takes care of switching, BSs providing radio frequency interface, and the mobile terminals in order to make voice or data connections. As the number of BSs increases, it becomes crucial to address their ener gy con- sumption for a cellular network. In the next few subsections, we will discuss dif ferent ways to reduce energy consumption due to BSs. A. Minimizing BS ener gy consumption The energy consumption of a typical BS can be reduced by improving the BS hardware design and by including additional software and system features to balance between energy consumption and performance. In order to improv e hardware design of a BS for energy consumption, we need to address the ener gy ef ﬁciency of the power ampliﬁer (P A). A P A dominates the energy consumption of a BS and its energy efﬁcienc y depends on the frequency band, modulation and operating en vironment [25]. Some typical system features to improv e BS energy efﬁcienc y are to shut down BS during low trafﬁc or cell zooming [26], [27]. Besides hardware redesign and new system lev el features, there are various site lev el solutions that can be used in order to save energy . F or example, outdoor sites can be used ov er wider lev el of temperatures, and thus less cooling would be required. Another solution is to use more fresh air-cooling rather than power consuming air conditioners for indoor sites. In addition, RF heads and modular BS design can be implemented to reduce power loss in feeder cables [25]. 5 T ABLE I S O ME E N ER G Y E FFI C IE N C Y M E TR I C S Metric T ype Units Description PUE (Power Usage Efﬁcienc y) Facility-Lev el Ratio ( ≥ 1) Deﬁned as ratio of total facility power consumption to total equipment power consumption. DCE (Data Center Efﬁciency) Facility-Le vel Percentage Deﬁned as reciprocal of PUE. T elecommunications Energy Efﬁcienc y Ratio (TEER) Equipment-Lev el Gbps/W att Ratio of useful work to power consumption T elecommunications Equipment Energy Efﬁciency Rating (TEEER) Equipment-Lev el − log  Gbps W att  − log  P total Throughput  , where P total is giv en by equation (1) Energy Consumption Rating (ECR) Equipment-Lev el W att/Gbps Ratio of energy consumption ov er effecti ve system capacity ECR-W eighted (ECR W) Equipment-Lev el W att/Gbps Calculated the same way as ECR except energy con- sumption is now calculated as 0 . 35 E f + 0 . 4 E h + 0 . 25 E i , where each term corresponds to energy con- sumption in full load, half load and idle modes. ECR-variable-load metric (ECR-VL) Equipment-Lev el W att/Gbps A verage ener gy rating in a reference network described by an array of utilization weights [16]. ECR-extended-idle metric (ECR-EX) Equipment-Lev el W att/Gbps A verage energy rating in a reference network, where extended energy savings capabilities are enabled [16]. Performance Indicator in rural areas ( P I rural ) Network-Le vel km 2 /W att Ratio of total coverage area to power consumed at site as giv en by eq. (3) Performance Indicator in urban areas ( P I urban ) Network-Le vel users/W att Ratio of number of subscribers to power consumed at the site as given by eq. (4) 1) Impr ovements in P ower Ampliﬁer: There are three es- sential parts of a BS: radio, baseband and feeder . Out of these three, radio consumes more than 80% of a BS’ s energy requirement, of which power ampliﬁer (P A) consumes almost 50% [28]. Shockingly , 80-90% of that is wasted as heat in the P A, and which in turn requires air-conditioners, adding ev en more to the energy costs. The total ef ﬁciency of a currently deployed ampliﬁer , which is the ratio of AC po wer input to generated RF output power , is generally in anywhere in the range from 5% to 20% (depending on the standard viz. GSM, UMTS, CDMA and the equipment’ s condition) [29]. Modern BSs are terribly inef ﬁcient because of their need for P A linearity and high peak-to-average power ratios (P APR). The modulation schemes that are used in communication standards such as WCDMA/HSP A and L TE are characterized by strongly v arying signal en velopes with P APR that exceeds 10dB. T o obtain high linearity of the P As in order to maintain the quality of radio signals, P As have to operate well below saturation, resulting in poor power ef ﬁciency [3]. Depending on their technology (e.g Class-AB with digital pre-distortion) and implementation, the component lev el efﬁcienc y of modern ampliﬁers for CDMA and UMTS systems is in the order of approximately 30% to 40% [29]. Since these technologies hav e reached their limits, P As based on special architectures such as digital pre-distorted Doherty-architectures and GaN (Aluminum Gallium nitride) based ampliﬁers seem to be more promising by pushing the power efﬁcienc y lev els to over 50% [29]. Doherty P As that consist of a carrier and a peak ampliﬁer is advantageous by providing easy additional linearization using con ventional methods such as feed-forward and en velope elimination and restoration (EER)[30]. Since GaN structures can work under higher temperature and higher voltage, they can potentially provide a higher po wer output. Additional improv ements in efﬁcienc y can be obtained by shifting to switch-mode P As from the traditional analog RF-ampliﬁers. Compared to standard analog P As, switch-mode P As tend to run cooler and draw less current. While amplifying a signal, a switch-mode ampliﬁer turns its output transistors on and off at an ultrasonic rate. The switching transistors produce no current when they are switched of f and produce no v oltage when switched on, therefore generate very little power as heat resulting in a highly efﬁcient power supply . It is expected that ov erall component-efﬁcienc y of these energy efﬁcient devices could be around 70% [29]. One more signiﬁcant setback in increasing power efﬁcienc y with P As is that they perform better at maximum output power in order to maintain the required signal quality . Howe ver , during the low traf ﬁc load conditions (e.g night time), lot of energy is routinely wasted. Therefore, design of ﬂexible P A architectures that would allo w a better adaptation of the ampliﬁer to the required output po wer needs to be addressed [29]. In addition to this, we need to in vestigate more efﬁcient modulation schemes, because modulation also af fects the P A efﬁciency . As an example, by focusing more on higher modulation schemes that require additional ﬁltering in order to prioritize data over voice, linearity of P A is more desirable because of the non-constant env elope of the signal [28]. Using different linearization techniques such as Cartesian feedback, digital pre-distortion and feed-forward along with dif ferent kind of DSP methods that reduces the requirement on the linear area of P A hav e also been suggested [25]. 2) P ower Saving Pr otocols: In the current cellular network architecture based on WCDMA/HSP A, BSs and mobile ter - minals are required to continuously transmit pilot signals. Newer standards such as L TE, L TE-Advanced and WiMAX hav e evolv ed to cater ev er-gro wing high speed data trafﬁc requirements. With such high data requirements, although BSs and mobile units (MU) employing newer hardware (such as 6 multiple-input and multiple-output (MIMO) antennas) increase spectral efﬁcienc y allowing to transmit more data with the same power , power consumption is still a signiﬁcant issue for future high speed data networks and they require energy conservation both in the hardware circuitry and protocols. A fairly intuitiv e way to save power is to switch off the transceiv ers whenever there is no need to transmit or receiv e. The L TE standard utilizes this concept by introducing power saving protocols such as discontinuous reception (DRX) and discontinuous transmission (DTX) modes for the mobile hand- set. DRX and DTX are methods to momentarily power down the devices to save po wer while remaining connected to the network with reduced throughput. Continuous transmission and reception in WCDMA/HSP A consumes signiﬁcant amount of power even if the transmit powers are far below the maximum levels, and therefore po wer savings due to DRX and DTX is an attractiv e addition. IEEE 802.16e or Mobile W iMAX also has similar provisions for sleep mode mecha- nisms for mobile stations [31]. The device negotiates with the BS and the BS will not schedule the user for transmission or reception when the radio is off. There are three power -sa ving classes with different on/of f cycles for the W iMAX standard. Unfortunately , such power sa ving protocols for BSs have not been considered in the current wireless standards. The trafﬁc per hour in a cell varies considerably o ver the time and BSs can regularly be under low load conditions, especially during the nighttime. In future wireless standards, energy sa ving potential of BSs needs to be exploited by designing protocols to enable sleep modes in BSs. The authors in [3] suggest making use of downlink DTX schemes for BSs by enabling micro-sleep modes (in the order of milliseconds) and deep-sleep modes (extended periods of time). Switching of f inactive hardware of BSs during these sleep modes can potentially sav e a lot of power , especially under low load conditions. B. Energy-A ware Cooperative BS P ower Manag ement T raf ﬁc load in cellular networks hav e signiﬁcant ﬂuctuations in space and time due to a number of factors such as user mobility and behaviour . During daytime, trafﬁc load is generally higher in ofﬁce areas compared to residential areas, while it is the other way around during the night. Therefore, there will always be some cells under low load, while some others may be under heavy traf ﬁc load. Hence, a static cell size deployment is not optimal with ﬂuctuating traf ﬁc conditions. For next generation cellular networks based on microcells and picocells and femtocells, such ﬂuctuations can be very serious. While limited cell size adjustment called “cell-breathing” cur- rently happens in currently deployed CDMA networks (a cell under hea vy load or interference reduces its size through po wer control and the mobile user is handed of f to the neighbouring cells), a more network-lev el power management is required where multiple BSs coordinate together . Since operating a BS consumes a considerable amount of energy , selectively letting BSs go to sleep based on their trafﬁc load can lead to signiﬁcant amount of energy savings. When some cells are switched of f or in sleep mode, the radio co verage can be guaranteed by the remaining active cells by ﬁlling in the gaps created. Such concepts of self-organizing networks (SON) hav e been introduced in 3GPP standard (3GPP TS 32.521) to add network management and intelligence features so that the network is able to optimize, reconﬁgure and heal itself in order to reduce costs and improv e network performance and ﬂexibility [32]. The concept of SONs can be applied in order to achieve div erse objectiv es. For instance, in [33] different use cases for SONs are discussed, e.g., load balancing, cell outage management, management of relays and repeaters, etc. In the context of power ef ﬁciency , the performance of these self-organizing techniques were initially explored in [27], [34]. Using the numerical results, the authors here suggested that substantial amount of energy savings can be obtained (of the order of 20%, and above) by selectiv ely reducing the number of activ e cells that are under low load conditions. On the other hand, a distributed algorithm is proposed in [35] in which BSs exchange information about their current lev el of power and take turns in reducing their powers. Recently , authors of [36], [37] introduced the notion of energy partitions which is the associations among powered-on and powered-of f BSs, and use this notion as the basis of rearranging the energy conﬁguration. A similar but even more ﬂexible concept called “Cell Zooming” was presented in [26]. Cell zooming is a technique through which BSs can adjust the cell size according to network or trafﬁc situation, in order to balance the trafﬁc load, while reducing the energy consumption. When a cell gets congested with increased number of users, it can zoom itself in, whereas the neighboring cells with less amount of trafﬁc can zoom out to cover those users that cannot be served by the congested cell. Cells that are unable to zoom in may ev en go to sleep to reduce energy consumption, while the neighboring cells can zoom out and help serv e the mobile users cooperativ ely . Another such proposal to dynamically adjust cell-size in a multi-layer cellular architecture was presented in [38]. 1) Implementation: The framework for cell zooming can include a cell-zooming server (CS) (implemented in the gate- way or distributed in the BSs) that senses the network state information such as trafﬁc, channel quality etc [26] and hence makes decisions for for cell zooming. If there is a need for a cell to zoom in or out, it will coordinate with its neighboring cells by the assistance of a CS. Cells can zoom in or out by a variety of techniques such as physical adjustment, BS cooperation and relaying [26]. Physical adjustment can be either done by adjusting the transmit powers of BSs and also by adjusting antenna height and tilt for cells to zoom in or out. BS cooperation here means that multiple BSs cooperatively transmit or receiv e from MUs. For an MU, a cluster of BSs cooperating form a new cell, the size of which is sum of cell sizes of these BSs. Relaying can also be used for cell size adjustment in a way that relay stations can help transfer the trafﬁc from a cell with hea vy load to a cell in low load conditions [26], [38]. The authors in [38] propose dynamic self-organization of the cellular layers using techniques such as timed sleep mode, user location prediction and rev erse channel sensing. In such networks, BS can also go to sleep mode where the energy consuming equipments such as air conditioner etc., can be switched off. The neighboring cells 7 can then reconﬁgure to guarantee the coverage. 2) Beneﬁts and Challenges: Self-organizing cellular net- works can be useful in load balancing as well as energy conservation by deciding when to disperse load for load balancing and when to concentrate load for energy savings. The advantages of techniques such as cell zooming also include improv ed user experience such as better throughput and increased battery life. For e.g in [38], a two-layer cellular architecture achiev es a power savings of up to 40% ov er the entire day . W ith techniques such as BS cooperation and relaying, inter-cell interference and fading ef fects can be mitigated and hence MUs can observe higher di versity gains and better cov erage. Howe ver , sufﬁcient challenges lay ahead to practically realize these networks such as radio frequency planning, conﬁguring switching thresholds, av oiding coverage holes, tracing spatial and temporal traf ﬁc load ﬂuctuations etc. [26], [38]. C. Using Renewable Energy Resources In several remote locations of the world such as Africa and Northern Canada, electrical grids are not a v ailable or are unreliable. Cellular network operators in these off-grid sites constantly rely on diesel po wered generators to run BSs which is not only expensiv e, but also generates CO 2 emissions. One such generator consumes an a verage of 1500 litres of diesel per month, resulting in a cost of approximately $30,000 per year to the network operator . Moreov er , this fuel has to be physically brought to the site and sometimes it is e ven transported by helicopter in remote places, which adds further to this cost. In such places, renew able energy resources such as sustainable biofuels, solar and wind energy seem to be more viable options to reduce the overall network expenditure. Hence, adopting re- new able energy resources could save cellular companies such recurrent costs, since they are capital intensiv e and cheaper to maintain. Also, since renewable energy is derived from resources that are regenerativ e, renew able energy resources do not generate greenhouse gases such as CO 2 . Recently , a program called “Green Power for Mobile” to use renew able energy resources for BSs has been started by 25 leading telecoms including MTN Uganda and Zain, united under the Global Systems for Mobile communications Association (GSMA) [39]. This program is meant to aid the mobile industry to deploy solar , wind, or sustainable biofuels technologies to po wer 118,000 new and existing off-grid BSs in developing countries by 2012. Powering that many BSs on renew able energy would save up to 2.5 billion litres of diesel per annum (0.35% of global diesel consumption of 700 billion litres per annum) and cut annual carbon emissions by up to 6.8 million tonnes. Such BSs operating on rene wable energy resources are expensi v e and network operators have been reluctant to adopt them because of fear of little commercial viability and lack of equipment expertise. Howe ver , according to a bi-annual recent report by GSMA, the implementation of green power technology represents a technically feasible and ﬁnancially attractiv e solution with a payback period of less than three years at many sites [40]. D. Other ways to r educe BS power consumption Since the energy consumption of the entire cellular network includes the summation of energy used by each BS, reducing the number of BSs has a direct impact on energy consumption of a cellular network. Howe ver , ef ﬁcient network design and ﬁnding an optimal balance between cell size and BS capacity can be very challenging. Features such as 2-way and 4-way div ersity , feeder less site, extended cell, low frequency band, 6-sector site and smart antenna can be used to minimize the number of BS sites [25]. Another way to impro ve power ef ﬁciency of a BS is to bring some architectural changes to the BS. Currently , the connection between the RF-transmitter and antenna is done by long coaxial cables that add almost 3dB to the losses in po wer transmission and therefore, lo w power RF-cables should be used and RF-ampliﬁer has to be kept closer to the antenna [29]. This will improv e the ef ﬁciency and reliability of the BS. In [41], the authors suggest an all-digital transmitter architecture for green BS that uses a combination of EER and pulse width modulation (PWM)/pulse position modulation (PPM) modulation. I V . N E T W O R K P L A N N I N G : H E T E R O G E N E O U S N E T W O R K D E P L OY M E N T The exponential growth in demand for higher data rates and other services in wireless networks requires a more dense deployment of base stations within network cells. Whereas con v entional macro-cellular network deployments are less ef- ﬁcient, it may not be economically feasible to modify the cur- rent network architectures. Macrocells are generally designed to provide large cov erage and are not efﬁcient in providing high data rates. One ob vious w ay to make the cellular networks more power efﬁcient in order to sustain high speed data-trafﬁc is by decreasing the propagation distance between nodes, hence reducing the transmission power . Therefore, cellular network deployment solutions based on smaller cells such as micro, pico and femtocells are very promising in this context. A typical heterogeneous network deployment is shown in Fig. 4. A micro/picocell is a cell in a mobile phone network served by a lo w power cellular BS that covers a small area with dense trafﬁc such as a shopping mall, residential areas, a hotel, or a train station. While a typical range of a micro/picocell is in the order of few hundred metres, femtocells are designed to serve much smaller areas such as priv ate homes or indoor areas. The range of femtocells is typically only a fe w metres and they are generally wired to a pri v ate owners’ cable broadband connection or a home digital subscriber line (DSL). Smaller cells because of their size are much more po wer efﬁcient in providing broadband co verage. As an example, a typical femtocell might only have a 100mW P A, and draw 5W total compared to a 5KW that would be needed to support macrocell. An analysis by OFCOM (UK regulator) and Ple xtek concluded that femtocell deplo yment could have a 7:1 operational energy advantage ratio over the expansion of the macrocell network to provide approximately similar indoor cov erage [42]. Simulations show that with only 20% of customers with picocells, a joint deployment of macrocell and 8 I n d o o r - F e m t o c e l l M a c r o - M a c r o P i c o - P i c o M a c r o - F e m t o M a c r o - M i c r o M a c r o - P i c o M a c r o - F e m t o M i c r o - F e m t o I n d o o r - F e m t o c e l l P i c o - M i c r o - C o v e r a g e a r e a o f M a c r o c e l l - C o v e r a g e a r e a o f M i c r o c e l l - C o v e r a g e a r e a o f P i c o c e l l Fig. 4. A typical heterogeneous network deployment picocell in a network can reduce the energy consumption of the network by up to 60% compared to a network with macro- cells only [29]. Another adv antage of smaller cells is that they can use higher frequency bands suitable to pro vide high data rates and also of fer localization of radio transmissions. Howe ver , deploying too many smaller cells within a macrocell may reduce the o verall efﬁciency of the macrocell BS, since it will ha ve to operate under low load conditions. Therefore, careful inv estigation of various deployment strategies should be done in order to ﬁnd ho w to best deploy such smaller cells. In [43], Calin et al. provided insight into possible architectures/scenarios for joint deployments of macro and femtocells with an analysis framew ork for quantifying poten- tial macro-of ﬂoading beneﬁts in realistic network scenarios. Richter et al. in [44], in vestig ate the impact of different deployment strategies on the power consumption of mobile communication network. Considering layouts with different number of micro BSs in a cell, in addition to macro sites, the authors introduce the concept of area power consumption as a system performance metric. Simulation results suggest that under full trafﬁc load scenarios, the use of micro BSs has a rather moderate effect on the area power consumption of a cellular network and strongly depends on the offset power consumption of both the macro and micro sites [44]. In [45], the authors in v estigate the potential improvements of the same metric achiev able in network layouts with different numbers of micro BSs together with macro sites for a given system performance targets under full load conditions. As large-scale femtocell deployment can result in signiﬁcant energy consumption, an energy saving procedure that allows femtocell BS to completely turn off its transmissions and processing when not in volved in an acti v e call was proposed in [46]. Depending on the voice trafﬁc model, this mechanism can provide an average po wer sa ving of 37.5% and for a high trafﬁc scenario, it can achieve ﬁv e times reduction in the occurrence of mobility events, compared to a ﬁxed pilot trans- mission [46]. A rather radial approach to create a link between fully centralized (cellular) and decentralized (ad hoc) networks in order to achiev e more efﬁcient network deployment is a paradigm shift to wards self-organizing small-cell networks (SCNs) [47]. Howe ver , coverage and performance prediction, interference and mobility management together with security issues are some of the many issues that must be dealt while designing such networks. V . E N A B L I N G T E C H N O L O G I E S : C O G N I T I V E R A D I O A N D C O O P E R A T I V E R E L AYI N G Recently , the research on technologies such as cognitiv e radio and cooperativ e relaying has received a signiﬁcant attention by both industry and academia. While cognitiv e radio is an intelligent and adaptiv e wireless communication system that enables us to utilize the radio spectrum in a more efﬁcient manner , cooperativ e relays can provide a lot of improv ement in throughput and cov erage for futuristic wireless networks. Ho we ver , dev elopments in both these technologies also enable us to solve the problem of energy ef ﬁciency via smart radio transmission and distributed signal processing. In the following subsections, we will discuss how we can enable green communication in cellular systems using cognitive radio and cooperativ e relaying. A. Green Communication via Cognitive Radio Bandwidth efﬁciency has been always a crucial concern for wireless communication engineers, and there exist a rich liter- 9 ature on this matter , resulting in bandwidth efﬁcient systems, but not always considering power efﬁcienc y . On the other hand, it has been realized that the allocated spectrum is highly underutilized [48], and this is where cogniti ve radio comes into the picture. The main purpose of cogniti ve radio is to collect information on the spectrum usage and to try to access the unused frequency bands intelligently , in order to compensate for this spectrum underutilization [49]. Ho wev er , the question is why using spectrum more efﬁciently is important and how it can reduce po wer consumption? The answer lies under Shannon’ s capacity formula [50], where we can see the trade- off between the bandwidth and po wer . The capacity increases linearly with bandwidth, but only logarithmically with power . This means that in order to reduce power , we should seek for more bandwidth [51], or in other words, manage the spectrum optimally and dynamically , and this falls into the scope of cognitiv e radio. In fact, it has been shown in [52] that up to 50% of power can be saved if the operator dynamically manages its spectrum by acti vities such as dynamically moving users into particularly active bands from other bands, or the sharing of spectrum to allow channel bandwidths to be increased. Howe ver , ef ﬁcient spectrum usage is not the only concern of cognitive radio. Actually , in the original deﬁnition of cognitiv e radio by J. Mitola [53], ev ery possible parameter measurable by a wireless node or network is taken into account (Cognition) so that the network intelligently modiﬁes its functionality (Reconﬁgurability) to meet a certain objective. One of these objectiv es can be po wer saving. It has been sho wn in recent works that structures and techniques based on cogni- tiv e radio reduce the energy consumption, while maintaining the required quality-of-service (QoS), under various channel conditions [54], [55]. Ne vertheless, due to the complexity of these proposed algorithms, still vendors ﬁnd it unappealing to implement these techniques. Hence, a roadway to future would be striving for more feasible, less complex, and less expensiv e schemes within the scope of cogniti ve radio. B. Cooperative Relays to deliver gr een communication In infra-structured wireless networks, extending coverage of a BS is an important issue. Considering well-known properties of the wireless channel, including large path losses, shadowing effects and different types of signal fading, covering very distant users via direct transmission becomes very expensi ve in terms of required power in order to establish a reliable connection. This high-power transmission requirement further translates into the high power consumption and also introduces high lev els of interface at nearby users and BSs. On the other hand, in recent years, cooperativ e commu- nication techniques have been proposed to create a virtual MIMO systems, where installing large antennas on small devices such as MUs is not possible. Hence, using cooperativ e communication, well-known improvements of MIMO systems including cov erage enlar ging and capacity enhancement can be achiev ed [56]. Cooperative techniques also combat shadowing by covering cov erage wholes [56]. In fact, early research has shown that relaying techniques extend the battery life [57], which is the ﬁrst step tow ards energy efﬁcient networks. In particular, multi-hop communication divides a direct path between mobile terminals and BS into sev eral shorter links [58], in which wireless channel impairments such as path loss are less destructive, hence lo wer transmission power can be assigned to the BS and relays. Authors in [59] mentioned that two-hop communication consumes less energy than direct communication. And ﬁnally , it has been shown in [60] that using multi-hopping in CDMA cellular networks can reduce the av erage energy consumed per call. Deliv ering green communication via cooperativ e techniques can be achieved by two different approaches. The ﬁrst ap- proach is to install ﬁxed relays within the network cov erage area in order to provide service to more users using less power . And the second approach is to exploit the users to act as relays. In this work, a relay is roughly deﬁned as one of the network elements which can be ﬁxed or mobile, much more sophisticated than a repeater, and it has capabilities such as storing and forw arding data, and cooperating in scheduling and routing procedures. While the second scenario eliminates the cost of installing relay nodes, it increases the complexity of the system, mostly because centralized or distributed algorithms must be designed to dynamically select relays among the users, as well as new user mobile terminals hav e to be designed such that they support relaying. In the two following sub-sections, we discuss these two scenarios. 1) Enabling Green Communication via F ixed Relays: Non- linear signal attenuation or path loss is an interesting property of a wireless channel. This property helps to concentrate power on speciﬁc locations in a network, hence, leads to spatial reuse of v arious resources within a wireless network. A simple example in [61] shows that for an additi ve white Gaussian noise (A WGN) channel with a path loss exponent of 4, we can increase the number of BSs by a factor of 1.5 in an area unit, and reduce the transmitting power by a factor of 5, while achieving a same signal-to-noise ratio (SNR) le vel. In other words, a higher density of BSs leads to less ener gy consumption as well as a higher special reuse [61]. In fact, this is the key point which makes ﬁxed relays a good candidate for deliv ering green communication as well as a general improv ement of netw ork performance. Installing new BSs in order to have a higher BS density can be very expensi v e. Therefore, we can install relays instead of new BSs, which is economically advantageous, and does not introduce much complexity to the network. First of all, relays need not be as high as BSs, because they are supposed to cover a smaller area with a lower power [56]. Secondly , relays can be wirelessly connected to a BS, instead of being attached to the backhaul of the network by wire using a complicated interface [56]. And ﬁnally , in cellular systems, unlike ad-hoc and peer-to-peer networks, complex routing algorithms are not necessary [56]. All these reasons make installing relays a potential solution to having more energy efﬁcient cellular networks. In a very recent work [61], the authors hav e discussed how it is possible to deliv er a green communication structure in cellular networks, using ﬁxed relays. In this paper , It is shown that relays provide a ﬂexible way to improve the spatial reuse, 10 are less complex than BSs and therefore cheaper to deploy , and the relays reduce the power in the system compared to systems based on direct transmission. 2) Green Communications in Cellular Networks via User Cooperation: User cooperation was ﬁrst introduced in [62], and has been shown that not only it increases the data rate, but also the system is more robust, i.e., the achiev able rates are less sensitive to channel variations. Howe ver , despite all these advantages, energy efﬁcienc y issues of user cooperation render this paradigm unappealing in wireless mobile networks. The reason is, increased rate of one user comes at the price of the energy consumed by another user acting as a relay . The limited battery life time of mobile users in a mobile network leads to selﬁsh users who do not ha ve incentiv e to cooperate. In fact, in a very recent work by Nokleby and Aazhang [63], this fundamental question has been posed: whether or not user cooperation is advantageous from the perspective of energy efﬁcienc y . In this paper , a game-theoretic approach is proposed to giv e users incentiv e to act as relays when they are idle, and it is sho wn that user cooperation has the potential of simultaneously improving both user’ s bits-per- energy efﬁciency under dif ferent channel conditions. User cooperation in which selﬁsh users ﬁnd cooperation fa vourable to their energy concerns, has recently been consid- ered, but has still not attracted much research. Howe ver , based on existing literature, this new approach can be a promising technique to increase the system performance in terms of energy efﬁciency in future wireless mobile networks. V I . D E S I G N : A D D R E S S I N G E N E R G Y E FFI C I E N C Y I N F U T U R E G E N E R A T I O N W I R E L E S S S Y S T E M S In previous sections, we discussed that ho w cognitiv e radio and cooperativ e communication are becoming key technolo- gies to address the power efﬁcienc y of a cellular network. As we mentioned earlier , European Union has already started C2PO WER project with objecti ves to reduce power con- sumption of mobile terminals using cognitiv e and cooperative technologies by up to 50%. In this section, we will mainly discuss techniques to enable green communication in future generation of wireless systems that will rely on cooperation and cognition to meet the increasing demand for high data rates. So far , achie ving high data rate has been the primary focus of research in cooperative and cognitive radio systems, without much consideration of energy ef ﬁciency . Howe v er , many of these techniques signiﬁcantly increase system com- plexity and energy consumption. For instance, in the context of green communication via cogniti ve radio, authors of [64] mention that there are two fundamental but entangled aspects: how to use cognitiv e radio for energy efﬁcienc y purposes, and ho w to make the cogniti ve radio operate in an energy efﬁcient manner . Escalating energy costs and environmental concerns have already created an urgent need for more energy- efﬁcient “green” wireless communication. Hence, we need to be proactiv e in designing energy-efﬁcient solutions for cooper- ativ e and cognitive networks, which will potentially drive the future generation of wireless communication. As an example, if cogniti ve and cooperativ e techniques are expected to giv e 50% of power sa vings, then an additional 50% improvement in the energy ef ﬁciency of these techniques will further increase the net savings by 25%. In the next few subsections, we will discuss an approach to obtain energy ef ﬁciency of cellular networks on an algorithmic and protocol design lev el, instead of energy-efﬁcient circuitry design for communication devices. A. Low-Energy Spectrum Sensing The use of cognitive radio technology requires frequent sensing of the radio spectrum and processing of the sensor data which would require additional power . Therefore, it is necessary to design energy-ef ﬁcient sensing schemes so that improv ement in data rate due to opportunistically acquired spectrum does not lead to signiﬁcant increase in the energy consumption. Low-comple xity spectrum sensing techniques such as energy detection require high sensing time to ac- curately detect a primary signal and e ven fail to detect the signal at lo w SNR due to presence of noise-uncertainty [65]. Therefore, detectors exploiting the cyclostationarity of the primary signals have been studied in the literature that perform better at lo w SNR. Howe ver , they are highly complex and need signiﬁcant processing po wer . Therefore, design of low- complexity cyclostationary detectors needs to be in vestig ated. Cooperativ e spectrum sensing improv es the sensing perfor- mance by using the spatial di versity between v arious sensors [65]. Howe v er , cooperativ e sensing would also increase the signaling overhead and thus, energy consumption. By taking into consideration the power consumed for sensing, processing and transmitting sensing data, we need to ﬁnd conditions under which cooperativ e sensing is more energy efﬁcient in order to achiev e a certain sensing performance. New strategies should be designed to select the sensors to participate in cooperativ e sensing that could reduce the power consumed without se vere loss in the sensing performance. Also, optimal location of the sensors should be determined that would make the sensing system energy efﬁcient in presence of a single and multiple primary users. Cluster-based sensing architecture has been sho wn to achiev e higher ener gy efﬁcienc y and hence cluster-based de- signs to reduce po wer consumption should be considered in research [66]. Sequential detection techniques also needs to be explored to improve the energy ef ﬁciency of the system [67]. Compressiv e sensing has recently been proposed to reduce the complexity of wide-band sensing by sampling at a rate signiﬁcantly lower than Nyquist rate, taking advantage of the sparse nature of the radio spectrum usage [68]. Therefore, efﬁcient cooperative compressive spectrum sensing schemes is also a possible research area. B. Energy-A ware Medium Access Contr ol and Gr een Routing Medium access control (MA C) in cooperativ e and cognitiv e wireless systems introduces a number of ne w challenges un- seen in traditional wireless systems. For example, coordinating medium access in presence of multiple relays with different channel qualities requires a much more agile and adapti ve 11 MA C in cooperativ e systems. In cognitiv e radio systems, sens- ing accuracy , duration and time varying av ailability of primary user channels are some of the factors affecting the MA C design. The need for optimizing energy consumption further adds another dimension that can be conﬂicting to the goal of achieving better system performance, user satisfaction and QoS. Many of the cooperative and cognitiv e wireless systems will rely on multihop communication between a transmitter and its intended receiver . In addition to MA C design, proper routing schemes will thus be necessary to achiev e desired end- to-end QoS. Although, a number of MAC and routing schemes special- ized for cooperative and cogniti v e networks exist in the liter- ature [69], [70], little research has been done to reg arding the energy efﬁciency of such systems. For instance, a signiﬁcant volume of research e xists on joint routing and spectrum alloca- tion with objectiv es of throughput maximization in multi-hop cognitiv e and cooperative systems. In [71], decentralized and localized algorithms for joint dynamic routing, relay assign- ment, and spectrum allocation in a distributed and dynamic en vironment are proposed and analyzed. Ho we ver , most of the research on joint routing and spectrum allocation does not take into account power efﬁciency constraints directly . Nev er - theless, throughput maximization via routing-driv en spectrum allocation can be interpreted as power efﬁciency , since more throughput is achieved using the same amount of power . As an another example of MA C and routing schemes specialized for cooperative and cogniti ve networks, in [72], Alonso-Zarate et al. proposed persistent relay carrier sens- ing multiple access (PRCSMA) MA C protocol employing distributed cooperative automatic retransmission request (C- ARQ) scheme (users who overheard the message can act as spontaneous relays for retransmission) in IEEE 802.11 wireless networks and in [73], they recently ev aluated the energy consumption of this protocol. In particular, Alonso- Zarate et al. described the conditions under which a C-ARQ scheme with PRCSMA outperforms non-cooperative ARQ schemes in terms of energy efﬁciency . On the other hand, some ener gy-aw are MAC and routing mechanisms [74], [75] exist primarily for wireless sensor networks. Howev er , sensor networks are very different than cooperativ e and cogniti ve networks in system dynamics and performance objectiv es. Therefore for cellular networks, objectiv e should be to inv esti- gate novel ener gy-efﬁcient MAC and routing schemes design for cooperativ e and cognitiv e wireless networks. In addition, we need to focus on optimizing energy consumption while deliv ering desired system performance, user satisfaction and QoS. Hybrid-ARQ (HARQ) are another set of ARQ type pro- tocols that use Forward-Error -Correction (FEC) coding and can be typically employed at the MA C layer to improve QoS and rob ustness for delay insensitiv e applications. There are three important subclasses of HARQ protocols namely: HARQ-IT (T ype I, in which erroneous data packets are retransmitted for memoryless detection), HARQ-CC (chase combining, where packets in error are preserved for soft combining), and HARQ-IR (Incremental Redundancy , where ev ery retransmission contains dif ferent information bits than previous one). HARQ protocols can potentially reduce the transmission energy required for decoding at the destination for delay insensitiv e systems and the total energy consumption for both the transmission po wer and the energy consumed in the electronic circuitry of all inv olved terminals (source, destination and, ev en relays) has been studied in [76]. Hence, future MA C protocols for cogniti ve and cooperativ e systems that employ HARQ has potential to reduce energy costs of such systems. In cooperativ e systems, the medium time is accessed for both direct and relayed transmissions. Addition of relayed transmission means more po wer consumption in the network. Future research on developing a MA C protocol that will be able to suitably quantify potential performance gain in QoS against any additional energy consumption and coordinate medium access among direct and relayed transmissions, will be important. Also, while doing so, focus should be on lo w- complexity schemes so that the energy savings acquired are not wasted in an increased need for processing power . For routing in a multihop cooperative system, we need to employ new pro- tocols that can intelligently use the most energy-ef ﬁcient path giv en the relays that are selected by the resource allocation and MA C schemes. In order to facilitate the operation of our targeted mechanisms, we must e xplore analytical models that can quantify trade-offs between energy savings and end-to-end QoS performance from selecting alternate routing paths. In this regard, there has already been a paradigm shift from early ﬂooding-based and hierarchical protocols to geographic and self-organizing coordinate-based routing solutions and Internet Engineering T ask Force (IETF) Routing Over Low power and Lossy networks (ROLL) w orking group is in process of standardization of Routing Protocol for Low power and lossy networks (RPL) [77], [78]. For cognitive networks, energy efﬁcienc y in the MA C can be increased signiﬁcantly if the access mechanism is designed to av oid collisions between primary and secondary users. Existing random access based protocols must be modiﬁed to achiev e this objecti ve in a distributed cognitive MAC with as lo w system complexity as possible. Statistical information of a v ailable channels can be used for QoS provisioning such as in [79] but we should also consider energy efﬁciency as a trade-of f. Furthermore, we must focus on developing analytical models to relate important parameters of these random access methods to resulting energy consumption and QoS performance. This will enable the system engineers to choose optimal parameter values to minimize energy con- sumption while satisfying desired QoS performance. In a multihop cognitive radio network, due to the presence of primary user spectrum, more energy efﬁcient routes can now be selected which would not be av ailable without cognitive technology . Routing algorithms should be designed such that they can utilize these additional routes to minimize energy consumption. C. Energy-Ef ﬁcient Resour ce Management with Applications in Heter og eneous Networks Energy consumption in wireless networks is closely related to their radio resource management schemes. Recently , po wer- 12 efﬁcient resource management for wireless networks based on cooperativ e and cognitive architectures has been discussed in [80], [81]. Howe ver , current research that addresses energy efﬁcient resource management for these systems under a variety of network objecti v es and constraints is not yet fully dev eloped. For cooperativ e systems, relaying mechanisms that mini- mize energy consumption while satisfying certain QoS per - formance criterion should be in v estigated. W e also need to explore distrib uted schemes based on economic models with energy as a cost in the o verall utility function. More speciﬁ- cally , we need to ﬁnd answers to three fundamental questions: “where to place relays”, “whom to relay” and “when to relay”. In order to answer the ﬁrst question “where to place relays”, we ﬁrst need to obtain the optimal relay geometry , in terms of energy consumption, within a cell with different number of relays and then we must also optimize the number of relays. The second question is related to the design of optimal relay selection criterion. This relay selection criterion should be based on both fairness and energy efﬁciency . The authors in [82], [83] hav e proposed an energy efﬁcient distributed relay selection criterion with ﬁnite-state Markov channels and adaptiv e modulation and coding in a single user cooperati ve system. Such ideas should be explored for multi-user scenario. T o solve the last problem of “when to relay”, design of resource allocation strategies for single and multiuser wireless systems should be studied such that relaying is selecti vely enabled so as to reduce overall power consumption [84], [85]. T o improv e energy efﬁcienc y of cognitive radio systems, energy consumed per bit can be taken as performance metric [86]. W e also need to inv estigate low po wer consumption based scheduling mechanisms in presence of multiple cog- nitiv e users. In [87], the authors have proposed some energy- efﬁcient and lo w complexity scheduling mechanisms for up- link cognitive cellular networks and ha ve shown that round- robin scheduling is more energy efﬁcient than opportunistic scheduling while providing the same average capacity and BER. Using mathematical tools based on dynamic program- ming and optimal control, we need to design resource allo- cation schemes for cognitive radio systems such that ov erall power consumption is minimized over a period of time while providing satisfactory performance. Lastly , we also need to inv estigate into the design of energy- aware heterogeneous networks, where the macrocell (high- power node) and femtocell (low-po wer node) coexist for co- channel deployment. Femtocell must cogniti vely adapt to its surrounding en vironment and transmit in such a way in order not to create cross-tier interference to main cellular network [88]. Using cooperativ e relaying, the network coverage of these femtocells can be improved without causing huge in- terference to the macrocell system. For such heterogeneous network architecture, emplo ying po wer -ef ﬁcient resource man- agement techniques such as selectiv e relaying, energy efﬁcient modulation etc. can be very attractiv e. Further research on optimal cell sizes and femtocell BS locations taking into consideration the energy spent for the system backhaul and signaling overhead, can sav e a lot of power . This way we can further reduce the energy consumption of macrocell BSs and user handsets to achieve a giv en system performance. D. Cr oss-Layer Design and Optimization Cellular wireless communication systems ought to sup- port dif ferent kinds of applications, including v oice and data applications. Each application has a dif ferent ener gy limit, required bit rate, bit error rate, delay constraints, outage probability , etc. T raditionally , these requirements have been tried to be achiev ed within the scope of a layered structure, called protocol stack. In fact, there has been a signiﬁcant amount of research tackling these problems within each layer , assuming each layer operates independently of the other layers. Examples of these efforts in the link layer are employing MIMO techniques, channel coding, po wer control and adaptiv e resource allocation techniques. In the MAC layer , different channelization or random access schemes, along with schedul- ing and power control can be mentioned. Moreov er , regarding the network layer , a rich literature can be found on the energy-constrained and delay-constrained routing. And ﬁnally , adjusting QoS requirements adaptiv ely is a venue to meet the users demand in the application layer [89]. The rationale behind using the protocol stack is that it helps the designer to break the design problem into several simpler problems, namely layer modules. This paradigm also makes the ev aluation of the proposed algorithms easier . How- ev er , limiting each layer to be independent of others and sub-optimality of this modularized paradigm lead to a poor performance, especially when the resources such as energy are scarce [89]. Therefore, cross-layer design can be a very useful tool to minimize energy consumed across the entire protocol stack. In cross-layer design, we try to escape from the limitations that the traditional waterfall-like concept of protocol stack imposes. In the ne w paradigm, we want to not only consider the interdependencies between dif ferent layers, but also take advantage of them. In particular, for energy saving purposes, it is necessary to consider the inv ariably changing operation conditions in cellular networks. Due to the mobility of the users, and also characteristics of wireless channel along with the nature of modern applications, prop- agation en vironment and application requirements are time varying. Thus, more holistic control algorithms from cross- layer perspective must be designed which adapts the system to these dynamics at run time. Going in this direction, greener cellular communication systems can be delivered compared to the existing ones [90]. Howe ver , if not carefully designed cross-layer design might itself lead to increased complexity and energy consumption. Hence, we should explore the cross- layer alternatives to schemes proposed for an indi vidual layer (PHY , MA C etc.) and analyze important tradeoffs in energy consumption and system performance. Multiple relays in co- operativ e communication and spectrum sensing mechanism in cognitiv e radio networks introduce new challenges in cross- layer design for these networks [91], [92]. One of the objectives should be to devise cross-layer schemes that will allo w joint optimization of some or all of the following parameters: assigning the subcarriers, rates, and power (physical layer attrib utes), channel access mechanisms 13 (MA C layer attribute), routing (network layer attribute), and rate (transport layer attribute) while taking into account system related errors (e.g. sensing errors in cognitiv e radios) and other errors that contribute to cross layer issues. Further , in order to sa ve energy in an ad-hoc wireless network, more packets should be transmitted when channel quality is good and at the same time collision losses due to network congestion must be reduced so that the packets need not to be retransmitted. In this regard, we need to explore ne w mechanisms for cogniti ve radio and cooperativ e ad-hoc networks by which channel trafﬁc can be measured either by single-bit or multi-bit signalling ov erhead or by loss or delay in the network. Ener gy-ef ﬁcient cross-layer schemes that will optimize resources in various layers while considering the channel quality as well as the network traf ﬁc, should be inv estigated. An example, where cross-layer design would be crucial, is in the use of cooperative relaying to improve spectrum div ersity in cognitiv e radio networks [93]. Large gains in efﬁcienc y and fairness of resource sharing can be obtained by cooperation among cognitiv e radio nodes. Speciﬁcally , some cognitiv e radio users with low traf ﬁc demand can help impro v e spectrum efﬁcienc y by acting as relays for the cognitive radio users that have high traf ﬁc demand but lo w av ailable bandwidth. For such a network, cross-layer design is important as while performing resource allocation (relay , power etc.), transmission demand of each cogniti ve radio user has to be taken into account. E. Addressing Uncertainty Issues Most research in the ﬁeld of cooperative and cogniti ve radio systems is mainly based on the assumption of perfect channel state information (CSI), which is often unrealistic in practice. Presence of non-Gaussian noise, quantization effects, fast v arying en vironment, delay in CSI feedback systems and hardware limitations are the main factors that cause errors in CSI. The performance of cognitive radio sensing system is drastically impaired, when various wireless channels e.g., detecting, reporting, and inter-user channels have uncertainty [94]. For cooperativ e systems, the optimal relay selection and rob ust resource allocation with imperfect CSI has also remained largely unexplored. For cogniti ve radio systems, it is also important to take into account the effects of imperfect sensing [95]. Spectrum sensing is further complicated due to uncertainty in interference from other secondary networks [96]. Providing robustness in conjunction with energy efﬁcient solutions to such scenarios is, therefore, a task of signiﬁ- cant practical interest. Also, the robustness of the efﬁcient scheduling schemes for MA C and cross-layer optimization needs further in vestigation taking uncertainty in the channel congestion into account. In order to maintain energy sa vings under imprecise conditions, we must in vestigate the rob ustness of our proposed energy-ef ﬁcient schemes and compare the per- formance with the existing schemes in practical scenario with uncertain en vironment. Hence, depending on the QoS targets, robust algorithms for energy efﬁcient resource optimization considering uncertainty in CSI, should be explored. V I I . S O M E B R OA D E R P E R S P E C T I V E S The most important issue in developing networks which are energy-a ware is to model the consumption of the wire- less interfaces [97]. Usually , the wireless interface consumes energy with the same rate in recei ve, transmit or idle states. In turn, the less the wireless interface is operating, the less energy is consumed. Based on the preceding argument, the best strategy to minimize the energy consumption is to shut down the wireless interface, or to go to energy saving mode as much as possible. In order to achiev e this, algorithms needed to determine when it is suitable to switch to energy saving mode or turning off the transceivers. W e already have discussed strategies with the aforementioned concept in this paper . For instance, we hav e mentioned Discontinuous Reception (DRX) and Discontinuous Transmission (DTX) modes in L TE standard, and sleep mode mechanism in IEEE 802.16e, both for mobile terminals. W e also hav e talked about enabling sleep mode for BSs. Ho we ver , these methods are based on instantaneous observations. On the other hand, the trafﬁc pattern is dramatically different in different times of the day or in dif ferent geographical locations. In a broader perspectiv e, there can be a data-base in BS and mobile terminals, in which the trafﬁc pattern during dif ferent times of the day is sav ed. Based on this obtained statistics, dynamic algorithms can be designed in order to switch the BS or mobile terminal to a different power proﬁle appropriate for that time of the day . In a recent paper by Dufkov a et al. [98], it has been sho wn that if such predictions on users are av ailable, savings from 25% up to 50% can be achiev ed, depending on the time of the day . Howe ver , their results are based on off-line optimizations and represent an upper bound on the energy savings possible. From another perspecti ve, BSs distributed over a certain geographical area are connected to a power grid. In recent years, smart grid has emerged to coordinate the power gen- erators, transmission systems and appliances utilizing two- way communication lines between all these dif ferent entities. These two-way communication lines can be dedicated point- to-point wireless channels or IP-based connections [99]. On the other hand, BSs in general, are power hungry elements. Hence, looking at BSs as power consumers or appliances, and absorbing them in a smart grid can exceedingly increase the power efﬁcienc y without adversely affecting the QoS and capacity . This can be done by adding measurement sensors which can update the status of BSs, and then transmit them to the other BSs and smart grid control system. Here, in addition to cooperation with each other , BSs also cooperate with the power system to manage the energy consumption. Contrary to the ideas mentioned by far , Humar et al. in [100] suggest a dif ferent way of thinking in energy ef ﬁciency modeling. Almost all the research on making cellular com- munication green results in lar ger number of BSs with lower lev el of powers, since the objectiv e is to reduce the operating ener gy . Howe v er , authors of [100] noticed that in all the cases, the ne w BSs are more sophisticated equipments, and producing these sophisticated equipments requires more energy compared to con ventional ones. This energy which is associated with all the processes of producing an equipment is called embodied 14 ener gy . According to this paper , embodied energy accounts for a signiﬁcant proportion of energy consumed by the BS, and taking this energy into account along with operating ener gy in modeling cellular network’ s energy consumption results in solutions which disagree with increasing the number of BSs and lowering their po wer . T ABLE II E N ER G Y S A V I N G S O B TAI N E D B Y S O ME O F T H E D I S C US S E D T E CH N I QU E S Description Reported sa vings Improv ements in Po wer Ampli- ﬁer - up to 50% with doherty ar- chitecture and GaN-based am- pliﬁers - up to 70% with switch-mode power ampliﬁers Network self-organizing tech- niques between 20-40% BS power savings Renew able Energy Resources in off-grid sites up to 0.35% of global diesel consumption Heterogeneous network deployment up to 60% savings compared to a network with macro-cells Dynamic spectrum manage- ment up to 50% V I I I . C O N C L U S I O N This paper addresses the ener gy efﬁciency of cellular com- munication systems, which is becoming a major concern for network operators to not only reduce the operational costs, but also to reduce their en vironmental effects. W e began our discussion with green metrics or energy ef ﬁciency metrics. Here, we presented a brief surve y of current efforts for the standardization of the metrics and the challenges that lay ahead. Regarding architecture, since BSs represent a major chunk of energy consumed in a cellular network, we then presented an exhaustiv e survey of methods that ha ve been currently adopted or will be adopted in future in order to obtain energy savings from BSs. In particular, we discussed the recent improv ements in power ampliﬁer technology that can be used to bring energy savings in BSs. Improvements in the power ampliﬁer will not only decrease the power consumption of the hardware system, but will also make the BS less dependant on air-conditioning. W e also discussed the power saving protocols such as sleep modes, that hav e been suggested for next generation wireless standards. Such power saving protocols at the BS side still need to be explored in future wireless systems. Next, we discussed energy-a ware cooperativ e BS power management, where certain BSs can be turned off depending on the load. A recent concept called “Cell zooming” appears to be a promising solution in this regard. Another way to signiﬁcantly reduce the power consumption of BSs, in particular, those at the off-grid sites, is by using renewable energy resources such as solar and wind energy in place of diesel generators. Lastly , we discussed how minimizing the number of BSs with a better network design and bringing minor architectural changes can be beneﬁcial in achieving energy efﬁciency . Heterogeneous network deployment based on smaller cells such as micro, pico and femtocells is another signiﬁcant technique that can possibly reduce the po wer consumption of a cellular network. Howe ver , as some of the recent research suggests, careful network design is required as deploying too many smaller cells may in fact reduce the power efﬁcienc y of the central BS. Also, when a large number of BSs with small cell sizes are deployed, the embodied energy consumption will dominate and lead to an increase in total energy consumption [100]. W e also discussed how emerging technologies such as cognitive radio and cooperative relaying can be useful for obtaining “green” network technology . In this re gard, we discussed research challenges to address energy efﬁcienc y in cognitiv e and cooperative networks including low-ener gy spectrum sensing, energy-a ware MA C and routing, efﬁcient resource management, cross-layer optimization, and uncer- tainty issues. Finally , we explored some broader perspectiv es such as statistical power proﬁles, smart grid technology and embodied energy to achiev e energy efﬁcient cellular network. T able II lists the energy sa vings reported by authors, that can be obtained by some of the techniques discussed in the paper . In summary , research on energy efﬁcient or “green” cellular network is quite broad and a number of research issues and challenges lay ahead. Nevertheless, it is in fav or of both the network operators and the society to swiftly address these challenges to minimize the en vironmental and ﬁnancial impact of such a fast growing and widely adopted technology . This article attempts to brieﬂy explore the current technology with respect to some aspects related to green communications and we discuss future research that may prove beneﬁcial in pursuing this vision. A C K N O W L E D G M E N T This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under their strategic project award program and in part by Alexander Graham Bell Canadian Graduate Scholarship. R E F E R E N C E S [1] Green Power for Mobile, GSMA, “Community Power Using Mobile to Extend the Grid”. 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Green Cellular Networks: A Survey, Some Research Issues and Challenges

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