IEEE Wireless Communications • December 201244 1536-1284/12/$25.00 © 2012 IEEE

AC C E P T E D F R O M OP E N CALL

INTRODUCTIONUntil now, the wireless communications systemhas been well developed and optimized interms of spectrum efficiency, transmission reli-ability, and users’ satisfaction from a variety ofmobile applications. However, the new chal-lenge of wireless communications systems hasrecently emerged due to the increasing cost ofpower and higher volume of teletraffic demand.These create an immediate need for “green”wireless communications which is a set of con-cepts, designs, and approaches to improve thepower efficiency of wireless systems, whilemeeting the quality-of-service (QoS) of mobileusers. Green wireless communications will helpthe network operator not only to save on thecost of power through better power efficiencyper service, but also to be environmentallyresponsible by minimizing the environmentalimpact (e.g. by using renewable power sourcesto reduce CO2 emissions). In addition, wirelesscommunications systems must adapt to thechanges on the power supply side, which willbecome more dynamic and distributed. This is

known as “smart grid”. Given all these require-ments, the issue of power management forwireless systems will become crucial and needsto be addressed accordingly. The typical wire-less communications system consists of threeparts: the core network, the access network,and the mobile unit. The largest fraction ofpower consumption in wireless networks comesfrom the access network, especially the wirelessbase station, as the number of wireless basestations is enormous and the correspondingpower consumption is high. With this premise,power saving in wireless base stations is partic-ularly important for network operators.

In this article, we first provide an introduc-tion of green wireless communications with thefocus on the power efficiency of wireless basestations, renewable power sources, and smartgrid. Then, we consider the adaptive powermanagement for the wireless base station witha renewable power source in a smart grid envi-ronment. While the main power supply of thewireless base station is from the electrical grid,a solar panel is considered to be an alternativepower source. Adaptive power management isused to coordinate among the electrical gridand solar panels, which is energy-efficient andallows for greater penetration of variablerenewable energy sources in a green communi-cations system. With smart grid, adaptive powermanagement can communicate about thepower price with the electrical grid and adjustpower buying accordingly. However, in such anenvironment, many parameters are uncertain(e.g. generated renewable power, the price ofpower from the electrical grid, and power con-sumption of wireless base stations whichdepends on the traffic load). Therefore, thestochastic optimization problem is formulatedand solved to achieve the optimal power man-agement. The performance evaluation is per-formed and clearly shows that with the optimalpolicy of adaptive power management, thepower cost of the wireless base station can beminimized.

The rest of this article is organized as follows.We present an overview of green wireless com-munications. We introduce adaptive power man-agement for the wireless base station with arenewable power source in a smart grid environ-ment. Finally, we conclude the article.

DUSIT NIYATO, XIAO LU, AND PING WANG, NANYANG TECHNOLOGICAL UNIVERSITY

ABSTRACTThe growing concerns of a global environ-

mental change leads to a revolution in the wayenergy is utilized. In the wireless industry, greenwireless communications has recently gainedincreasing attention and is expected to play amajor role in the reduction of electrical powerconsumption. Actions to promote energy savingof wireless communications with regard to envi-ronmental protection are becoming imperative.To this purpose, we study a green communica-tions system model where a wireless base stationis provisioned with a combination of a renew-able power source and the electrical grid tominimize the cost of power consumption as wellas meet the users’ demand. More specifically,we focus on adaptive power management for awireless base station under various uncertain-ties, including renewable power generation,power price, and wireless traffic load. Webelieve that demand-side power managementsolutions based on the studied communicationarchitecture is a major step toward green wire-less communications.

ADAPTIVE POWER MANAGEMENT FOR WIRELESSBASE STATIONS IN A SMART GRID ENVIRONMENT

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IEEE Wireless Communications • December 2012 45

GREEN WIRELESS COMMUNICATIONSMuch research and development efforts havebeen made in the wireless industry, aiming forenvironment-friendly power solutions that leadto green wireless communications. Green wire-less communications will contribute to the reduc-tion of our global carbon footprint and enablemutual broader impacts across related fields,among which, renewable power resources andsmart grid are attracting growing interests. Inthis section, we present an overview of the majorconcerns in green wireless communications relat-ed to renewable power and smart grid.

RENEWABLE POWER SOURCES ANDGREEN WIRELESS COMMUNICATIONS

The climate change deriving from enormouspower consumption as a result of rapid industri-al development has pushed people to use sus-tainable alternative power, among which,renewable power resources with their low pollu-tion and sustainable accessibility are attractive asa replacement for traditional fossil energy. Ingreen wireless communications, renewable powersources can be used to replenish the energy ofwireless base station/network nodes as alterna-tives to a traditional power source. Manufactur-ers and network operators have starteddeveloping and deploying a wireless base stationwith a renewable power source [1]. For example,Ericsson and Telecom Italia developed and test-ed the Eco-Smart solution, which uses solar pan-els to fully power the cell site. Vodafone, ChinaMobile, and Huawei jointly performed variousexperiments on renewable power sources, includ-ing solar panels, wind power generators, and thehybrid system for the wireless base station [2].The experiments focus on the implementationverification, power reliability, and cost reduction.

However, renewable power sources, e.g. solar,wind, hydro, geothermal, tidal energy, andbiomass, are typically featured as weather-driv-en, unevenly distributed geographically, non-scheduled, and relatively unpredictable. Thus,the main problem of the applications of renew-able energy is that the power generation cannotbe fully forecasted and may not follow the trendof actual power demand.

For the use of a renewable power source ingreen wireless communications, efficient powermanagement is the primary concern since thereplenishment rate depends on renewable powergeneration, which is known to fluctuate and beintermittent. In a wireless network, power savingoften requires a degradation in network perfor-mance (i.e. higher latency and lower through-put). Designing efficient power management istherefore challenging because of the necessarycompromises between power saving and networkperformance. A number of interesting workshave been carried out to address the issue. Theauthors in [3] studied throughput and value-based wireless transmission scheduling undertime limits and energy constraints for wirelessnetwork employing a renewable power source.Optimal scheduling algorithms that selectivelytransmit data at calculated rates were presentedto maximize the throughput and total transmis-

sion value. The authors in [4] developed a modelto characterize the performance of multi-hopwireless networks with different types of energyconstraints (i.e. renewable and non-renewablepower source). Based on this model, the authorsproposed an algorithm of energy-aware routingwith distributed energy replenishment to opti-mally utilize available energy. More recently, theauthors in [5] proposed an energy-awareresource provisioning algorithm for energy pro-vision in a solar-powered wireless mesh networkwith the goal to save resource usage while pre-venting node outage. Significant resource savingis achieved with the proposed algorithm inhybrid networks with a mixture of solar-poweredand continuously powered nodes.

SMART GRID ANDGREEN WIRELESS COMMUNICATIONS

Large-scale centralized electricity generation andhigh-voltage long-distance transmission adoptedby a traditional electrical grid system are the twobasic causes of power inefficiency. The conceptof smart grid is introduced by using informationand communications technology (ICT) toimprove the efficiency and reliability of the elec-trical grid. The main features of smart grid relat-ed to green wireless communications are demandside management (DSM), decentralized powergeneration, and price signaling. With demandside management, the power generators andconsumers can interact to improve the efficiencyof power supply and consumption, respectively.For example, the operation and power consump-tion of the deferrable load (e.g. heating andpumping) can be adjusted according to the gen-erator capability. Decentralized power genera-tion can be performed by consumers and smallpower plants (e.g. solar panel and wind turbine).As a result, consumers will be less dependent onthe main electrical grid, reducing the power costand avoiding any impact from power failure.With price signaling, the consumers will beaware of the current power price and the gener-ator can use a cheap power price to encouragethe consumers to use electric power during off-peak periods (e.g. nights or weekends). Conse-quently, the peak load will be reduced, whichresults in lower investment in the infrastructure(e.g. transmission line and substation).

Many current research works focus on theenabling technologies of interaction betweensmart grid and wireless communications [6]. Onone hand, wireless communications is a key com-ponent in smart grid to communicate a variety ofdata and measurements among power genera-tors, transmission lines, distribution substations,and consumer loads. On the other hand, smartgrid can be used to support green wireless com-munications for better use of power to providewireless service to mobile users. This similarconcept has been explored in “green computing”[7]. In this case, the data center can schedule theservice request (i.e. data processing) accordingto the power supply from electrical grid. Also,efforts have been made on theoretical analysis.In a wireless network, each wireless base sta-tion/node powered by smart grid might be selfishin improving performance in capacity or QoS.

For the use of arenewable power

source in green wire-less communications,

efficient power management is the

primary concernsince the

replenishment ratedepends on

renewable powergeneration, which isknown to fluctuate

and be intermittent.

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IEEE Wireless Communications • December 201246

How to improve power saving without adverselyaffecting QoS performance and capacity is oneof the main concerns. Recent progress in wireddistributed computing theory [8] provides funda-mental models for coordinated management andload balancing of wireless base stations undersmart grid, which has potential for addressingthe concern.

From the above related works, it is clear thatthe use of renewable power sources and smartgrid will be the major trend of green wirelesscommunications. However, there are many issuesto be addressed, including protocol design, radioresource optimization, and power management.

ADAPTIVE POWER MANAGEMENT

In this section, we present adaptive power man-agement for the wireless base station with arenewable power source in a smart grid environ-ment. First, the system model is described. Theoptimization formulation to achieve an optimalpolicy of adaptive power management is dis-cussed. Then, the performance evaluation of theproposed scheme is presented.

SYSTEM MODELThe system model of adaptive power manage-ment for the wireless base station is shown inFig. 1. The components in this system model areas follows:

Wireless base station: A wireless base stationor access point is a centralized device used toprovide wireless services to mobile units. Thewireless base station is the power consumptiondevice. The amount of power consumptiondepends on the type of base station and trafficload (i.e. the number of ongoing connectionsfrom active mobile units).

Electrical grid: The electrical grid providesan interconnected network including transmis-sion lines and distribution substations for deliv-ering electricity from generators to consumers.The electrical grid is a main source of power tothe wireless base station. The power suppliedfrom the electrical grid has a price per kWh(kilowatt-hours).

Renewable power source: Renewable power isprovided from natural resources such as sunlight

and wind that are replenishable. As a result, thevariable cost of renewable power is cheaper thanthat from the electrical grid. However, the powergenerated by a renewable source is typically ran-dom due to the unpredictable availability of nat-ural resources. Renewable sources areconsidered to be an alternative power supply ofthe electrical grid. The maximum amount ofgenerated power from renewable sources (i.e.capacity) is denoted by R kW (kilowatt).

Power storage: The battery is the power stor-age device for the wireless base station. The bat-tery can be charged by the power from therenewable power source or from the electricalgrid when the power price is low. The batteryhas a limited maximum capacity for power stor-age denoted by B kWh. Note that the powerstored in a battery can decrease even withoutconsumption. This is referred to as the self-dis-charge phenomenon. The self-discharge rate pertime unit is denoted by L, and its associated costis denoted by Ploss (e.g. the cost to replace self-discharge power).

Adaptive power management controller: Theadaptive power management controller has amechanism to make a decision on power supplyfrom a renewable source and the electrical gridto the battery and wireless base station. Theadaptive power management controller utilizesthe available information to optimize the deci-sion with the objective to minimize the powercost while meeting the demand of the wirelessbase station. The details of this optimization willbe presented later in this section.

While the electrical grid is owned by the utili-ty company, the wireless base station, powerstorage, renewable power source, and adaptivepower management controller belong to the net-work operator, with the objective to minimizepower cost. The information to be exchangedand maintained among the above components tosupport adaptive power management of thewireless base station is as follows: power pricefrom the electrical grid, generated power fromrenewable sources, battery storage, and powerconsumption of the wireless base station. Thisinformation is measured and reported periodi-cally to the adaptive power management con-troller. In the smart grid environment, thecommunications infrastructure to transfer thisinformation is assumed to be available. In thiscase, broadband access (e.g. ADSL) and a localarea network (e.g. Ethernet) can connect theadaptive power management controller with theelectrical power grid and renewable powersource as well as the wireless base station.

Adaptive power management can be consid-ered as the demand side management (DSM).DSM is part of smart grid and allows the powerconsumers (e.g. the wireless base station) toadjust their power consumption. The main aimof DSM is to balance the consumption frompeak periods to non-peak periods such that thecost of infrastructure to accommodate peakdemand is reduced. In this context, adaptivepower management has the ability to controlthe purchase of power from the electrical grid(i.e. consumption) given the varied price andpower generated from renewable sources. Inaddition, adaptive power management will

Figure 1. System model of adaptive power management for wireless base sta-tion in smart grid.

Wirelessbase

station Mobileunits

Adaptive powermanagement

Distribution station ofelectrical grid

Renewable powersource (solar panel)

Powerstorage(battery)

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IEEE Wireless Communications • December 2012 47

defer power buying when the price is high (e.g.peak hours) and move the consumption (e.g.charging the battery) to off-peak periods suchas nighttime. Although DSM was studied in theelectrical engineering field (e.g., [9, 10]), thespecific characteristics of the wireless base sta-tion were not taken into account. Also, theuncertainty (e.g. due to traffic load) wasignored when optimizing the power manage-ment strategy. Consequently, the optimality(i.e. minimum power cost) cannot be guaran-teed, and this is the focus of our article.

The power consumption models of a wirelessbase station have been studied in the literature,e.g. [11]. For the wireless base station, the powerconsumption is composed of two parts, i.e. staticand dynamic. The static power consumption isconstant when the base station is active evenwithout ongoing connections from users. On theother hand, the dynamic power consumptiondepends on the ongoing connections and is afunction of traffic load. The power consumptionof a base station is from different components,including power amplifier, signal processing unit,antenna, and cooling. In this article, the microbase station is considered in which its powerconsumption depends on the traffic load (i.e.dynamic power consumption is significant com-pared to static consumption). The power con-sumption of the micro base station can beexpressed as follows: C = Est + EdyN , where Estis the static power consumption, Edy is thedynamic power consumption coefficient, and Nis the number of ongoing connections. The staticand dynamic power consumption of the microbase station depends on the transmit power,power amplifier efficiency, and power supplyloss. Also, the dynamic power consumptiondepends on the signal processing and transmitpower per connection. In summary, given thenumber of ongoing connections, the power con-sumption of a wireless (i.e. micro) base stationcan be calculated. This information will be usedto optimize the decision by the adaptive powermanagement controller.

OPTIMIZATION-BASEDADAPTIVE POWER MANAGEMENT

Adaptive power management of a wireless basestation is a challenging issue due to the uncer-tainty in the environment and the system. Toaddress this issue, the stochastic optimizationproblem can be formulated and solved to obtainthe best decision of the adaptive power manage-ment controller such that the power cost of thewireless base station is minimized.

Uncertainty — A variety of uncertainties exist forpower management for the wireless base station.

Renewable power source: The power generat-ed from renewable sources such as solar andwind generators is highly random due to theweather conditions [12]. For example, the solarenergy depends on the amount of sunlight.Cloud and rain, which are unpredictable, reducethe amount of generated power.

Power price from electrical grid: Due to theunpredictable condition (e.g. demand) of theelectrical power grid, the power price can be

random within a certain range depending on thecurrent system conditions [13]. For example, thepower price can be high (i.e. peak-hour price) ina certain time period. In this case, the consumercan be informed about power price (i.e. a pricesignaling feature in smart grid) [10].

Traffic load of wireless base station: Theconnection arrival (i.e. newly initiated and hand-off users) can be varied (e.g. due to the mobili-ty). Also, the connection demand of the wirelessbase station depends on the usage condition(e.g. special events that result in peak loads). Asa result, the number of ongoing connections Nwill be random [14], and the power consumptionthat can be obtained from the aforementionedpower consumption model is also random.

The uncertainty can be represented by the“scenario” which is the realization of a randomvariable. The scenario takes value from the cor-responding space that is commonly assumed tobe a finite discrete set. For example, the powerprice at a certain period can take value from aset of 12 and 20 cents per kWh (i.e. normal andpeak-hour prices, respectively). The scenario canbe also defined over multiple periods. For exam-ple, with three periods in one day, the first sce-nario can be defined as {12, 12, 20, 12} centsper kWh for the power prices in the morning(6:00–12:00), afternoon (12:00–18:00), evening(18:00–24:00), and at night (0:00–6:00), respec-tively. Alternatively, the second scenario can bedefined as {12, 20, 20, 12} cents per kWh. Thatis, the second scenario represents the case ofhaving a peak-hour price in the afternoon. Withmultiple random parameters, the scenario isdefined as a composite value of generatedrenewable power, power price from the electricalgrid, and power consumption of the wirelessbase station. For example, one scenario denotedby w is defined as follows. For morning, after-noon, evening, and night, the generated renew-able powers are {130, 290, 0, 0} Wh, the powerprices are {12, 12, 20, 12} cents per kWh, andpower consumption is {200, 230, 240, 200} W,respectively. The scenarios can be extractedfrom historical data, e.g. the traffic load historyand power price from the electrical grid. Also,the weather forecast can be used to determinethe scenario of generated renewable power.

The probability distribution associated withthe scenarios of generated renewable power,power price from the electrical grid, and powerconsumption of a wireless base station can beestimated. Given the observation period (e.g. 60days), the number of days for the observed sce-nario can be counted. The corresponding proba-bility can then be calculated by dividing thisnumber of days by the duration of the observa-tion period (i.e. 60 days). For example, if thenumber of days for the power price scenario {12,12, 20, 12} is 15 days, while the number of daysfor scenario {12, 20, 20, 12} is 45 days, then theprobabilities for the first and second power pricescenarios are 15/60 = 0.25 and 45/60 = 0.75,respectively. The same method can be appliedfor the scenarios of generated renewable powerand power consumption.

Given the uncertainty, the objective of theadaptive power management controller is tominimize the cost of buying power from the

Adaptive power man-agement of a wire-

less base station is achallenging issue dueto the uncertainty inthe environment and

the system. Toaddress this issue,the stochastic opti-mization problem

can be formulatedand solved to obtain

the best decision.

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IEEE Wireless Communications • December 201248

electrical grid with the constraint to meet thepower consumption demand of a wireless basestation.

Stochastic Programming Formulation — To obtain thedecision of the adaptive power managementcontroller under uncertainty, the optimizationproblem based on multi-period linear stochasticprogramming can be formulated and solved [15].Stochastic programming is a mathematical toolto model the optimization problem with uncer-tainty of parameters. Stochastic programming isan extension of deterministic mathematical pro-gramming in which stochastic programming doesnot have a strong assumption on the completeknowledge of the parameters. Instead, forstochastic programming, the probability distribu-tion of random parameters which can be esti-mated is incorporated into the optimizationformulation. Stochastic programming can beused to obtain the optimal solution that is a fea-sible policy for the possible cases (i.e. scenarios).This optimal solution or policy, which is a map-ping from the scenario to the decision, will mini-mize the expectation of the objective (i.e. cost).The optimal solution of stochastic programmingcan be obtained by formulating an equivalentdeterministic mathematical program in whichthe standard methods (e.g. interior pointmethod) can be applied efficiently.

Although there exist other approaches (e.g.Markov decision process, robust optimization,and chance-constrained programming) toaddress the optimization problem with uncer-tainty, these approaches are not suitable for thecost optimization of adaptive power manage-ment for the wireless base station. For theMarkov decision process, the stochastic processof the random parameters must have Markovproperty. That is, the next state (i.e. scenario) ofthe random parameter depends on the currentstate, but not the past state. This Markov prop-erty may not be held in many situations (e.g.power price of electrical grid). For robust opti-mization, the solution is obtained only for theworst case scenario with which the performancecan be unrealistically poor due to the considera-tion of the extreme case. For chance-constrainedprogramming, with optimal solution, the con-straint violation will be bounded by the thresh-old. However, only complex analysis exists forthe basic probability distribution (e.g. normaldistribution).

Therefore, stochastic programming becomesthe suitable approach for adaptive power man-agement since this approach can be used toobtain the optimal solution that ensures that allconstraints will be met. The efficient method canbe applied to obtain the optimal solution forpossible scenarios in which the expected costgiven uncertainty is minimized.

The multi-period stochastic programmingmodel for adaptive power management is shownin Equations 1 through 5. This optimizationmodel is for a decision horizon which is dividedinto T decision periods. We consider the lengthof a period to be one hour in which the spotpower price from the electrical grid can be var-ied. The objective and constraints of optimiza-tion formulation are defined as follows:

(1)

subject to st,w + xt,w + Rt,w = st+1,w + Ct,w + yt,w,t = 1, …, T – 1, wŒW (2)

st,w £ B, t = 1, …, T, w Œ W (3)

s1,w = B1, sT,w = BT (4)

xt,w ≥ 0, st,w ≥ 0, yt,w ≥ 0, t = 1, …,T, w Œ W (5)

•Equation 1 is the objective to minimize theexpected cost due to power buying from theelectrical grid and battery loss due to self dis-charging over the entire decision horizon (i.e. t= 1, …, T), where E(◊) is expectation, and Pt,w isa power price. This expectation is over all sce-narios in space W given the corresponding prob-ability Pr(w) of scenario w Œ W.

•Equation 2 is the constraint for the balanceof power input and output of a decision period t.The power input of a decision period t includesthe power stored in battery st,w at the beginningof period t, power buying from the electrical gridxt,w , and generated renewable power Rt,w inperiod t. The power output of a decision period tincludes the power remaining in battery st+1,w atthe end of period t (i.e. at the beginning of peri-od t+1), the power consumption of the wirelessbase station in the current period Ct,w , andexcess power yt,w. Note that the excess power isused to represent the amount of power inputexceeding the power consumption and batterycapacity.

•Equation 3 is the constraint of power stor-age that the power in the battery must be lowerthan or equal to the capacity B.

•Equation 4 is the initial and terminationcondition constraint where B1 and BT are thepower storage in the battery at the first and lastdecision periods, respectively.

•Equation 5 is the constraints of non-nega-tive value of power.

The multi-period stochastic programmingmodel can be transformed into a linear program-ming problem [15], and the standard method ofsolving linear programming can be applied toobtain a solution. The solution is the amount ofpower bought from the electrical grid, denotedby x*t,w at time period t given scenario w. Thissolution is applied when the realization of thescenario of generated renewable power, powerprice, and power consumption is observed.

PERFORMANCE EVALUATIONParameter Setting — We consider the adaptivepower management for the long-term evolution(LTE) micro base station. The parameter settingof the micro base station is similar to that in[11]. The static power consumption is Est =194.25W, while the dynamic power consumptioncoefficient is Edy = 24W per connection. Thetransmission range of the micro base station is

∑

∑∑ ω

+

= +ω

ω ω ω

=

∈Ω=

ω

E x P s LP

Pr x P s LP

min ( )

( )( )

xt t t

t

T

t t tt

T

loss1

, , , loss1

t ,

Stochastic programmingbecomes the suitableapproach for adaptive power management sincethis approach can beused to obtain theoptimal solution thatensures that all constraints will be met.

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IEEE Wireless Communications • December 2012 49

100 meters in which the transmit power calculat-ed as in [11] is applied to ensure the reliableconnectivity of the users. The maximum numberof connections of the micro base station is 25.

We consider the solar panel as a renewablepower source. The capacity of the solar panel is300Wh. The battery capacity is 2kWh. The initialand termination power levels of battery areassumed to be 500W. The self-discharge rate ofa battery is 0.1 percent per hour. We considerthe randomness of the power price, generatedrenewable power, and the traffic load of themicro base station. For the power price, two sce-narios are considered, i.e. peak-hour and normalprices, whose average power prices are 20 and12 cents per kWh, and the corresponding proba-bilities are 0.6 and 0.4, respectively. For therenewable power, two scenarios are considered,i.e. clear sky and cloudy, whose average generat-ed power from 6:00–18:00 are 195Wh and100Wh, and the corresponding probabilities are0.6 and 0.4, respectively. For the traffic load ofthe micro base station, five scenarios are consid-ered, i.e. heavy uniform, medium uniform, lightuniform, heavy morning, and heavy evening, andthe corresponding probabilities are 0.1, 0.1, 0.2,0.2, and 0.4, respectively. For heavy uniform,medium uniform, and light uniform scenarios,the traffic load is uniform and the mean connec-tion arrival rates are 0.56, 0.22, and 0.15 connec-tions per minute, respectively. For heavymorning and heavy evening scenarios, the con-nection arrival is peak during 8:00–11:00 and17:00–21:00, whose mean connection arrival rateis 0.8 connections per minute, respectively. Theadaptive power management scheme is opti-mized for a 24-hour period (i.e. T = 24), sincethe power consumption and power price tend tohave the repeated patterns over 24 hours [10].

Numerical Results — Figure 2 shows the differentaverage power over the optimization period. Inthis case, the renewable source (i.e. solar panel)can generate power only when sunlight is avail-able. Therefore, the adaptive power manage-ment controller has to optimize the powerstorage in the battery and the power buyingfrom electrical grid to meet the requirement ofthe micro base station. We observe that the bat-tery is charged with the renewable power. Thepower is bought from the electrical grid occa-sionally for the micro base station (e.g. when therenewable power is not available) or to chargethe battery (e.g. at 8:00). Given the averagepower shown in Fig. 2, the power cost of thismicro base station with adaptive power manage-ment is 12.60 dollars per month.

For comparison purpose, we consider a sim-ple power management scheme in which thepower is bought from the electrical grid to main-tain constant battery storage (i.e. 1kW). Thepower cost per month of this simple power man-agement scheme is 15.94 dollars per month.Clearly, the proposed adaptive power manage-ment scheme achieves lower cost, and lowerpower consumption from the electrical grid.Nevertheless, even though the cost saving forone micro base station may be marginal (i.e. 3.34dollars per month or about 20.95 percent), thiscost saving can be significant when a number of

micro base stations are deployed (e.g. 100 basestations on a campus). Also, the lower use ofpower from the electrical grid which is mostlygenerated from the fossil fuel (e.g. coal and oil)will reduce CO2 emission, which is the main aimof green wireless communications.

Next we study the impact of battery capacityon the power cost. Figure 3 shows the powercost per month under different battery capacityand different renewable source capacity. As thebattery capacity increases, the adaptive powermanagement controller can store more powerwhen there is renewable power generated orwhen the power price from the electrical grid ischeap. As a result, the power cost per monthdecreases. However, at a certain capacity, the

Figure 2. Average generated renewable power, power consumed by wirelessbase station, battery storage, and buying power from smart grid.

Hour87

0.2

0

Ave

rage

pow

er (

kWh)

0.4

0.6

0.8

1

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6

Renewable powerPower consumptionBattery storageBuying power

Figure 3. Power cost per month under different battery capacity.

Battery capacity (kWh)

1.51

13

12

Pow

er c

ost

per

mon

th (

$)

14

15

16

17

18

19

20

21

22

2 2.5 3

No renewable sourceRenewable source capacity = 150WhRenewable source capacity = 300Wh

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IEEE Wireless Communications • December 201250

power cost becomes constant in which all gener-ated renewable power or the power with a cheap-er price from the electrical grid can be stored inthe battery and be sufficient for future demandof the micro base station. Consequently, increas-ing the capacity of the battery further will notreduce power cost, while the cost of the batterywill be higher.

In addition, as expected, when the capacity ofrenewable power sources increases, the powerprice bought from the electrical grid decreases(Fig. 3). However, it is also important to notethat the cost of increasing the capacity of renew-able power sources cannot be ignored. For exam-ple, the average price of a solar panel with

capacity of 150W is 200 dollars. If two panels areused, the power cost will be reduced from 20.66to 12.60 dollars per month (saving 8.06 dollarsper month). However, the cost of two solar pan-els will be 400 dollars. In this case, the break-even point (i.e. the time period in which the costsaving from renewable power source is equal toor larger than the cost of installing renewablepower source) will be at 49.6 months, or aboutfour years. This simple example clearly showsthat the cost of installing the renewable powersource is crucial from an economic point of view.However, this cost-benefit analysis is not thefocus of this article, and it is left to be studied infuture work.

Then, we investigate the effect of traffic loadof the micro base station on the power consump-tion. Fig. 4 shows the power bought from theelectrical grid and power cost per month underdifferent connection arrival rates. As expected,when the connection arrival rate increases, themicro base station consumes more power, whichresults in higher power cost. In addition, Fig. 4shows the comparison between the cases withand without the adaptive power managementcontroller. Without power management, poweris bought from the electrical grid to maintain theconstant battery storage of 1kW. Clearly, fromFig. 4 the power bought from the electrical gridand hence the power cost per month are lowerwith power management. These results clearlyshow the benefit of the proposed optimization-based adaptive power management.

Next, the impact of threshold in connectionadmission control (CAC) to the QoS perfor-mance and power cost is studied. With the guardchannel CAC [16], the threshold is used to reservethe channels for handoff connections, since themobile users are more sensitive to the droppingof handoff connections than the blocking of newconnections. With guard channel CAC, the newconnection is accepted if the current number ofongoing connections is less than the threshold,and will be rejected otherwise. As expected, asthe threshold becomes larger, more new connec-tions are accepted and can perform data trans-mission. As a result, the new connection blockingprobability decreases. However, handoff connec-tion dropping probability increases, since lesschannels are reserved. We observe that as thethreshold increases, there will be more ongoingconnections with the micro base station. There-fore, power consumption increases, and the powercost saving (compared to that without CAC)decreases. This result can be used to optimize theparameter (i.e. threshold) of CAC. For example,if the objective is to minimize the handoff con-nection dropping probability and to maximize thepower cost saving subject to the new connectiondropping probability to be less than 0.1. Then, thethreshold should be set to 20.

From the above results, it is clear that theproposed adaptive power management can mini-mize the cost of power consumption given vari-ous uncertainties, including renewable powergeneration, power price, and traffic load of thewireless base station. The optimization formula-tion will be useful for the design of the resourcemanagement of the wireless system in greenwireless communications.

Figure 4. Power cost per month under different battery capacity.

Average connection arrival rate (connections/minute)0.4

80

Ave

rage

buy

ing

pow

er (

Wh)

90

100

110

0.420.38 0.44 0.46 0.48 0.5 0.52 0.54 0.56

Without power managementWith power management

Average connection arrival rate (connections/minute)0.4

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ost

per

mon

th (

$)

80

90

100

110

0.420.38 0.44 0.46 0.48 0.5 0.52 0.54 0.56

Without power managementWith power management

Figure 5. Handoff call dropping probability, new call blocking probability, andpercentage of cost saving under different threshold in call admission control(CAC).

Threshold in call admission control (CAC)20 2515

0.05

0

Prob

abili

ty

0.1

0.15

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Threshold in call admission control (CAC)20 2515

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Handoff call dropping probabilityNew call blocking probability

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IEEE Wireless Communications • December 2012 51

CONCLUSIONIn this article, adaptive power management for thewireless base station incorporating a renewablepower source in a smart grid environment hasbeen proposed. The renewable source offers analternative power supply that not only saves thepower cost, but also reduces CO2 emission. Inaddition, with the smart grid, adaptive power man-agement can be part of the demand side manage-ment in which the power consumption can beadapted according to the power supply condition(i.e. power price). In this regard, the optimizationproblem has been formulated and solved to obtainthe optimal decision of adaptive power manage-ment. The uncertainty of generated power fromrenewable sources, power price from the electricalgrid, and power consumption of the wireless basestation due to varied traffic load has been takeninto account. The performance evaluation hasbeen performed and the results have clearly shownthat the optimal decision of adaptive power man-agement can successfully minimize the power cost.

ACKNOWLEDGMENTThis work was done at the Centre for Multime-dia and Network Technology (CeMNet) of theSchool of Computer Engineering, NanyangTechnological University, Singapore.

REFERENCES[1] J. Gozalvez, “Green Radio Technologies [Mobile Radio],”

IEEE Vehic. Tech. Mag., vol. 5, no. 1, Mar. 2010, pp.9–14.

[2] M. Belfqih et al., “Joint Study on Renewable EnergyApplication in Base Transceiver Stations,” Proc. Int’l.Telecommun. Energy Conf. (INTELEC), Oct. 2009.

[3] F. Zhang and S. T. Chanson, “Improving Communica-tion Energy Efficiency in Wireless Networks Powered byRenewable Energy Sources,” IEEE Trans. Vehic. Tech.,vol. 54, no. 6, Nov. 2005, pp. 2125–36.

[4] L. Lin, B. Shroff, and R. Srikant, “Asymptotically OptimalEnergy-Aware Routing for Multihop Wireless Networkswith Renewable Energy Sources,” IEEE/ACM Trans. Net.,vol. 15, no. 5, Oct. 2007.

[5] G. H. Badawy, A. A. Sayegh and T. D. Todd, “EnergyProvisioning in Solar-Powered Wireless mesh Net-works,” IEEE Trans. Vehic. Tech., vol. 59, issue 8, Oct.2010, pp. 3859–71.

[6] B. Heile, “Smart Grids for Green Communications[Industry Perspectives],” IEEE Wireless Commun., vol.17, no. 3, June 2010, pp. 3–6.

[7] A.-H. Mohsenian-Rad and A. Leon-Garcia, “Coordinationof Cloud Computing and Smart Power Grids,” Proc.IEEE Smart Grid Commun. Conf., Oct. 2010.

[8] W. Jai and W. Zhou, Distributed Network Systems, NewYork, Springer, 2005.

[9] X. Vallve et al., “Micro Storage and Demand Side Man-agement in Distributed PV Grid-Connected Installa-tions,” Proc. Int’l. Conf. Electrical Power Quality andUtilisation (EPQU), Oct. 2007, pp. 1–6.

[10] J. Kennedy, B. Fox, and D. Flynn, “Use of ElectricityPrice to Match Heat Load with Wind Power Genera-tion,” Proc. Int’l. Conf. Sustainable Power Generationand Supply (SUPERGEN), Apr. 2009, pp. 1–6.

[11] O. Arnold et al., “Power Consumption Modeling ofDifferent Base Station Types in Heterogeneous CellularNetworks,” Future Network and MobileSummit 2010Conf. Proc., June 2010.

[12] K. Ponnambalam et al., “Comparison of Methods forBattery Capacity Design in Renewable Energy Systemsfor Constant Demand and Uncertain Supply,” Proc. IEEEInt’l. Conf. European Energy Market (EEM), June 2010,pp. 1–5.

[13] F. J. Heredia, M. J. Rider, and C. Corchero, “OptimalBidding Strategies for Thermal and Generic Program-ming Units in the Day-Ahead Electricity Market,” IEEETrans. Power Systems, vol. 25, no. 3, Aug. 2010, pp.1504–18.

[14] B. M. Epstein and M. Schwartz, “Predictive QoS-basedAdmission Control for Multiclass Traffic in CellularWireless Networks,” IEEE JSAC, vol. 18, no. 3, Mar.2000, pp. 523–34.

[15] J. R. Birge and F. Louveaux, Introduction to StochasticProgramming, Springer, July 1997.

[16] Y. Fang and Y. Zhang, “Call Admission ControlSchemes and Performance Analysis in Wireless MobileNetworks,” IEEE Trans. Vehic. Tech., vol. 51, no. 2, Mar.2002, pp. 371–82.

BIOGRAPHIESDUSIT NIYATO [M’09] (dniyato@ntu.edu.sg) is currently anassistant professor in the School of Computer Engineering,at the Nanyang Technological University, Singapore. Heobtained his Bachelor of Engineering in Computer Engi-neering from King Mongkut‘s Institute of Technology Lad-krabang (KMITL), Bangkok, Thailand. He received Ph.D. inElectrical and Computer Engineering from the University ofManitoba, Canada. His research interests are in the area ofradio resource management in cognitive radio networksand broadband wireless access networks.

XIAO LU (luxiao@ntu.edu.sg) received a B.Eng. degree incommunication engineering from Beijing University ofPosts and Telecommunications in 2008, and an M.Eng.degree in computer engineering from Nanyang Technologi-cal University in 2010. He is currently a research associatewith the School of Computer Engineering, Nangyang Tech-nological University. His current research interests focus onapplications of game theory in wireless networks and vehi-cle-to-grid systems.

PING WANG [M’08] (wangping@ntu.edu.sg) received herPh.D. degree in electrical engineering from the Universityof Waterloo, Canada, in 2008. She is currently an assistantprofessor in the School of Computer Engineering, NanyangTechnological University, Singapore. Her current researchinterests include QoS provisioning and resource allocationin multimedia wireless communications. She was a corecip-ient of a Best Paper Award from the 2007 IEEE Internation-al Conference on Communications.

It is clear that theproposed adaptive

power managementcan minimize the

cost of power consumption given

various uncertainties,including renewable

power generation,power price, andtraffic load of the

wireless base station.

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