Priority-Based Dynamic Spectrum Management in a Smart Grid Network Environment

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSAC.2014.2361088, IEEE Journal on Selected Areas in Communications

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Priority-Based Dynamic Spectrum Management in aSmart Grid Network Environment

Zhiyong Feng∗, Qian Li∗, Wei Li†, T. Aaron Gulliver†, Ping Zhang∗∗Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and

Telecommunications, Beijing, P. R. China 100876†Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC Canada V8W 2Y2

Abstract—The heterogeneous Smart Grid (SG) poses twomajor challenges for wireless networks, namely providing suf-ficient bandwidth for a wide variety of applications and highreliability for critical real-time applications. To address thesechallenges, the impact of communication outage on the DemandResponse Management (DRM) as a typical SG application isanalyzed in this paper. A Dynamic Spectrum Management (DSM)technique is proposed to allocate resources considering the QoSand application priorities. Vacant Digital TV (DTV) frequencybands are utilized to support SG applications. An algorithm toestimate the SG capacity is introduced which can be applied tovarious user distributions and SG environments. This is used inconjunction with a low complexity coloring theory algorithm toallocate the spectrum. Results are presented that show that DSMprovides better performance than traditional fixed spectrummanagement (FSM) in terms of QoS and secondary spectrumutilization.

Index Terms—Smart Grid; DSM; DRM; Heterogeneous Net-works.

I. INTRODUCTION

The traditional electrical utility industry is undergoing amajor transformation that will enable demand management,reliability gains, resource efficiency, customer participation,and cleaner energy across the electrical grid [1]. This trans-formation is being facilitated by the development of so-calledSmart Grid (SG) applications such as Demand Response Man-agement (DRM), the Supervisory Control and Data Acquisi-tion (SCADA) system, the Advanced Metering Infrastructure(AMI), and emergency response [2].

Smart grid applications have different Quality-of-Service(QoS) requirements and specifications in terms of reliabilityand communication delay [3] [4]. For example, the SCADAsystem needs to support time-critical applications, which aremainly for monitoring the status of transmission lines, sub-station equipment and other key components [5]. In contrast,AMI smart meter data can be delayed by minutes or evenhours before reaching the operation center [6]. Further, recentfield tests reveal that wireless links in the smart grid havehigher packet error rates and variable link capacities due tothe effects of dynamic topology changes, obstructions, andfading [4] [7]. This leads to both time and location dependentdelay and capacity variations on the wireless links. Thus,it is crucial that the communications networks supporting

Manuscript received Jan 5, 2014,revised May 8, 2014 and Jul 18, 2014.

heterogeneous SG applications be properly designed for theseadverse transmission conditions [8].

Although cellular networks are a promising communicationtechnology for SG applications due to the available data rates,coverage area, range, security, and delay performance [2],there remain two key challenges. First, the cellular networksshould provide sufficient guaranteed bandwidth for the SGbecause of the large volume of data traffic from numerouspower grid units [3]. However, the available cellular bandwidthcan be scarce according to the FCC [9]. As an alternative, TVwhite space can be used via Cognitive Radio (CR) techniques[10] [11] [37]. TV service characteristics (e.g. people usuallywatch TV between 7 PM and 11 PM), show that there isample spectrum available at certain times. Further, the transi-tion to Digital TV (DTV) will release significant bandwidthfor other uses. As a consequence, a CR based hierarchicalcommunications architecture for the SG was proposed in [11]that dynamically utilizes TV white space. Second, networkcongestion can occur and decrease network reliability becausecellular networks are shared by many mobile users. Thiscongestion can be severe during critical applications (e.g.emergency response). To avoid this, traffic scheduling can beprioritized to free bandwidth for critical applications [12] andenhance data transmission reliability.

In this paper, the influence of communication reliability onDemand Response Management (DRM) is analyzed as it is atypical SG application. Based on the reliability requirements, aDynamic Spectrum Management (DSM) technique is proposedto allocate frequency resources to heterogeneous SG applica-tions considering their priorities. DTV spectrum is dynam-ically used to create more spectrum utilization opportunitiesand limit the interference to the DTV system. In addition, sincetechnologies such as CR are employed to improve spectrumuse [11] [13], DSM should also minimize the number ofallocated frequency bands so that unoccupied spectrum canbe used by CR based SG applications [37]. An algorithmfor estimating SG network capacity is introduced that can beemployed with various user distributions and hybrid servicescenarios. These capacities are used to allocate spectrum via acoloring theory algorithm to reduce the complexity. Simulationresults are presented which verify that the proposed approachcan satisfy the QoS requirements of SG applications andprovide a reliable network for critical real-time applications. Inaddition, more secondary spectrum opportunities are createdcompared to traditional Fixed Spectrum Management (FSM).



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II. LITERATURE REVIEW

A. Demand Response Management

Although SG applications have specific communicationand QoS requirements, results concerning the communicationreliability of Demand Response Management (DRM) arelimited. DRM is an important function in Smart Grid (SG)energy management as it can reduce peak power consumption.Recent interest in DRM has been directed towards real-time interaction with customers through smart meters. Smartmeters act on behalf of consumers to make the best useof the available power based on energy market prices [14].Therefore, by setting proper real-time price and incentives,consumers can be motivated to make energy consumptionbeneficial to both energy providers and themselves. SeveralDRM techniques have been developed in an attempt to reduceand shift peak loads [15]-[19]. A distributed iterative algorithmwas proposed in [15] to balance and shape the real-time load.In [16], the optimal price under different load uncertainties wasanalyzed. Game theory was used in [17] to provide DRM viaprice prediction and energy consumption scheduling. However,perfect two-way communications was assumed, so the impactof communication unreliability on DRM performance was notexamined. Although the authors in [18] investigated the impactof communication impairments on a DR control algorithm,only experimental results were presented with no analysis.

In [19], the relationship between communication outage andDRM performance was analyzed, but the load uncertainty dueto outage was not considered, and only one consumer wasexamined in the simulation results presented. Conversely, theload uncertainty due to communication outage is consideredin this paper, and the relationship between outage and DRMperformance with multiple energy consumers is derived. Dif-ferent from our previous work [13], the load uncertainty ismodeled in a more realistic and useful way using bounded,Gaussian, and unknown models.

B. Smart Grid Wireless Resource Management

The smart grid requires high Quality of Service (QoS)and resource efficiency. There have been several results onSG wireless resource management. In [20], an SG HomeArea Network (HAN) infrastructure was proposed based onnext generation heterogeneous wireless systems. However, thecorresponding spectrum management was not analyzed. Acentralized resource scheduling scheme in the context of SGheterogeneous networks was proposed in [21], but the RadioAccess Technologies (RATs) were not specified. A queuingmodel based algorithm that dynamically allocates traffic withdifferent QoS requirements in terms of throughput, delay andfailure probability was presented in [22], but spectrum effi-ciency issues were not considered. In [23], a dynamic spectrumallocation scheme for power load prediction was introducedto reduce communication costs, however multiple serviceswere not examined. A hybrid spectrum access technique wasproposed in [24] to enhance spectrum efficiency, but the QoSrequirements of SG applications were not considered. Theauthors in [12] proposed a prioritized SG traffic schedulingscheme, but the spectrum allocation issue was not adequately

addressed. We proposed a dynamic spectrum managementframework in [25], but the heterogeneity of the SG applica-tions was not considered, i.e., the QoS of these applicationswas not included. In our previous work [13], a joint spatialand temporal spectrum sharing technique was proposed toenhance the spectrum sharing opportunities and thus increasethe communication reliability for DRM. However, the focuswas on only a single SG application (i.e., DRM), whereasmultiple SG applications are considered in this paper. Further,spectrum sharing and allocation between the SG and DTVnetworks was not considered in [13], which is the mainconsideration here.

III. DEMAND RESPONSE MANAGEMENT

DRM allows consumers to adjust their energy consumptionbehavior based on real-time pricing. To be specific, in eachtime slot, the energy provider first broadcasts the real-timeelectricity price λ to all consumers. According to the priceλ, consumers report their energy demands xi to the energyprovider. The energy provider then decides its energy supplyL. This reflects how a free and open energy market wouldwork in practice. Note that the energy demand xi has a mini-mum value xmini that represents the load from the appliancesof consumer i that must always be on. xi also has a maximumvalue xmaxi that represents the total energy consumption ifall appliances are on. Consumer i decides its energy demandaccording to the real-time electricity price λ, but this is limitedto [xmini , xmaxi ].

We consider a smart grid consisting of one energy providerand N0 energy consumers. The provider has a control centerfor DRM. Each consumer is equipped with a smart meterconnected to the control center through a Neighbourhood AreaNetwork (NAN). Consumers act independently and there is nocommunications among them.

For the energy provider with a price λ, the benefit insupplying L power units is given by λL−C(L) where C(L)is a cost function that is assumed to be quadratic [15], i.e.,C(L) = a0L

2 + b0L + c0 with predetermined constants a0,b0, and c0. The goal is to adjust the supply L to maximize itsbenefit

maxL

λL− C(L). (1)

For consumer i under electricity price λ, the benefit withenergy demand xi is Ui(xi, ωi)− λxi where Ui(xi, ωi) is theutility function defined as [15]

Ui(xi, ωi) =

ωi xi − α0

2 x2i 0 ≤ xi ≤ ωi

α0ω2i

2α0xi ≥ ωi

α0

, (2)

where ωi is a parameter characterizing the behavior of con-sumer i. A higher value of wi results in an increase inUi(xi, wi), which means that user i is easier to satisfy withthe same energy demand xi. In practice, ωi can be obtained byexamining the benefit of various appliances at given times anddays. For example, heating is more important during the night,and is less important in the summer. The benefit is relative,and also subjective, but a set of criteria can be establishedto obtain suitable values. α0 is a constant characterizing the



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saturation point of the utility. The higher the value of α0,the lower the power consumption required to reach this point.Note that the utility function represents the level of satisfactionobtained by the user as a function of its energy consumption.Further, this utility function is non-decreasing, so users willwant to consume more power if possible until they reachtheir maximum consumption level. There have been severalutility models proposed for consumers as the satisfactionderived in consuming energy is an abstract concept. The modelemployed here is widely accepted [15] [16], and is appropriateto illustrate the demand response concepts.

The two-way communications between consumer i and thecontrol center suffers from outage with probability ζ. Thepower demand from consumer i received at the control centeris then (1−ζ)xi+ζδi, where δi is the load uncertainty due tooutage within a time slot, and is assumed to be a zero-meanrandom variable with variance σ2

δ . The utility in the presenceof load uncertainty is thus a random quantity, so the expectedutility of consumer i can be expressed as

Ui(xi, ωi) =

ωi (1− ζ)xi − α0

2 ζ2σ2δ

−α0

2 (1− ζ)2x2i , 0 ≤ xi ≤ ω

α0ω2

2α0−α0

2 ζ2σ2δ , xi ≥ ω

α0

.

(3)The goal of consumer i is to adjust its energy demand xi tomaximize its benefit

maxxi

Ui(xi, ωi)− λxi. (4)

From an overall perspective, it is desirable to minimize theprovider cost and maximize the aggregate satisfaction of theconsumers [16]. Considering this as the goal of DRM leadsto the following optimization problem

maxL,xi

N0∑i=1

Ui(xi, ωi)− C(L), (5)

s.t.

N0∑i=1

((1− ζ)xi + ζδi) ≤ L. (6)

Bounded uncertainty has long been used in modeling adynamic control system [26]. According to our previous work[13], the optimization problem constraint can be rewritten asfollows given that the load uncertainty has maximum ∆

s.t. (1− ζ)

N0∑i=1

xi ≤ L− ζN0∆ , Lboundede . (7)

A Gaussian distribution has been used to model load uncer-tainty [27] [28]. Here, it is assumed that the δi are independentand identically distributed Gaussian random variables withzero mean and variance σ2

δ . Further, the energy provider isassumed to know this mean and variance. Since a Gaussianrandom variable is unbounded, we are interested in the proba-bilistic constraint on the total power consumption. The energyprovider guarantees that the probability of the total powerconsumption exceeding the generated capacity by a specified

threshold will be smaller than some small positive number.This gives the constraint

s.t.Pr

[N0∑i=1

((1− ζ)xi + ζδi)− L ≥ η

]≤ ε, (8)

where η is a threshold on the total power consumption, andε is a small positive value, which can be interpreted as anoutage probability.

The well-known tail probability of a Gaussian distributionis now used to rewrite the probabilistic constraint as a deter-ministic constraint. First, the LHS of (8) is manipulated togive

Pr

[N0∑i=1

((1− ζ)xi + ζδi)− L ≥ η

]

= Pr

N0∑i=1

δi ≥η + L− (1− ζ)

N0∑i=1

xi

ζ

= Pr

N0∑i=1

δi√N0σδ

≥η + L− (1− ζ)

N0∑i=1

xi

ζ√N0σδ

= Q

η + L− (1− ζ)

N0∑i=1

xi

ζ√N0σδ

≤ ε,

(9)

which is equivalent to

(1− ζ)

N0∑i=1

xi ≤ L+ η − ζ√N0Q

−1 (ε) , LGaussiane . (10)

A more general case than bounded or Gaussian uncertaintyis when the distribution of δi is unknown [16]. We assumethat the distribution of δi is symmetric about its mean, whichreflects an equal chance of deviation in the positive andnegative directions. Then, the constraint in (8) is

Pr

[N0∑i=1

((1− ζ)xi + ζδi)− L ≥ η

]

= Pr

[ζ

N0∑i=1

δi ≥ η + L− (1− ζ)

N0∑i=1

xi

]

=1

2Pr

∣∣∣∣∣N0∑i=1

δi

∣∣∣∣∣ ≥η + L− (1− ζ)

N0∑i=1

xi

ζ

≤ 1

2

N0σ2δζ

2(η + L− (1− ζ)

N0∑i=1

xi

)2 ≤ ε,

(11)

which is equivalent to

(1− ζ)

N0∑i=1

xi ≤ η + L−√N0

2εζσδ , Lunknown

e . (12)



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The objective functions in (1) and (4) are quadratic, soexplicit solutions for L∗ and x∗i can be obtained by settingthe first derivatives of these functions to zero giving

L∗ =λ− b0

2a0,

x∗i =ωi − λ

(1− ζ)α0. (13)

Then L∗ and x∗i are substituted into the following Lagrangian[13]∑i∈N0

Ui(xi, ωi)− C(L)− λ

(∑i∈N0

(1− ζi)xi − Le

), (14)

where Le is Lboundede , LGaussian

e or Lunknowne according to the

specific uncertainty model. Setting the first derivative equalto zero, the optimal power price under bounded uncertainty,Gaussian uncertainty and unknown uncertainty, respectively,is given by

λ∗bounded =

N0∑i=1

ωi/α0 + b0/2a0 + ζN0∆

1/2a0 +N0/α0, (15)

λ∗Gaussian =

N0∑i=1

ωi/α0 + b0/2a0 + ζσδ√N0Q

−1 (ε)− η

1/2a0 +N/α0,

(16)

λ∗unknown =

N0∑i=1

ωi/α0 + b0/2a0 +√

N0

2ε σδζ − η

1/2a0 +N/α0. (17)

The optimal power provider benefit is then

B∗P =1

4a0(λ∗ − b0)

2 − c0, (18)

and the optimal benefit for power consumer i is

B∗U =1

2α0(λ∗ − ωi)2 − α0

2ζ2σ2

δ . (19)

The optimal DRM performance is then

B∗D =∑i∈N0

ω2i − (λ∗)

2

2α0− α0

2ζ2σ2

δN0

− (λ∗)2 − b20

4a0− c0, (20)

where λ∗ can be λ∗bounded, λ∗Gaussian, or λ∗unknown dependingon the load uncertainty model employed. In [13], it was shownthat the two-way communications outage increases with powerprice, but it may not decrease the benefit of the power providerand consumers. Further, DRM performance is reduced with anincrease in outage.

Figures 1-3 show the influence of the communication outageprobability on Demand Response Management (DRM). Thecorresponding simulation parameters are α = 0.5, a0 =0.01, b0 = 5, c0 = 0, σ2

δ = 1, and ω follows a uniformdistribution ω ∼ U [1, 4]. For the bounded load uncertainty

model, ∆ = 0.1. For the Gaussian and unknown models,the parameters are η = 0.1, ε = 0.001, and σδ = 0.1. Asshown in Fig. 1, the electricity price increases with increasingcommunication outage. In addition, the electricity price withan unknown distribution is higher than with the bounded andGaussian load uncertainty models. At a given outage (e.g.ζ = 0.3) in Fig. 2, the electricity price increases as the numberof energy consumers N0 rises, but it increases only slightlywhen N0 is large. The corresponding DRM performance isshown in Fig. 3. While the DRM performance generallydeclines as the outage probability increases, it decreases fastestwith the unknown distribution model. This indicates thatcommunication outage has a greater impact with this model.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11.99

2

2.01

2.02

2.03

2.04

2.05

2.06

2.07

2.08

2.09

Communication Outage Probability

Ele

ctr

icity P

rice

Electricity Price, Bounded Load Uncertainty

Electricity Price, Gaussian Load Uncertainty

Electricity Price, Unknown Distribution

Fig. 1. Electricity price versus communication outage with N0 = 100.

0 20 40 60 80 100 120 140 160 180 2000

0.5

1

1.5

2

2.5

Number of Consmers, N0

Ele

ctr

icity P

rice

Electricity Price, Bounded Load Uncertainty

Electricity Price, Gaussian Load Uncertainty

Electricity Price, Unknown Distribution

Fig. 2. Electricity price versus the number of consumers with ζ = 0.3.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1210

220

230

240

250

260

270

280

290

Communication Outage Probability

DR

M P

erf

orm

ance

DRM Performance, Bounded Load Uncertainty

DRM Performance, Gaussian Load Uncertainty

DRM Performance, Unknown Distribution

Fig. 3. DRM performance versus communication outage.



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IV. DYNAMIC SPECTRUM MANAGEMENT (DSM)

The analysis in the previous section shows that good DRMperformance relies on a low communication outage proba-bility. However, DRM in a real SG environment must beconducted in conjunction with other SG applications. Theresulting data traffic may result in network congestion anddrastically decrease network reliability. Therefore, we firstpropose a heterogeneous network where the SG is overlaidwith a DTV system in order to utilize TV white space viaa new approach to dynamic spectrum management (DSM).This provides more frequency bands for DSM so that networkoutage can be reduced. Further, DSM can differentiate SGapplications according to their QoS requirements in order tosupport critical SG applications and guarantee DRM perfor-mance.

A. System Architecture

The focus here is on the SG Neighbourhood Area Network(SG-NAN) that connects multiple smart meters to local accesspoints, and is overlaid with a DTV system, as illustrated inFig. 4. The SG-NAN smart meters are assumed to employWCDMA as the communications technology because it iswidely used in 3G systems, and WCDMA based solutions andproducts have been employed in the smart grid. In [29], anAMI architecture was proposed and evaluated with clusters ofsmart meters transferring data via a UMTS network to a server.The performance was evaluated based on network throughputand latency. A CDMA smart grid solution for the residentialutility market was introduced by Verizon that uses their 3GCDMA network as the backbone with SmartSynch smart gridsolutions [30]. An HSDPA based smart meter product has alsobeen developed [31].

The popular point-to-multipoint wireless communicationsystem Digital Video Broadcasting (DVB) is used as theDTV infrastructure. However, our results can be applied toother environments with different features where spectrumsharing can be used. In addition, we assume that the SG-NANand DVB systems have a common operator and overlappingcoverage areas.

There are KS WCDMA cells utilized by the SG-NAN,denoted as SG-NANi with Base Station SG-BSi, each ofwhich serves a cluster of SG nodes (i.e., smart meters). Thereare KD DVB cells which serve TV subscribers located withintheir coverage areas. The total number of SG-NAN and DVBcells is then K = KD+KS . We assume the first KD cells arefor DVB, and the latter KS are for SG-NAN. The carriers forSG-NAN and DVB are denoted as m, where 1 ≤ m ≤M , andM is the number of carriers in both systems. M = MD+MS

where the first MD are DVB carriers and the remaining MS

are SG-NAN carriers. Further, we assume that the first MS/2carriers are for uplink transmission while the other MS/2carriers are for the downlink. The uplink and downlink donot interfere with each other due to the effective spectrumseparation. In a commercial network, due to the DTV servicecharacteristics, DVB is more likely to have unused spectrumresources to share than a cellular system (e.g. WCDMA).

Fig. 4. The system model.

The objective of Dynamic Spectrum Management (DSM) isto appropriately allocate the spectrum resources (fm), to SG-NANk. According to the system architecture, SG nodes areallocated spectrum according to their specific application QoSrequirements, and can utilize the DVB spectrum when it is notin use for DTV services. Therefore, each SG-NAN cell has 2M

possible allocations while every DVB cell has only 2MD allo-cations. The objective is to choose the best allocation amongthese choices to meet the current QoS requirements of SGapplications, while ensuring DVB performance is maintained.

The proposed DSM is implemented in three steps. First, theSG-NAN network capacity is estimated based on the hetero-geneous SG application QoS requirements. Second, protectioncriteria for the DVB system, such as interference constraintsand DVB cell capacity demands, are derived. Finally, a pri-ority based dynamic spectrum allocation is determined usingcoloring theory.

V. SG-NAN CAPACITY ESTIMATION FORHETEROGENEOUS APPLICATION REQUIREMENTS

To allocate spectrum appropriately, the SG-NAN capacityrequirements must be determined. Define the downlink capac-ity on carrier m as ndi,m, which is used to support the downlinktransmission of control messages such as the electricity pricein DRM. Since for most smart grid applications, such as meterreading, the transmission from the smart meters to the SG basestation (uplink transmission) constitutes the majority of thetraffic, the uplink capacity nui,m must also be considered. Notethat the capacity represents the number of supported users ineach SG-NAN cell.

We assume the total power of an SG-NAN Base Station(BS) on carrier m is PSm, the power of the DVB station isPDm , and the maximum power levels of all SG node are setto one value PS,Nm,max for convenience, but they can be set todifferent levels in practice. It is also assumed that SG-BSi



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provides T applications (services) in area Si, and the densitydistribution of the smart meters with the t-th (1 ≤ t ≤ T )application on carrier m is wti,m (s). Let wi (s) and ηti bethe normalized density function and occupation ratio betweendifferent SG applications, respectively. Therefore we havewti,m (s) = ηtiwi (s). Since WCDMA is an interference limitedsystem, the downlink and uplink capacities are derived basedon the interference analysis.

A. Downlink Interference Analysis

For an SG node Ni,m(x, y) at position (x, y) with applica-tion t on carrier m, the downlink interference has four compo-nents. Idintra (x, y) and Idinter (x, y) are the downlink intra-celland inter-cell interference from the SG-NAN, respectively, andIdD CO (x, y) and IdD AC (x, y) are the downlink co-channeland adjacent channel interference to SG node Ni,m(x, y) fromthe DVB system, respectively. To simplify the calculations,Gaussian noise is ignored. This is reasonable given that thesystem is interference limited. The four interference terms aregiven below.

IdIntra (x, y) = αDPSm − P

t,di,m (x, y)

LS,di,m (x, y), (21)

where αD is the orthogonality coefficient between carriers.

IdInter (x, y) =

KS∑k 6=i

ϕki,mPSm

LS,dki,m (x, y), (22)

where LS,dki,m (x, y) is the path loss from SG-BSk to SGnode Ni,m(x, y) on carrier m, and ϕki,m is the correlationcoefficient between SG-BSi and SG-BSk on carrier m. WhenSG-BSi is assigned the same carrier as SG-BSk, ϕki,m is 1,otherwise it is 0.

IdD CO (x, y) =

KD∑k=1

ρki,mPDm

LD,dk,m (x, y), (23)

where LD,dk,m (x, y) is the path loss from base station DVBk toSG node Ni,m(x, y) on carrier m, and ρki,m is the correlationcoefficient between SG-BSi and DVBk on carrier m. WhenSG-BSi is assigned the same carrier as DVBk, ρki,m is 1,otherwise it is 0.

IdD AC (x, y) =

KD∑k=1

ρ+ki,m

PIRSPDm

LD,dk,m−1 (x, y)

+

KD∑k=1

ρ−ki,mPIRSP

Dm

LD,dk,m+1 (x, y), (24)

where PIRS is the ratio of the power of an adjacent DVBchannel leaking to an SG-NAN channel to the total power ofthis DVB channel, LD,dk,m+1 (x, y) and LD,dk,m−1 (x, y) are thepath losses from DVBk to SG node Ni,m(x, y) on carriersm+ 1 and m− 1, respectively, and ρ+

ki,m (ρ−ki,m) is the up(down) correlation coefficient between DVBk and SG-BSion carrier m. If SG-BSi is assigned carrier m and DVBkis assigned m+ 1 (m− 1), this coefficient is 1, otherwise itis 0.

B. SG-NAN Downlink Capacity Estimation

Since the path loss differs depending on the carrier, thetransmit power must be varied to obtain the desired coverage.For SG node Ni,m(x, y), the carrier-to-interference ratio (CIR)is [21] (

C

I

)dt

=W

νtRt

P t,di,m (x, y)

LSi,m (x, y)/(IdIntra (x, y) +

IdInter (x, y) + IdD CO (x, y) + IdD AC (x, y)),

(25)

where W is the bandwidth assumed to be 3.84 MHz, Rt is thedata rate of service t, νt is the activation factor of applicationt, P t,di,m (x, y) is the power allocated by SG-BSi to Ni,m(x, y),and LS,di,m (x, y) is the path loss from SG-BSi to Ni,m(x, y).Substituting (21), (22), (23) and (24) into (25), the powerallocated to SG node Ni,m(x, y) is

P t,di,m(x, y) = θtPSm +

θtαD

PSm

K∑k=1

ϕki,mLS,di,m(x, y)

LS,dk,m(x, y)

+θtαD

PDm

KD∑k=1

ρki,mLS,di,fm(x, y)

LDki,m(x, y)

+ζtαD

PDm

KD∑k=1

(ρ+ki,m + ρ−ki,m)

LS,di,m(x, y)

LD,dki,m(x, y),

(26)

where θt and ζt are service-related coefficients given by

θt =(

1 +W/(αD(C/I)

dt νtRt

))−1

, (27)

andςt = PIRS

(1 +W/

(αD(C/I)

dt νtRt

))−1

. (28)

Since the power of SG-BSi, PSm, is allocated to smart meterswith different services as well as the control channel, we have

PSm = (1− ξ)PSm +T∑t=1

[∫∫Si

P t,di,m(x, y)ndi,mwti,m(s)ds

],

(29)where ξ is the fraction of the power used for payload trans-mission from the total power of SG-BSi. Substituting (26) into(29), the downlink capacity of SG-NANi on carrier m is then

ndi,m = ξPSm

/T∑t=1

[PSmθtη

ti +

θtPSmη

ti

αD

K∑k=1

ϕki,mχmki

+PDm η

ti

αD

KD∑k=1

ρki,mθtψmki

+PDm η

ti

αD

KD∑k=1

(ςtρ

+ki,m + ςtρ

−ki,m

)ψmki

], (30)

where χmki is the downlink interference from SG-BSk to SG-NANi when they use the same carrier m and is given by

χmki =

∫∫Si

LSi,m(x, y)

LSki,m(x, y)wi(s)ds. (31)



7

The downlink interference from DVBk to SG-NANi is

ψmki =

∫∫Si

LSi,m(x, y)

LDki,m(x, y)wi(s)ds. (32)

The total downlink capacity of SG-NANi is then the sum

ndi =

M∑m=1

ndi,m. (33)

C. Uplink Interference Analysis

It is assumed that SG-NANi employs ideal uplink powercontrol for each SG node Ni,m(x, y) [32], and the transmitpower of each node satisfies the minimum CIR for successfulreception at SG-BSi. Therefore, the SG node signals arrivingat SG-BSi have the same CIR, i.e. the received signal powerfrom each SG node has the same value P reci .

For the SG-BSi, the uplink interference also has fourcomponents. Iuintra is the uplink intra-cell interference fromother SG nodes in SG-NANi, Iuinter is the uplink inter-cellinterference from the SG nodes in SG-NANk(∀k 6= i).IdD CO (x, y) and IdD AC (x, y) are the uplink co-channel andadjacent channel interference to SG-BSi from the DVB sys-tem, respectively. These four interference terms are givenbelow.

Iuintra = (nui,m − 1)Pirec, (34)

where nui,m is the uplink capacity, which will be introducedin the next subsection.

Iuinter =

KS∑k 6=i

ϕki,mγmki , (35)

where γmki is the interference from all the SG nodes in SG-NANk on carrier m to a BS or user in cell i, and γmki is definedas

γmki =

T∑t=1

∫∫Sk

PS,Nk,m (x, y)

LS,uki,m(x, y)nuk,mw

tk,m(s)ds (36)

where LS,uki,m(x, y) is the path loss from SG nodes Nk,m(x, y)

in SG-NANk to SG-BSi, and PS,Nk,m (x, y) is the transmissionpower of Nk,m(x, y).

IuD CO =

KD∑k=1

ρki,mPDm

LD,uki,m

, (37)

where LD,uki,m is the path loss from DVBk to SG-BSi.

IuD AC =

KD∑k=1

(ρ+ki,m + ρ−ki,m

) PIRSPDm

LD,uki,m

. (38)

Algorithm 1 SG-NAN uplink capacity estimation algorithm1: Initialize nui,m = 0.2: Add a new SG node following the distribution wti,m. Using

ideal uplink power control [32], calculate the receivedpower from the SG nodes P reci at SG-BSi, and thetransmission power PS,Ni,m (x, y) of each SG node.

3: If there are nodes that reach the maximum transmissionpower, i.e., PS,Ni,m (x, y) = PS,Nm,max, terminate the algo-rithm. Otherwise, let nui,m = nui,m + 1 and return to Step2.

D. SG-NAN Uplink Capacity Estimation

The uplink capacity nui,m is estimated using Algorithm 1.The CIR on SG-BSi is then obtained as(

C

I

)u=

P reciWvtRt

Iuintra + Iuinter + IuD CO + IuD AC

. (39)

Substituting (34), (35), (37) and (38) into (39), the uplinkcapacity nui,m is

nui,m =W

vtRt (C/I)u −

1

P reci

KS∑k 6=i

ϕki,mγki

+

KD∑k=1

ρki,mPDm

LD,uki,m

+

KD∑k=1

(ρ+ki,m + ρ−ki,m

) PIRSPDm

LD,uki,m

]+ 1. (40)

Similar to the downlink, the total uplink capacity of SG-NANiis the sum

nui =

M∑m=1

nui,m (41)

VI. DVB SYSTEM PROTECTION AGAINST INTERFERENCE

A. DVB System Capacity

The arrival rates λi of the DVB service requests are assumedto follow a Poisson distribution [25]. In a DVB system, onlythose users interested in the service at a particular time willaccess it. The choice of a Poisson model comes from the factthat, as widely reported in the literature and as mentioned in[33], the frequency that a human decides to use the network fora specific task is well described using Poisson processes. Letthe number of carriers allocated to DVBi be FD,i. Each carriercan provide π channels, giving πFD,i DVB channels for basestation DVBi. We also assume that the average service timeis µ, and the required quality of service of the DVB networkis QoSDVB. To guarantee QoSDVB, assuming that the πFD,iDVB carriers can support an average service rate of λi, basedon the Erlang B formula, λi should satisfy

1−QoSDVB =(λi/µ)

πFD,i(πFDi

) /

πFD,i∑k=0

(λi/µ)k

k!, (42)

so that λi can be obtained by solving (42).



8

B. Interference Constraints

DVB is a point-to-multipoint broadcast system where allusers have a minimum CIR requirement. Thus, the capacityestimation can be based on the assumption that this minimumhas been satisfied. To guarantee DVB system performance,DVB and SG-NAN interference on the same and adjacentcarriers must be considered.

If in the spectrum allocation solution, carrier m is assignedto DVBi, to ensure adequate performance on this carrierinterference from other DVB cells using the same carrier mustbe considered, as well as SG-NAN interference from the sameand adjacent carriers. Define a worst-case user as one at theintersection of the cell edge of DVBi and the segment thatconnects DVBi

and the interfering cell. Since it is closest to the interferingcell, it suffers from the most severe interference. Therefore,whether DVBi and DVBk can use the same carrier dependson the following three constraints.

PDi,m/LDi,m

PDk,m/LDki,m

− CIRD ≥ 0, (43)

where LDi,m is the path loss from base station DVBi to theworst-case user, LDki,m is the path loss from base station DVBkto the worst-case user, and CIRD is the minimum DVB inter-cell co-frequency interference threshold.

Using similar arguments as above, SG-NANk and DVBiusing the same carriers should conform to

PDi,m/LDi,m

PSk,m/LS,dki,m + γmki

− CIRSCO≥ 0, (44)

where LDi,m is the path loss from SG-BSk to the worst-caseuser, and γmki is the interference from all SG nodes in SG-NANk on carrier m to the worst-case user in DVBi given by(36), and CIRSCO

is the minimum co-frequency interferencethreshold of SG-BSk to DVBi on the same carrier.

Finally, the adjacent frequency interference from SG-BSkusing carrier m− 1 or m+ 1 to DVBi using carrier m mustsatisfy

PDi,m/LDi,m

PIRD

(PSk,m/L

S,dki,m + γki

) − CIRSAC≥ 0, (45)

where PIRD is the ratio of the power of an adjacent SG-NANcarrier leaking to a DVB carrier to the total power on this SG-NAN carrier, and CIRSAC

is the corresponding interferencethreshold.

The interference thresholds CIRD, CIRSCOand CIRSAC

are determined by the operator according to network re-quirements using the same principles as for static spectrummanagement.

VII. PRIORITY BASED DYNAMIC SPECTRUM ALLOCATION

A. DSM Modeling

Based on the estimated SG-NAN and DVB capacities, theDSM algorithm determines the dynamic spectrum allocation.The DSM parameters are described below [34].

Spectrum availability matrix: L = lk,m|lk,m ∈ 0, 1 isa K×M binary matrix representing the spectrum availability.lk,m = 1 if and only if carrier m is available at SG-BSk orDVBk.

Interference constraint matrix: C =ck,j,m,n|ck,j,m,n ∈ 0, 1 is a K × K × M × M matrixrepresenting the interference constraints among cells. Ifck,j,m,n = 1, cells k and j will interfere with each other ifcell k uses carrier m and cell j uses carrier n. To be specific,ck,j,m,n = 1 in the following three situations:

(1) Two DVB cells (1 ≤ k ≤ KD, 1 ≤ j ≤ KD) usethe same carrier (1 ≤ m,n ≤ MD,m = n), and theyinterfere with each other.

(2) A DVB cell and an SG-NAN cell (1 ≤ k ≤ KD,KD +1 ≤ j ≤ K) use the same carrier (1 ≤ m,n ≤MD,m =n), and they interfere with each other.

(3) A DVB cell and an SG-NAN cell (1 ≤ k ≤ KD,KD +1 ≤ j ≤ K) use adjacent carriers m, n (1 ≤ m,n ≤MD,m = n±1), and there is interference between them.

Spectrum assignment matrix: A =ak,m|ak,m ∈ 0, 1, ak,m ≤ lk,m is a K × M matrixwhere ak,m = 1 if carrier m is assigned to cell k. Thisassignment must satisfy all interference constraints definedby C, that is, ak,m + aj,n ≤ 1 if ck,j,m = 1, where1 ≤ k, j ≤ K, 1 ≤ m ≤ M . This matrix is updated eachiteration of the algorithm.

Spectrum allocation set: Ω. As mentioned in Section III,there are 2MKS+MDKD possible allocations. Allocation Ω isdirectly related to the assignment matrix A.

Spectrum rewards matrix: R = rk,m, is a K ×M matrixof spectrum rewards defined as

rk,m =

λk,m, 1 ≤ k ≤ KD

nk,m,KD + 1 ≤ k ≤ K . (46)

This represents the increase in the capacity of cell k whencarrier m is chosen. Here λk,m represents the capacity that canbe acquired by DVBk, while nk,m represents the capacity thatcan be obtained by SG-NANk. Note that nk,m can be eitherthe downlink capacity ndk,m or the uplink capacity nuk,m.

Cell reward vector: R = rk|rk =M∑m=1

ak,m · rk,m is a

length K vector that represents the SG-NAN or DVB cellreward for a given spectrum assignment.

Application priority matrix: P =

αk,t|T∑t=1

αk,t = 1, 0 ≤ αk,t ≤ 1 is a K × T matrix

where αk,t is the priority of application/service t in cell k. Ahigher value of αk,t indicates a greater priority for applicationt. If there is only one application in cell k (i.e., T = 1),αk,t = 1.

Objective function: f(Ω) represents the performance witha specific spectrum allocation Ω. Since most SG applications(and in particular real-time applications), require significantspectrum resources, the objective of DSM is to satisfy theheterogeneous SG application requirements and also minimizethe number of allocated carriers (i.e., reserve as many carriersas possible for SG secondary spectrum use). This is the



9

solution to the following optimization problem

ΩOpt = arg minΩ∈Ω

f(Ω). (47)

Using the DVB interference constraints (43)-(45), f(Ω) isgiven by

f(Ω) =

K(=KD+KS)∑k=1

(T∑t=1

αk,tηtk

)|rk −

−rk |

=

KD∑k=1

|λk −−λk |

+

K∑k=KD+1

(T∑t=1

αk,tηtk

)|nk −

−nk |, (48)

where

−rk =

−λk 1 ≤ k ≤ KD−nk KD + 1 ≤ k ≤ K

,

is the capacity demand by an SG-NAN cell or a DVB cell.Note that to fulfill the SG applications without communi-

cation outage, the capacity of a spectrum allocation should beequal to or larger than the capacity demand, i.e., rk ≥

−rk. If

rk >−rk, spectrum will be wasted because a larger rk requires

additional allocated spectrum. If rk <−rk, communication

outage will occur. Therefore, minimizing |rk −−rk | has two

objectives: 1) if rk >−rk, spectrum waste is minimized and

therefore more carriers are available for other uses, and 2) ifrk <

−rk, communication outage is minimized.

We also consider the SG application priorities in the objec-tive function. Since 0 ≤ αk,t ≤ 1 is the weight of applicationt representing its importance, and ηtk is the occupation ratio of

the application, a higher value ofT∑t=1

αk,tηtk, (

T∑t=1

αk,tηtk ≤ 1),

means that SG-NAN cell k has more applications of higher

priority. For DVB cells,T∑t=1

αk,tηtk = 1 since there is only

one DVB service and DVB is considered the primary networkwith highest priority.

B. Coloring Theory Based DSM Algorithm

Coloring theory is an effective method to solve complexproblems such as NP-hard problems [34]. As these problemsare characterized by significant computational complexity anda large search space, obtaining an optimal solution is typi-cally intractable. Coloring theory can be used to efficientlyfind a near optimal solution. Spectrum allocation in an SG-NAN/DVB heterogeneous system is difficult because of thelarge number of possible spectrum allocations and the inter-ference constraints. Conflict graphs have proven to be veryeffective in modeling interference [35] [36]. In this paper, weextend this concept to multi-color conflict graphs to reducethe complexity and improve convergence.

Our approach to solving the allocation optimization problemis to reduce it to a variant of the graph coloring problemby mapping carriers to colors, and assigning them to cells

(vertices in a graph). Based on coloring theory, the spec-trum allocation then becomes a problem of coloring withG = (U,C,B), where U is the vertex set of graph G witheach vertex representing a cell (SG-BS or DVB). B representsthe set of available carriers or colors at each vertex, andBk represents the set of available colors at vertex k. C isthe set of undirected edges between vertices representing theinterference between them. For any two vertices k and j, acolored edge exists between them if ck,j,m,n = 1. The set ofedges depends on the interference constraint matrix C. Thecoloring theory based DSM algorithm is given in Algorithm2.

Algorithm 2 Coloring theory based DSM algorithm1: Labeling: For each vertex k in G, label with a non-empty

color list according to a labeling rule (defined later). Thenfind k∗ = argmax label(k).

2: If the available color set Bk∗ of vertex k∗ is empty (i.e.,there exist available carriers in cell k∗), go to Step 3.Otherwise return to Step 1.

3: Coloring: Calculate the reward of each available colorin Bk∗ (i.e., the spectrum reward rk∗,m of carrier m asdefined in Section VII.A). Then allocate color(k∗) withthe maximum reward to vertex k∗ (i.e., allocate spectrumto cell k∗).

4: Topology Update: Remove color(k∗) from color set Bk∗and the color set of any neighbor connected to vertex k∗.Delete the edges associated with color (k∗).

5: If G is empty, terminate the algorithm. Otherwise returnto Step 1.

1) Labeling Rule: For the SG-NAN, the DSM labeling ruleshould not only consider the capacity requirements of eachcell, but also the priority of the T SG applications. Therefore,the label of SG-NAN cell k, KD + 1 ≤ k ≤ K, is

label(k)KD+1≤k≤K

=

(T∑t=1

αk,tηtk

)| −rk −rk|

=

(T∑t=1

αk,tηtk

)| −rk −

M∑m=1

ak,m · rk,m|.

(49)

As mentioned in Section VII.A, a higher value ofT∑t=1

αk,tηtk

means that SG-NAN cell k has more applications of greaterimportance, and so has earlier opportunities to be allocated

spectrum. | −rk −M∑m=1

ak,m · rk,m| represents the remaining ca-

pacity requirements of SG-NAN cell k in each iteration.Therefore, the label of SG-NAN cell k not only considersthe remaining requirements, but also the priority of SG appli-cations.

Since the proposed DSM algorithm can use DVB frequencybands to support SG applications, the coverage and capacitycharacteristics of the DVB system should be ensured first.Hence, the priority of a DVB cell is assumed to be higher thanthat of an SG-NAN cell, i.e., label(k) > label(j) ∀k, j, 1 ≤



10

k ≤ KD,KD + 1 ≤ j ≤ K. Further, the label of DVB cell k(1 ≤ j ≤ KD), is defined as follows

label(k)1≤k≤KD

= | −rk −rk|+ maxKD+1≤j≤K

label(j)

= | −rk −M∑m=1

ak,m · rk,m|+ maxKD+1≤j≤K

label(j),

(50)

where | −rk −M∑m=1

ak,m · rk,m| represents the remaining

requirements of DVB cell k in each iteration, andmax

KD+1≤j≤Klabel(j) is added to ensure the DVB cell

has a higher priority than the SG-NAN cells.2) Color Selection Rule: According to the optimization

function, to meet the real-time capacity demands quickly andefficiently, we choose the spectrum of cell k (i.e., the colorfor vertex k), according to the following rule

color(k) = arg maxm∈Bk

rk,m. (51)

This means that spectrum is allocated to cell k so as to providethe maximum increase in capacity of cell k.

VIII. SIMULATION RESULTS

We consider a WCDMA based SG-NAN with 19 cellsand 8 carriers (4 for downlink transmission and 4 for uplinktransmission), for each cell, and a DVB network with one basiccluster (7 cells) and 14 carriers. The maximum SG-BS transmitpower is 43 dBm on an SG-NAN carrier, and 34 dBm whenusing DVB spectrum. DVB cells only use the DVB spectrumand have a maximum transmit power of 60 dBm.

The DVB system only offers on-demand video services forits users, i.e., TV subscribers. For illustration purposes, weonly consider two SG applications so that T = 2 (EmergencyResponse with high priority α1, and DRM with low priorityα2, α1 > α2). Denote the proposed priority-based DSM asDSM-Pr, and DSM without priorities as DSM-NoPr. Notethat our proposed algorithm can easily be expanded to anarbitrary number of SG applications. The required data ratefor Emergency Response is 250 kbps, and for DRM is 56kbps [38]. The maximum outage probability for EmergencyResponse is set to 2%. Communication outage occurs when auser data rate is less than the threshold.

Both DSM-Pr and DSM-NoPr are performed every 30minutes. The Demand Response Management (DRM) period isset in accordance with the DSM, i.e., DRM is also performedevery 30 minutes. The performance of the DSM algorithmis compared with that of static Fixed Spectrum Management(FSM). In FSM, 3 carriers are assigned to an SG-NAN cell,and 2 carriers are allocated to a DVB cell with reuse factor7. For both DSM and FSM, the simulation time is 24 hours,i.e., 48 DSM periods.

The user distribution is a major factor influencing the spec-trum allocation. In this paper, we consider the user distributionacross the entire system as well as within each individual cell.We assume that base stations are updated with the most recentuser and network status, which is then used to determine the

user distribution. The distribution of users in a cell follows thedouble-Gaussian distribution model given by

w(x, y) =1

2πσ1σ2e− 1

2

(x−µ1σ1

)2

e− 1

2

(y−µ2σ2

)2

, (52)

where µ1 and µ2 are the centers (greatest density of users),and σ1 and σ2 represent the concentration of users.

The SG-NAN downlink outage probabilities with DSM andFSM are shown in Fig. 5. The average outage probabilitywith FSM is 21.21%, with a maximum of 35.6% during peakhours. This does not satisfy the SG application requirements.Although DSM-NoPr (α1 = 0.5 and α2 = 0.5), can providemuch better outage performance with an average of only 2%,it does not satisfy the QoS of Emergency Response in the peakhours (eg. from 6am to 12pm, and 5pm to 9pm). In contrast,DSM-Pr (α1 = 0.7 and α2 = 0.3), can provide EmergencyResponse with an average outage of 0.85% and a peak outageof 1.51%, with a small loss in DRM performance.

0 2 4 6 8 10 12 14 16 18 20 22 240

0.1

0.2

0.3

0.4

0.5

Time (h)

SG

−N

AN

Outa

ge P

robabili

ty

FSM

DSM−No−Pr (α1=α

2=0.5)

DSM−Pr,EM (α1=0.7)

DSM−Pr,DRM (α2=0.3)

Fig. 5. Downlink SG-NAN outage performance.

The DRM performance with downlink communication out-age is shown in Fig. 6. The load uncertainty model withunknown distribution is chosen since it has the worst perfor-mance as the communication outage probability rises. BothDSM-Pr and DSM-NoPr provide better DRM performancethan FSM, especially during peak hours. In DSM-Pr, thepriority of DRM is set to 0.3 < 0.5 in order to ensure the QoSof Emergency Response. Although the performance is slightlylower than DSM without priority, the difference is trivial.

The SG-NAN uplink outage probabilities are shown in Fig.7. Due to the large traffic loads from smart meters to theSG-BSs, the overall performance of all techniques is worsethan the downlink performance. To be specific, the averageoutage probability with FSM is 31.53%, with a maximumof 49.8% during peak hours, which is much worse than forthe downlink. The performance of DSM-Pr and DSM-NoPris also decreased compared to the downlink case. However,the proposed DSM-Pr still satisfies the QoS of EmergenceResponse, with average outage 1.26% and peak outage 1.98%.The corresponding DRM performance with uplink communi-cation outage is shown in Fig. 8. It can be seen that both DSM-Pr and DSM-NoPr provide better DRM performance than



11

0 2 4 6 8 10 12 14 16 18 20 22 24270

275

280

285

290

295

300

Time (h)

DR

M P

erf

orm

ance

FSM

DSM−No−Pr (α2=0.5)

DSM−Pr,(α2=0.3)

Fig. 6. DRM performance with downlink communication outage.

FSM. However, the performance difference between DSM-Pr and DSM-NoPr is greater than on the downlink. Thus theuplink has a greater impact on DSM-Pr performance than thedownlink.

0 2 4 6 8 10 12 14 16 18 20 22 240

0.1

0.2

0.3

0.4

0.5

0.6

Time (h)

SG

−N

AN

Outa

ge P

robabili

ty

FSM

DSM−No−Pr (α1=α

2=0.5)

DSM−Pr,EM (α1=0.7)

DSM−Pr,DRM (α2=0.3)

Fig. 7. Uplink SG-NAN outage performance.

0 2 4 6 8 10 12 14 16 18 20 22 24245

250

255

260

265

270

275

280

285

Time (h)

DR

M P

erf

orm

ance

FSM

DSM−No−Pr (α2=0.5)

DSM−Pr,(α2=0.3)

Fig. 8. DRM performance with uplink communication outage.

The DVB network outage performance is shown in Fig.9. In this case, FSM allocates all spectrum resources to theDVB cells, so the blocking probability is close to 0. However,

this is achieved at the expense of wasted spectrum resources,as the required QoS is typically 98%, not 100%. The callblocking rate of DSM is slightly higher than that of FSM, butit remains at 0.7% or less (QoS above 99.3%), so the requiredperformance level is met. Therefore, the proposed DSM has aminimal effect on the DVB network.

0 2 4 6 8 10 12 14 16 18 20 22 240

1

2

3

4

5

6

7x 10

−3

Time (h)

DV

B O

uta

ge P

robabili

ty

FSM

DSM

Fig. 9. DVB outage performance.

The average secondary spectrum opportunity, defined as theratio of the spectrum remaining to the total available, is shownin Fig. 10. DSM-NoPr is highest with an average of 26%.The average secondary spectrum opportunity with DSM-Pr isonly 20% since it utilizes some frequencies to satisfy the highpriority SG applications, but this is 13% higher than with FSM.

Fig. 10. The average secondary spectrum opportunities.

IX. CONCLUSION

A new priority based dynamic spectrum management(DSM) technique for the smart grid (SG) has been proposed.Resources are allocated considering the Quality of Service(QoS) and priorities of SG applications. Further, vacant DigitalTV (DTV) frequency bands are dynamically utilized to im-prove support for these applications. Simulation results werepresented that validate the proposed DSM technique. It was



12

shown that it can provide reliable communications for criticalSG applications and good performance for other applications,while mitigating the interference to the DTV system.

ACKNOWLEDGMENT

This work was supported by the National Natural Sci-ence Foundation of China (61227801, 61201152, 61121001),the National Science and Technology Major Project(2012ZX03003006), and the Fundamental Research Funds forthe Central Universities (2013RC0106).

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Zhiyong Feng received her B.S. and M.S. degreesin Communication Engineering, and Ph.D. degree inElectric Circuits and Systems from Beijing Univer-sity of Posts and Telecommunications. She currentlyis a professor at Beijing University of Posts andTelecommunications. She is the Director of the KeyLaboratory of Universal Wireless Communications,Ministry of Education. Her research interests in-clude spectrum monitoring and management, cog-nitive wireless networks, wireless networking, radioresource management, and Wireless network virtu-

alization. She has published 120 papers. She now own 38 patens, 4 publishedbooks and 40 standard proposals accepted by ITU, IEEE.

Qian Li received his B.S. degree from Beijing Uni-versity of Posts and Telecommunications (BUPT) in2009. In 2009 he began studies towards the Ph.D.degree in Communication and Information Systemsat the Key Laboratory of Universal Wireless Com-munications, Ministry of Education of BUPT. Hewas a visiting student at the University of Victoria,Canada, from Sept. 2012 to Oct. 2013. His researchinterests include the convergence of heterogeneouswireless networks, cognitive radio technology, fem-tocell configuration and optimization, and wireless

communication in smart grid.

Wei Li received the Ph.D. degree in ElectricalEngineering from the University of Victoria, Canadain 2004. In 2005 he joined the France Telecom SanFrancisco Lab as a Research Scientist, where hewas a member of international standard bodies forwireless networks. He is one of the authors of theIEEE802.15.3c Standard. He is currently an AdjunctProfessor at the University of Victoria, Canada. Hisresearch interests are in information theory, wirelessnetworks, smart grid and heterogeneous networks.

T. Aaron Gulliver received the Ph.D. degree inElectrical Engineering from the University of Vic-toria, Victoria, BC, Canada in 1989. From 1989to 1991 he was employed as a Defence Scientistat Defence Research Establishment Ottawa, Ottawa,ON, Canada. He has held academic positions atCarleton University, Ottawa, and the University ofCanterbury, Christchurch, New Zealand. He joinedthe University of Victoria in 1999 and is a Professorin the Department of Electrical and Computer Engi-neering. In 2002, he became a Fellow of the Engi-

neering Institute of Canada, and in 2012 a Fellow of the Canadian Academyof Engineering. He is currently an Area Editor for IEEE TRANSACTIONSON WIRELESS COMMUNICATIONS. From 2000 to 2003, he was Secretaryand a member of the Board of Governors of the IEEE Information TheorySociety. His research interests include information theory and communicationtheory, algebraic coding theory, smart grid, cognitive radio, and ultrawidebandcommunications.

Ping Zhang received the M.S. degrees in Physicsand Electrical Engineering from Northwest Univer-sity in 1982 and 1986, respectively, and he receivedthe Ph.D. degree in Electric Circuits and Systemsfrom Beijing University of Posts and Telecommu-nications (BUPT) in 1990. From 1994 to 1995, hewas a Post-Doctoral Researcher in the PCS Depart-ment, Korea Telecom Wireless System DevelopmentCenter. He currently is a professor in the Schoolof Information and Communication Engineering atBUPT. He is the Executive Associate Editor-in-

Chief on Information Sciences of the Chinese Science Bulletin, a GuestEditor of IEEE Wireless Communications Magazine, and an Editor of ChinaCommunications. He has received the first and second prizes of the nationaltechnology invention and technological progress awards, the first prize of theoutstanding achievement award of scientific research in college. His researchinterests include key techniques of 3G and B3G systems, cognitive radiotechnology, cognitive wireless networks, and MIMO-OFDM.

Documents

Priority-Based Dynamic Spectrum Management in a Smart Grid Network Environment