IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014 1047

A Cooperative Matching Approach forResource Management in

Dynamic Spectrum Access NetworksHaibo Zhou, Student Member, IEEE, Bo Liu, Member, IEEE,

Yongkang Liu, Ning Zhang, Lin Gui, Member, IEEE,Ying Li, Xuemin (Sherman) Shen, Fellow, IEEE, and Quan Yu

Abstract—Dynamic spectrum access (DSA) can be leveragedby introducing external spectrum sensing for secondary users(SUs) to overcome the hidden primary users (PUs) problemand improve spectrum utilization. In this paper, we investigatethe DSA networks with external sensors, i.e., external sensingagents, to utilize spectrum access opportunities located in cellularfrequency bands. Considering the diversity of SUs’ demandsand the secondary bandwidths discovered by external sensors,it is critical to manage the detected spectrum resources in anefficient way. To this end, we formulate the resource managementproblem in the DSA networks as a dynamic resource demand-supply matching problem, and propose a cooperative matchingsolution. Specifically, spectrum access opportunities are classifiedinto two types by the resource block size: massive sized blocksand small sized blocks. For the former type, SUs are encouragedto share the whole time-frequency block via forming coalitionalgroups with a “wholesale” sharing approach. For the latter type,the resource “aggregation” sharing approach is proposed to meetthe time-frequency demand of individual SUs. To further reducethe delay in the spectrum allocation and compress the matchingprocess, we develop a distributed fast spectrum sharing (DFSS)algorithm, which can deal with both two aforementioned typesof resource sharing cases. Simulation results show that the DFSSalgorithm can adapt to the dynamic spectrum variations in theDSA networks and the average utilization of detected spectrumaccess opportunities reaches nearly 90%.

Index Terms—Dynamic spectrum access, cooperation, coali-tion, matching, utilization maximization.

I. INTRODUCTION

CURRENT static spectrum allocation policy has resultedin inefficient spectrum utilization in the licensed spec-

trum bands [1]. To improve the spectrum utilization, thecognitive radios (CR) empowered dynamic spectrum access

Manuscript received May 8, 2013; revised September 9, 2013; acceptedNovember 10, 2013. The associate editor coordinating the review of this paperand approving it for publication was M. Bennis.

H. Zhou, B. Liu, L. Gui, and Q. Yu are with the Depart-ment of Electrical Engineering, Shanghai Jiao Tong University, Shang-hai, China, 200240 (e-mail: {haibozhou, liubo lb, guilin}@sjtu.edu.cn,yuquan@public3.bta.net.cn). B. Liu is the corresponding author.

Y. Liu, N. Zhang, and X. Shen are with the Department of Electrical andComputer Engineering, University of Waterloo, 200 University Avenue West,Waterloo, Ontario, Canada, N2L 3G1 (e-mail: yongkang.liu.phd@gmail.com,n35zhang@uwaterloo.ca, xshen@bbcr.uwaterloo.ca).

Q. Yu is also with the China Electronic System Engineering Company,Beijing, China, 100141; Y. Li is with the China Electronic System EngineeringCompany, Beijing, China, 100141 (e-mail: liying8800@163.com).

Digital Object Identifier 10.1109/TWC.2013.122413.130833

(DSA) technology allows the secondary users (SUs) to op-portunistically exploit the unused spectrum resource that aretemporally released by primary users (PUs), which gainsgrowing attentions from both academia and industry [2][3][4].

Currently, most extensive research efforts have been madeto exploit the TV “white spectrum” for DSA, and amongthem, a novel solution of radio environmental maps (REM) isproposed in [5]. However, the recent investigations revealedthat the cellular bands have also shown the potential for DSAimplementation [6][7], where the SUs can use the temporallyunused time-frequency resources in cellular networks in anopportunistic way. As discovered in [6], the cellular DSAapplications are promising and attractive in non-peak hours,for example, the period of nights and weekends. Furthermore,based on spectrum data mining technology, such as the fre-quent pattern mining [7], the long-term spectral and temporalstate in cellular bands can be predicted with a predictionaccuracy higher than 95%, which motivates the study of DSAtechnology in cellular bands.

In this paper, we first introduce an external sensor aideddynamic spectrum access model in cellular networks, wherethe external sensors, i.e., external sensing agents, can performcooperative spectrum sensing and supply flexible availablelicensed spectrum resources in cellular networks to SUs withdifferent resource demands. Specifically, as the external sens-ing agents can cooperatively sense the spectrum usage stateand process the collected sensing information in a distributedway in the cellular networks, the realtime spectral-temporalavailabilities of spectrum resource in specific local cellularnetworks can be acquired. Therefore, the SUs can request forthose time-frequency resources according to their own needs.Those available time-frequency resources are referred to astime-frequency blocks (TFBs), where one TFB is composed ofdifferent number of resource block units in the cellular system[8]. We then propose a set of resource management rules, andmodel the dynamic spectrum sharing process as a cooperativesupply-demand matching problem, to match the dynamicalTFBs supplied by external sensing agents and various resourcedemands of SUs. Considering the time-frequency variationof detected resource blocks, those TFBs can be classifiedinto two types according to the TFBs size: massive sizedblocks and small sized blocks; and the “wholesale” sharingand “aggregation” sharing approaches are proposed accord-

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1048 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014

Cellular Network

Secondary Network

Secondary Users

Base Station Spectrum Sensing Agent

Primary User

Vacant Block

Occupied Block

Freq

uen

cy

Time-slot

Spectrum Sensor Node

Fig. 1. Coexistence scenario with cellular network and unlicensed network.

ingly. Particularly, in the “wholesale” sharing approach, thecooperation among the SUs is utilized for maximizing theutilization of massive sized blocks, while in “aggregation”sharing approach the external sensing agents cooperate witheach other for minimizing the under utilized small sizedblocks. Finally, we devise a distributed fast spectrum sharing(DFSS) algorithm to realize both the “wholesale” sharingand “aggregation” sharing approaches based on the heuristicpacking method. The main contributions of our work aresummarized as follows,

• We propose a scalable DSA model and novel associatedresource management rules, which can provide a threadfor efficiently utilizing the unused spectrum resources incellular bands in a distributed and flexible way.

• We introduce a dynamic spectrum supply-demand match-ing strategy for the dynamic spectrum access networks,which can significantly improve the utilization of cellularbands, while helping to alleviate the spectrum scarcity.

• We develop the DFSS algorithm to reduce the delay in thespectrum allocation for resource management in cellularDSA applications, which can be helpful to accelerate thespectrum access process in the real applications.

The reminder of this paper is organized as follows. Relatedwork is presented in section II. Network model and problemformulation are presented in section III. In section IV, wegive the cooperative strategies for two cases in the formulatedsupply-demand resource matching problems. In section V,we present the performance analysis and simulations of thealgorithm. Section VI concludes this paper.

II. RELATED WORK

Spectrum sensing is to detect dynamic access opportunitiesfor SUs [9]. In [10][11], joint spectrum sensing and dynamicaccess are investigated using coalitional games. However,in cellular networks, the spectrum usage tends to exhibitmuch more temporal and geographical variations [6], the

internal spectrum sensing scheme including some centralizedor distributed sensing technology cannot handle it properly.To increase the spectrum vacancy detection probability andaccuracy while decreasing the sensing time [12]-[14], exter-nal sensing agents aided spectrum sensing schemes becomemore popular to accurately acquire and predict the availablespectrum resources in the cellular bands [15][16]. Underthe external-aided spectrum sensing scenarios [17], externalsensing agents can act as the sink nodes in the spectrum sensornetworks for cooperative sensing and centralized processing.

Another issue of the DSA application in cellular bands isthe management of spectrum resources in an effective way.Although the approach based on REM in [5] provides anattractive architecture for the DSA application in TV bands,it is more complicated in the REM approach to performthe functionality of storage and dynamic spectrum resourcemanagement for cellular bands. Typically, there are two mainchallenges: i) different SUs have various resource require-ments, in terms of the wireless access duration, bandwidth,etc.; and ii) compared with TV spectrum, the spectrum usagein licensed spectrum tends to exhibit much more temporaland geographical variations, and for different spectrum usagebehaviors and service requirements, the pre-allocated spectrumresources for PUs are composed of variant time-frequencyblocks (TFBs), especially in existing heterogeneous communi-cation systems. Considering that the explored available TFBsin cellular networks have different number of continuoussubchannels with different holding slots [6], the improperresource management and utilization approach will lead tolow resource utilization. In [18], H. Mutlu et al. investigatedefficient pricing policies for resource providers to price theexcess cellular spectrum bandwidths to SUs. In [19], Y. Liuet al. proposed an adaptive resource management frameworkto improve spectrum utilization efficiency and mitigate the in-terference to PUs. To quickly and properly match those variantand scattered time-frequency blocks with various demanders

ZHOU et al.: A COOPERATIVE MATCHING APPROACH FOR RESOURCE MANAGEMENT IN DYNAMIC SPECTRUM ACCESS NETWORKS 1049

in cellular DSA, the problem of dynamic resource supplywith various demanders should be well investigated [20].From the perspective of quality of service for SUs’ demands,in [21], Alshamrani et al. proposed a spectrum allocationframework for heterogeneous SUs in real time and non-realtime (NRT) applications, respectively. In [22], Sodagari etal. proposed a time-optimized and truthful dynamic spectrumrental mechanism. In [23], H. Zhou et al. introduced a packingapproach to fast and optimally allocate the time-frequencyblocks. In [24], Yuan et al. discussed a dynamic time-spectrumblocks allocation problem in cognitive radio networks. In[25], C. Singh et al. introduced a provider-customer matchingresource allocation strategy based on the coalitional games. In[26], N. Zhang et al. investigated a maximum weight matchingproblem for the cooperative DSA in multi-channel cognitiveradio networks. However, none of these works are specific forcellular networks and consider the aforementioned features ofcellular DSA.

III. NETWORK MODEL AND PROBLEM FORMULATION

A. Dynamic Spectrum Access Service Model

We consider a dynamic spectrum access scenario whichconsists of a cellular network as a primary network, a localspectrum sensing network and a secondary network as shownin Fig. 1. In the cellular network, the base station (BS) man-ages the resource scheduling to serve mobile stations (MSs)that are referred to as PUs. Due to different communication re-quirements of PUs, the statically pre-assigned time-frequencyresource blocks are different. In the meantime, the MSs havedifferent characteristics of spectrum usage behaviors [6][7],such as the frequent variations in time and space domain.Similar to [10], the secondary network is self-organized in thesame area. Once a transport link is requested for a realtimebulk data flow transmission between two SUs, e.g., videoconference, data forwarding and multi-media service, etc., SUswill apply to external sensing agents for the DSA opportunitieswith appropriately sized bandwidths and spectrum accessdurations.

The local spectrum sensing network is composed of thecommon sensor nodes and sink sensor nodes. The sink sensornodes can obtain the realtime channel prediction informationand provide the dynamic spectrum access opportunities forSUs. Once PU turns on in the free spectrum bands, the realtimespectrum usage update made by sensor nodes will inform SUsto stop transmission tasks to avoid the interference to PUs.

Definition 1: (TFBs Supply) The available TFBs sup-ply set from external sensing agents at time t is de-fined by RBt = {f t

p1, f t

p2, ..., f t

pn}, where the avail-

able TFBs supply function of external sensing agents isf tpi(κtpi

, αtpi, βt

pi,Δt

pi, ρmax,t

pi, πt

pi), ∀i = 1, 2, ..., n, n is the

available time-frequency block number, κtpiis channel band-

width, αtpi

and βtpi

are the arrival time and ending time,respectively, Δt

piis the time-slot size, Δt

pi= βt

pi−αt

pi, ρmax,t

pi

is the permitted transmission power, and πtpi

is the requiredprice for the TFBs.

Definition 2: (TFBs Demand) For SUs with TFBs de-mand, the set of n TFBs demanders is defined by Φ ={μ1, μ2, ..., μn}, and the user demands set is defined by

TABLE ISUMMARY OF IMPORTANT SYMBOLS

Symbol Definition

pi Primary user i

μj Secondary user j

f tpi

Resource block supply function of pi at time t

N Resource block number provided at time t

κtpi

Channel bandwidth in f tpi

Δtpi

Time-slot size in f tpi

ρmax,tpi Permitted transmission power in f t

pi

πtpi

Required price in f tpi

ㆵj

Resource block demand function of μj at time t

ꆵj

Required channel bandwidth of μj at time t

Δ†µj

Applied time-slot of μj at time t

ρ†µjTransmission power ability of μj at time t

π†µj

Accepted leasing price of μj at time t

RBt The available time-frequency block supply set at time t

RBtp The massive sized time-frequency block supply set at time t

RBtd The small sized time-frequency block providing set at time t

Θtpi

Evaluated transmitting data capacity

φ†µj

The real transmitting data of SU μj

N The coalitional player set

v Spectrum sharing payoff function

B Realtime coalition structure

ζft

µjReal resource consuming cost of SU μj at time t

ζct

µjUnder utilized resource cost shared by SU μj at time t

D† = {γ†μ1, γ†μ2

, ..., γ†μj}, where the j-th user γ†μj

=

{κ†μj,Δ†

μj, ρ†μj

, π†μj}, κ†μj

is the required channel bandwidth,Δ†

μjis the applied time-slot, ρ†μj

is the power transmissionability, and π†

μjis the acceptable leasing price.

In Definition 1, we assume that the sensing function ofcooperative agents can guarantee the short detection time ofvacant spectrum [17][27], and the release time of spectrumvacancy information t′ is no late than the resource availabletime αt

pi, i.e., αt

pi− t′ ≥ 0. Hence, the released spectrum

information can satisfy both the demands of online dynamicspectrum sharing and offline spectrum reservation. In Defini-tion 2, all the parameters can be calculated according to thefactual transmitting data volume, required data rate and powerconstraint conditions, etc. For the delay-sensitive SUs, theycan reserve the realtime TFBs before transmission, to avoidthe problems caused by the channel reservation delay.

Generally speaking, all the external sensing agents act likethe local sellers in a spectrum market [28]-[30]. At timeinstant t, each sensing agent will publish TFBs information.There are a random number of supplied TFBs that will betraded among the n independent SUs with different demands.We assume that all the realtime information provided by the

1050 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014

agents is available for all the SUs in the restricted area.Meantime, we do not consider the market competition, i.e.,the realtime spectrum resource blocks will be tagged with thefixed price πt

pi. According to the defined TFB supply and

demand function, for ∀μj , if the price of TFB can be acceptedby the SUs, i.e., π†

μj≥ πt

pi, all the SUs can join the resource

selection procedure.

B. Problem Formulation

For practical resource sharing in the spectrum market, theapplication rule of TFB demanders at time t is as follows,

Definition 3: (Spectrum Application Rule, SAR) Given theTFB supply set RBt, to meet the transmission data rate andservice duration requirements of SUs at time t, the spectrumapplication conditions are as follows, i), κtpi

≥ κ†μj; ii), Δt

pi≥

Δ†μj

, ∀t = 1, 2, ..T .In practice, due to the time-frequency variation of TFBs

supply and different demanded TFB sizes, there exists asupply-demand resource matching problem in the spectrumtrading market. On one hand, if the sizes of some TFBsare larger than the resource demands of SUs, the improperallocation of TFBs may make the TFBs underutilized. Onthe other hand, if the sizes of some TFBs are smaller sothat they cannot meet the need of any individual SU, thosesmaller sized TFBs will be wasted. Considering those twocases, the TFB supply function set RBt can be divided intotwo subsets, i.e., RBt

p and RBtd . For RBt

d , ∀f tpi∈ RBt

d ,∃κtpi

< κ†μkor Δt

pi< Δ†

μk, μk ∈ Φ, ∀t = 1, 2, ..., T , and

RBt = RBtp ∪ RBt

d , RBtp ∩ RBt

d = ∅. The TFBs providedby external sensing agents are labeled with the unit of TFBs’number, and the detailed dynamic resource management ruleis regulated as follows,

• Case 1: If the time-frequency size of one individual TFBis larger than any current applicant’s demand, it will beleased as a whole and allowed to be re-divided amongmultiple applicant SUs for required sub-channels andsub-slots. We name this approach as “wholesale” sharing,where the SUs will be charged for the whole value ofapplied resource blocks.

• Case 2: If the time-frequency size of one individual TFBcannot meet the needs of users, the multiple small sizedblocks will be aggregated. We name this approach as“aggregation” sharing. To encourage SUs to apply forsmall TFBs in Bt

d , the applicants will be only chargedfor the practical applied block sizes.

Fig.2 demonstrates the examples to effectively share theprovided TFBs in the two cases. Case 1 illustrates that oneTFB can be shared via the optimal combination of multipleSUs to increase the TFB’s utilization. Case 2 shows that ifeither the bandwidth or time-slot requirement cannot be met bySUs, the multiple TFBs have to be aggregated for the resourceusage. The resource aggregation approach requires the SUs touse the spectrum switch technology for resource sharing andthe proposed charging policy will be an incentive for them touse the small sized resources.

Based on SUs’ transmission rates and data capacity, at timet, SU μj can select any of the two resource subsets for theTFB leasing, i.e., RBt

p or RBtd . Before the resource selection,

t1 2 3 4

1

23

4567

8

ContinuousFrequencyAggregation

Time-slotAggregation

f

DiscreteFrequencyAggregation

0Case 2: The aggregation of multi-RB shared by one SU.

f

t1 2 3 4

8

12345

67

0Case 1: RB is shared by multi-SUs.

Fig. 2. Two cases in the supply-demand resource matching problem.

the SU μj will evaluate the value of spectrum resource of pi,according to the specific wireless communication parameters,i.e., the allowed transmission power ρmax,t

pion the demanded

channel, the available channel bandwidths κtpi, channel gain

gtpi, and the noise variance (σt

pi)2. We choose the transmitting

data capacity Θpi,t as the evaluation function, according to theShannon-Hartley theorem, which is given as follows,

Θtpi

= κtpi· log2(1 +

ρmax,tpi

.∣∣gtpi

∣∣2

(σtpi)2 )

︸ ︷︷ ︸γpi

·Δtpi, (1)

where γpi is the maximum achievable data rate if SUs own thespectrum resource of pi. Here, ∀j = 1, 2, ..n in the SU set,we assume that all SUs can satisfy the transmission powerconstraint. Also, for all the SUs in the restricted area, weassume that the wireless communication environment at timet is the same, i.e., for ∀i = j, we have ρmax,t

pi= ρmax,t

pj, and

gtpi= gtpj

. Hence, the evaluation function of shared spectrumresource is only related to the bandwidth and time-slot size.

For simplification, let log2(1 +ρmax,tpi

.|gtpi|2

(σtpi

)2) = ωt

pi, we can

rewrite (1) to be

Θtpi

= κtpi· ωt

pi·Δt

pi, ∀i = 1, 2, ...,m. (2)

Similar to (2), we assume that SU μj leases the vacantspectrum of PU pi. Considering the spectrum management

ZHOU et al.: A COOPERATIVE MATCHING APPROACH FOR RESOURCE MANAGEMENT IN DYNAMIC SPECTRUM ACCESS NETWORKS 1051

rule, the practical benefit of SU μj is

φ†μj= κ†μj

· ω†μj·Δ†

μj, ∀j = 1, 2, ..., n. (3)

Lemma 1: For ∀μj in the secondary network, it can applyto the external sensing agents for the resource of pi iff Θt

pi≥

φ†μj.

Proof : According to SAR, SU μj only reserves the spec-trum resource of PU pi, under the conditions that, κtpi

≥ κ†μj

and Δtpi≥ Δ†

μj, ∀t = 1, 2, ..T . Combining (1) and (3), the

lemma is proved.Based on the considered charging policy for the spectrum

access opportunities in the two discussed cases, we cancalculate the spectrum leasing charge based on bandwidth andthe duration of the time-slot, i.e., at time t, the leasing revenueof whole spectrum block pi is Θt

pi· πt

pi. Hence, the TFB

with larger bandwidth and longer holding time will have moreleasing revenue.

IV. THE COOPERATIVE SUPPLY-DEMAND RESOURCE

MATCHING APPROACH

To satisfy users’ demands in the DSA network and reducethe spectrum sharing cost for SUs to share the time-frequencyblocks, we consider two dynamic spectrum sharing cases.For Case 1, when the provided TFB f t

pi∈ RBt

p, the TFBdemanders can cooperate to share one time-frequency blockand the corresponding cost; For Case 2, i.e., f t

pi∈ RBt

d,the TFB providers can cooperate for the resource aggregation.The following section will show the detailed supply-demandresource matching solutions.

A. Case 1: TFB Demanders’ Cooperation for “Wholesale”Sharing

We first consider the case that f tpi∈ RBt

p, i.e., ∃μj ,κtpi

≥ κ†μj&& Δt

pi≥ Δ†

μj. According to the resource

management rule, for μj , the burden of spectrum sharing costwill be increased if the RB cannot be shared fully both in timeand bandwidth domain.

Remark 1: The resource management rule can guarantee ahigh utilization for the supplied TFBs because it can avoidthe unordered demanders to get the unmatched resources.However, the spectrum leasing cost of pi is far beyond theactual benefit of μj , i.e., Θt

pi· πt

pi≥ φ†μj

· πtpi

. Hence, theTFB demanders’ cooperation is necessary for the spectrumand cost sharing.

In Fig. 3, we show one example of the TFB demander’scooperation case, where μ1, μ3, and μ7 form coalitional group1 to share the resource of p1. Accordingly, μ2, and μ4 formgroup 2, μ5 and μ6 form group 3, and μ8 forms group 4,to share the resource of p2, p3 and p4, respectively. Via thecooperation of SUs for the resources application, the supplyand demand for TFBs can be perfect matched, so all thecooperative SUs in the four groups can get benefits.

Definition 4: (Coalition structure) The characteristic func-tion of coalition is 〈N , v〉 [31], where N is the cooperativeplayer set and N ⊆ Φ, v is the utility function. At time t, thenumber of player set is λ =

∣∣RBtp

∣∣. For a realtime coalitionstructure B, B = {B1, B2, ..., Bλ}, ∀i = j, Bi

⋂Bj = ∅ and⋃λ

i=1 Bi = N . For ∀μj ∈ N , ∀t = 1, 2, ...T , when grouped

1p2p 3p4p

13

7

1p2 4

2p5 6

3p8

4p

Fig. 3. At time t, the idle spectrum information of PUs are as follows, κtp1

=1,Δt

p1= 6; κt

p2= 2,Δt

p2= 3; κt

p3= 3,Δt

p3= 3; κt

p4= 4,Δt

p4= 1.

The spectrum demand information of SUs are as follows, ꆵ1

= 1,Δ†µ1

= 1;ꆵ2 = 2,Ć

µ2 = 1; ꆵ3 = 1,Δ†

µ3 = 3; ꆵ4 = 2,Δ†

µ4 = 2, ꆵ5 =

3,Δ†µ5 = 1; κ†

µ6 = 3,Δ†µ6 = 2; κ†

µ7 = 1,Δ†µ7 = 2; κ†

µ8 = 4,Δ†µ8 = 1.

into Bi to lease one resource candidate f tpi

, the utility functionvtμj

can be given by

vtμj= |Θt

ftpi· πt

pi−Θt

ftpi· φtμj∑μh∈Bi

φtμh

· πtpi

︸ ︷︷ ︸ζtµj ,Bi

|, (4)

where vtμjis composed of two parts: the whole sharing cost

of TFB Θtftpi

·πtpi

, and the sharing cost in the formed coalition

ζtμj ,Bi.

Remark 2: From (4), we can see that μj can benefit fromthe decreased cost sharing, and the payoff Ct

μjvia forming

coalitional groups is in the following range:

0 ≤ Ctμj≤

∣∣∣Θtftpi· πt

pi− φtμj

· πtpi

∣∣∣ .Remark 2 shows that the spectrum cost for each cooperative

player will be decreased, so the cooperation can bring benefitsfor all cooperative players. Furthermore, ζtμj ,Bi

in coalition Bi

shows a fair cost sharing principle, and ζtμj ,Biincludes two

parts,

i) the practical resource consuming cost ζft

μj, and ζf

t

μj=

φtμj· πt

pi;

ii) the common sharing cost ζct

μjfor under utilized TFB,

which is given by

ζct

μj= (Θt

ftpi−

∑μh∈Bi

φtμh)

φtμj∑μh∈Bi

φtμh

· πtpi.

From Remark 2, if μj cannot join any coalition for co-operation, Ctµj equals to zero. In addition, for an optimalmatching in the coalition [32], any SU μj only expects topay for the practical resource consuming cost, i.e., ζc

t

μj= 0

and Θtftpi

=∑

μh∈Bi

φμh,t, which is also the goal for all the

coalitions forming.

For ∀μj , μj can cooperate with different SUs which alsosatisfy the SAR. To form a coalition B′

pifor sharing the

resource of f tpi

, the goal is to minimize the resource sharingcost in coalition candidate B′

pi, and the resource sharing cost

1052 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014

ζtμj ,B′pi

can be given as follow

ζtμj ,B′pi

= Θtftpi· φtμj∑μh∈B′

pi

φtμh

· πtpi.

Equivalently, we can minimize the under utilized time-frequency resource in TFB by cooperation, to achieve theminimal resource sharing cost. Hence, the process of coalitionforming B′

pican be obtained as follow,

B′pi

Δ= argmin

μh∈N ′\μj

∣∣∣∣∣∣Θt

ftpi− φtμj

−∑

μh∈N ′\μj

φtμh

∣∣∣∣∣∣, (5)

where N ′ is the SUs set satisfying the SAR to share theresource of f t

pi.

Definition 5: For a rational player (RP) μj , the resourcepreference relation between B′

pjand B′

pkis defined as:

if ζtμj ,B′pj

�μjζtμj ,B′

pk

, B′pj�μj

B′pk

. The symbol � is ex-

pressed as the preference relation.The preference relation is transitive among all the RPs, i.e.,

if ζtμj ,B′pj

�μj ζtμj ,B′

pk

and ζtμj ,B′pk

�μj ζtμj ,B′

pw, we can get

ζtμj ,B′pj

�μj ζtμj ,B′

pw. Via the optimal matching, μj can find

the best group that could minimize the spectrum sharing costof f t

pi. In fact, all the SUs are rational and thus they will have a

strong preference to choose the groups with more cooperationbenefits. Moreover, we assume that all the group members andthe new group applicants are treated equally in any coalitionin terms of the contributions to the payoff in the coalitionforming process. If B′

pkis the final formed coalition, it means

that μj can form coalition B′pk

with other SUs, at the minimalspectrum sharing cost ζtμj ,B′

pk

, i.e.,

B′pk

Δ= argmin

B′pi

∈B′{ζtμj ,B′

pi}, ∀k = 1, 2, ...,

∣∣RBtp

∣∣ . (6)

For a new rational applicant μj who will be compatiblewith the evolving coalitional player set Bpi , there are twosituations:

i) enlarging the cooperative player set iff∑μh∈Bpi

∪μj

φtμh≤ Θt

ftpi.

.ii) replacing some existed players in the formed coalition

iff ∑μh∈Bpi

φtμh≤

∑μh∈B∗

pi

φtμh,

where B∗pi

is the renewed coalition after the replacement.Definition 6: Given the coalition B′

pi, if no rational player

prefers to join the coalition or leave the coalition for betterutility, then the coalition B′

piis stable, i.e.,

B′pi

Δ= argmin

μh∈N

∣∣∣∣∣∣Θt

ftpi−

∑μh∈N

φtμh

∣∣∣∣∣∣.

.Theorem 1: If a coalition in the dynamic matching game

is stable, the coalition can reach the equilibrium.Proof : For any rational player μj , μj ∈ N , we assume

that they can acquire the dynamic matching information inthe process of coalition forming. Hence, the rational player

can calculate and compare the payoffs in different Bpi , whereBpi ⊆ B. If they can benefit from Bpk

, and Bpk= Bpi , the

player μj will choose to cooperate with Bpk. Due to the fixed

time-frequency block size, i.e., Θtpi

is fixed, with the repeatedmatching game1, Legros and Newman have proved in [33]that a stable coalition will be formed as Definition 6.

Dynamic coalitional matching game is a well-known NP-hard problem [33]. To solve the dynamic sharing problem viaTFB demanders’ cooperation, we design a DFSS algorithm.The DFSS algorithm can solve the two-dimensional packingproblem based on the minimal surplus strategy [34]. We firstformulate the packing problem with the best first fit (BFF)approach. Specifically, in the two-dimensional BFF packingapproach, the packing process takes the channel bandwidthas the first packing condition to satisfy. The packing processruns with round, and denote by � the number of rounds inthe process of coalition forming, where Bk

i is the subset ofB each round, and

⋃�

k=1 Bki = Bi. At each time, the most

suitable SU μj will be selected, i.e., the SU with maximalφ†μj

to be packed in TFB f tpi

. At each round, the reservationtime range is max{Δ†

μh}μh∈Bk

i. If the bandwidth of channel

in the fixed reservation time range is allocated, the conditionthat whether there is residual time-slot left will be checked. Ifthe condition holds, the DFSS algorithm will keep allocatingthe residual time-frequency block in the next round. Accordingto the time complexity analysis in [34], the time complexity ofDFSS algorithm is O(n log(n)). The formulation of packingproblem is as follows,

min∀fpi∈RBt

p,Bi⊆B{0, Θt

fpj−

�∑k=1

∑

μh∈Bki

φ†μh}, (7)

s.t. κtfpj ≥∑

μh∈Bki

κ†μh, ∀fpj ∈ RBt

p , (8)

Δtfpj≥

�∑k=1

max{Δ†μh}μh∈Bk

i

, ∀fpj ∈ RBtp . (9)

Based on the up-to-date spectrum leasing information fromagents and the application requirement of neighboring SUs,each SU decides to request for the needed resources from anypossible external agent. To realize the fast matching processin a distributed way, the coalition leaders can be selected toorganize the fast dynamic matching game.

Definition 7: (Coalition Leader): Given χ coalition leaders,and χ = |RBt|, to match the χ different TFBs, the leaderselection process is formulated as

{l1, l2, ...lχ} Δ= argmin

μj∈N ,ftpi

∈RBtp

∣∣∣Θtftpi− φ†μj

∣∣∣ ..

Once the χ coalition leaders are selected, the remainingSUs will choose to join one of the χ groups to share thespectrum in a distributed way, according to the packing targetand constraints. At time t, the SUs’ demand function setapplying for the resources in F t

p is Dp. The DFSS algorithmis shown in Algorithm 1.

1The minimal surplus strategy is adopted in the dynamic matching game.

ZHOU et al.: A COOPERATIVE MATCHING APPROACH FOR RESOURCE MANAGEMENT IN DYNAMIC SPECTRUM ACCESS NETWORKS 1053

Algorithm 1 DFSS algorithm for TFB demanders’ coopera-tion.Input: RBt

p and Dp,Output: B′ = {B′

p1, B′

p2, ..., B′

p‖Ftp‖}

1: Initialize: t, ρmaxpi,t , gpi , Bj′ = 0 and σ2

pi,t

2: while (F td = ∅ && Dp = ∅) do

3: Sort(F tp) with ↓ Θt

pi

4: Sort(Dp) with ↓ φ†µj

5: Coalition leader ← {l1, l2, ...lx}6: B∗ ← Coalition leader7: �← 18: while (SAR condition(�)) do9: Minimize surplus RBsupply(�)

10: B∗ = [B∗ B�]11: �← �+ 112: else while13: end while14: Return B′ = {B∗

p1, B∗

p2, ..., B∗

p|RBtp|}

15: t← t+ 1

B. Case 2: TFB Providers’ Cooperation for “Aggregation”Sharing

For Case 2, i.e., ∀f tpi∈ RBt

d, ∃κtpi< κ†μj

or Δtpi

<

Δ†μj

, ∀t = 1, 2, ..., T , the provided TFBs cannot satisfy theSAR of any individual SU. Since the spectrum aggregationtechnology [8] includes the contiguous and non-contiguousresource aggregation approaches, multiple individual TFBscan be aggregated together to widen the carrier bandwidthand prolong the duration of spectrum usage.

We assume that μk prefers to apply the TFB resource fromthe TFBs set RBt

d . Hence, multiple external sensing agentswill schedule and cooperate to supply the resources for μk.The TFB aggregation approaches in RBt

d can be dividedinto three types: frequency-band aggregation (FBA), time-slot aggregation (TSA), and mixed aggregation (MA). For∀k, μk ∈ Φ∗, and Φ∗ is the applicants set of SUs, and thespectrum switch technology for the spectrum access will beutilized by those SUs.

• FBA: ∀pi, pi ∈ P, and f tpi∈ F t

d , if pi has the requiredavailable duration, i.e., min

ftpi

∈F td

{Δtpi} ≥ Δ†

μk, to meet

the requirement of bandwidth, the FBA condition is∑ftpi

∈F tdκtpi≥ κ†μk

.

• TSA: ∀pi, pi ∈ P, and f tpi∈ RBt

d, if pi has therequired available bandwidth, i.e., min

ftpi

∈RBtd

{κtpi} ≥ κ†μk

,

to meet the requirement of time-slot, the TSA conditionis

∑ftpi

∈RBtdΔt

pi≥ Δ†

μk.

• MA: ∀pi, pi ∈ P, f tpi∈ RBt

d, the MA procedureincludes two steps: FBA and TSA. Firstly, the pro-vided TFBs are aggregated to form several largerTFBs which can meet the bandwidth requirement ofTFB demander μk, and the renewed TFBs set isRBt

d,μk= {RBt

d1,μk, RBt

d2,μk, ..., RBt

dm,μk}. Then, the

larger TFBs in RBtd,μk

are aggregated to meet the time-slot requirement of TFB demander μk. The MA con-

ditions are: i),∑

ftpi

∈RBtdm,µk

κtpi≥κ†μk

, ∀RBtdm,μk

⊂RBt

d,μk; and ii),

∑RBt

d,µk

minftpi

∈RBtdm,µk

{Δtpi} ≥

Δ†μk

.

According to the charging policy regulated for TFBs in Case2, the applicants only pay for the practical values of appliedTFBs, i.e., φtμk

· π†μk

. For all rational TFBs providers, theywant to lease the TFBs to the demanders via the cooperativecombination. Meantime, they prefer to avoid the redundanttime-frequency block supply, because more resource supplydoes not necessarily mean more payoff. Hence, in Case 2, eachresource provider will have the preference relation to choosepartners for the TFB aggregation. The cooperative matchinggame can be utilized to select the best combination amongdifferent providers to match the demand of one SU.

Remark 3: ∃f tpi∈ RBt

d, it is incentive for TFBs providersto form coalitions with other providers, satisfying the TFBrequirements of applicant μk, i.e., (Θt

pi+∑

ftpi

∈RBtdΘt

pk) ≥

φ†μk, and minimizing the waste of redundant time-frequency

space in TFB, i.e., minimizeftpi

∈F td

(Θtpi+∑

ftpi

∈RBtdΘt

pj)−φ†μk

.

The payoff of pi denoted by ψpi is as follow,

ψpi

Δ= φμk,t · πμk,t ·

Θpi,t

Θpi,t +∑

ftpk

∈RBtpΘpk,t

. (10)

Theorem 2: For ∀ pi, to form coalition in the set of RBtd ,

the equilibrium under the constraint of SAR can be reachedwhen maximizing ψpi , where ∀pi ∈ RBt

d.Proof : The coalition forming process of TFB providers

is similar to that of TFB demanders. For the cooperationamong TFB demanders in Case 1, the objective function islinear with the required size of TFB from the demanders andconstrained by the packing upperbound, i.e., the capacity ofprovided TFBs. For any rational player pi, pi ∈ RBt

d, whenmaximizing ψpi , where ∀pi ∈ RBt

d, all the players will haveno incentive to join a new coalition. Obviously, the optimalformed coalition can approach to the equilibrium state, i.e.,Θpi,t+

∑ftpj

∈RBtpΘpj ,t = φμk,t, to aggregate to be a renewed

TFB with the demanded size of time-frequency block. Hence,once the minimal waste of redundant time-frequency space inTFB is approached, the maximal payoff of pi will be achieved,and the coalition forming equilibrium will be reached.

The coalition forming process of TFB providers is alsoa NP-hard problem. Similar to Case 1, we can apply theminimal surplus strategy to form the dynamic groups amongthe cooperative external sensing agents. Specifically, we for-mulate the dynamic spectrum sharing as a two-dimensionalpacking problem. For general formulation, we consider theMA case. Firstly, via the FBA technology, the multiple TFBsare aggregated to meet the demand of bandwidth requirementof the objective μk, μk ∈ Φ∗. After that, the renewed TFBsare aggregated for the longer duration to meet the demand oftime-slot of the objective μk. The packing process runs in theunit of round, and the round number is assumed to be equal tothe number of renewed TFB m. For the fast spectrum sharing,on the basis of time-slot and channel requirements, the TFBwith larger available time-slot and bandwidth size, the higherpriority the TFB will be packed first. Meantime, the surplusof time-frequency sizes provided for an applied TFB should

1054 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014

0 10 20 30 40 50

5

10

15

20

25

30

35

40

45

Time−slot size of PU : 50 Units

Spe

ctru

m s

ize

of P

U :

48 U

nits

0 10 20 30 40

5

10

15

20

25

30

35

40

Time−slot size of PU : 45 Units

Spe

ctru

m s

ize

of P

U :

44 U

nits

Fig 4. The packing result by random packing strategy.

0 10 20 30 40 50

10

20

30

40

Time−slot size of PU : 50 Units

Spe

ctru

m s

ize

of P

U :

48 U

nits

0 10 20 30 40 45

5

10

15

20

25

30

35

40

Time−slot size of PU : 45 Units

Spe

ctru

m s

ize

of P

U :

44 U

nits

Fig 5. The packing result by DFSS algorithm.

be minimized. The detailed packing formulation is shown asfollowing,

minftpi

∈RBtd

{(∑m

h=1

∑RBt

dh,µk

Θtftpi)− φ†μk

, 0} (11)

s.t.∑

ftpi

∈RBtdm,µk

κtpi≥κ†μk

, ∀RBtdm,μk

⊂ RBtd,μk

,

(12)∑m

h=1minft

pi∈RBt

dm,µk

{Δtpi} ≥ Δ†

μk. (13)

Furthermore, we also adapt the BFF strategy to formcoalitions. Similarly, we will choose W coalition leaders tobe responsible for this coalition forming. Considering that notall the applications of TFB demanders can be accepted dueto the factor of supplied resources capacity, W ≤ |Φ∗|. Thecoalition leader selection rule is just like the Definition 7, i.e.,{l1, l2, ...lW } Δ

= argminμj∈Φ∗,ftpi

∈RBtd

∣∣∣φ†μk−Θt

ftpi

∣∣∣. The

SUs’ functions set applying for the TFBs in RBtd is denoted by

Dd at time t, and the aggregated TFB providers set is denotedby BΔ = {BΔ

μ1, BΔ

μ2, ..., BΔ

μW}. The DFSS algorithm for TFB

providers’ cooperation is shown in Algorithm 2.

V. PERFORMANCE EVALUATION

In this section, we evaluate the performance of DFSSalgorithm using Matlab. To verify the effectiveness of the pro-posed dynamic resource demand-supply matching approach,the simulation scenario is simplified by setting ωt

piand ωt

μj

to be constant. The detailed simulation parameters are shownin Table 2. To specify the simulation results, Fig. 4 and Fig.5 show the packed results under random packing strategy andour proposed DFSS algorithm for Case 1. In the two figures,there are two provided RBs for the SUs’ sharing. For the ran-dom packing, we do not consider the minimal surplus strategyin the packing process. Obviously, the DFSS algorithm canminimize the surplus space in the time-frequency block ofRBs. Furthermore, Fig. 6 shows that the achieved utilizationratio by three typical packing approaches. Specifically, byDFSS algorithm, the highest spectrum utilization ratio is about

Algorithm 2 DFSS algorithm for TFB providers’ cooperation.

Input: RBtd and Dd,

Output: BΔ = {BΔμ1, BΔ

μ2, ..., BΔ

W },1: Initialize: t, ρmax

pi,t , gpi , BΔ = 0 and σ2

pi,t

2: while (RBtd = ∅ && Dd = ∅) do

3: Sort(RBtd) with ↓ Θt

pi

4: Sort(Dd) with ↓ φ†µj

5: Coalition leader ← {l1, l2, ...lW }6: BΔ ← Coalition leader7: �← 18: while (SAR condition(�)) do9: Minimize surplus RBdemand

10: BΔ = [BΔ B�]11: �← �+ 112: else while13: end while14: Return BΔ = {BΔ

μ1, BΔ

μ2, ..., BΔ

W }15: t← t+ 1

96.09%, and the lowest utilization ratio can also reach upto 88.48%, which is at least 25.75% higher than the utiliza-tion ratio achieved by the random packing. Compared withone typical two-dimensional packing algorithm, i.e., SHELF-BWF algorithm in [35], which considers the strategy that theremaining width of the shelf space is minimized, the DFSSalgorithm can achieve nearly the same average packed ratio.However, the time complexity of SHELF-BWF algorithm isO(n2), hence, the dynamic spectrum matching time of DFSSalgorithm can be greatly reduced. More importantly, as a fastconvergence algorithm, the DFSS algorithm can quickly findthe optimal SUs to form coalitional group for the spectrumsharing. Fig. 7 shows that DFSS algorithm can reach 95%packed ratio with roughly 1/3 of the packing time by randompacking.

We also present the simulation results on the performance ofTFB providers’ cooperation case. Via the packing process of

ZHOU et al.: A COOPERATIVE MATCHING APPROACH FOR RESOURCE MANAGEMENT IN DYNAMIC SPECTRUM ACCESS NETWORKS 1055

1 2 3 4 5 6 7 8 9

40%

50%

60%

70%

80%

90%

100%

The number of RBs provided

The

aver

age

pack

ed r

atio

for

each

RB

The packed ratio by SHELF−BWF algorithmThe packed ratio by DFSS algorithmThe packed ratio by random packing strategy

Fig. 6. The comparison of packing results by different algorithms in Case 1.

0 10 20 30 40 50 600

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

The number of SUs selected for RB packing

The

pack

ed r

atio

of t

he c

apac

ity in

one

RB

The packed ratio by DFSS

The packed ratio by random packing

The ratio gap when DFSS has completed packing process

Fig. 7. The tracking process comparison between the DFSS and randompacking.

0 5 10 15 190

5

10

15

17

Time−slot size of SU’s demand

Spe

ctru

m s

ize

of S

U’s

dem

and

20 30 40 50

26

28

30

32

34

36

38

40

42

44

46

Time−slot size of providers’ blocks

Spe

ctru

m s

ize

of p

rovi

ders

’ blo

cks

Fig. 8. An illustration of cooperation in Case 2.

0 1 2 3 4 5 6 7 8 90

1000

2000

3000

4000

5000

6000

The number of RBs demanders

The

num

ber

of p

acke

d tim

e−fr

eque

ncy

units

The total packed number of units by random packing approachThe total packed number of units by DFSS algorithmThe total packed number of units by SHELF−BWF algorithm

The surplus packed time−frequency units by random packing approach

Fig. 9. The comparison of packing results by different algorithms in Case 2.

TABLE IIPARAMETERS USED IN THE SIMULATIONS

Parameters ValueNumber of TFBs in F t

p at t [1,10]Number of SUs in Dp at t [1,2000]Number of TFBs in F t

d at t [1,500]Number of SUs in Dd at t [1,10]

κtpi

in F tp at t [30,60]

Δtpi

in F tp at t [30,60]

ꆵj

in Dp at t [1,11]

Δ†µj

in Dp at t [1,11]κtpi

in F td at t [1,12]

Δtpi

in F td at t [1,12]

ꆵj

in Dd at t [16,32]

Δ†µj

in Dd at t [16,32]simulation times 100

DFSS algorithm, different scattered small sized TFBs can beaggregated to meet the resource requirement of one specificalindividual SU, as shown in Fig. 8. To compare the perfor-mance of packing process in Case 2 among the SHELF-BWFalgorithm, DFSS algorithm, and random packing strategy,the detailed packed results under different number of TFBdemanders are illustrated in Fig. 9. From the physical meaning

of packed results for TFBs aggregation in Case 2, the less over-packed TFB space, the higher utilization that the TFBs willhave. The data shows that, for the DFSS algorithm, the packedTFB units nearly meet the requirement of the demandedTFB units. Specifically, the maximal surplus ratio of DFSSalgorithm is only about 1.76% more than that of the SHELF-BWF algorithm. However, for random packing strategy, atleast 43.08% time-frequency TFB capacity is wasted whichindicates the significant performance improvement providedby the DFSS algorithm for the two sides matching problem.

VI. CONCLUSION

In this paper, we have investigated the resource managementproblem for DSA in cellular networks using external sensingagents, and formulated the resource management problem asa dynamic spectrum supply-demand matching problem. Thetime and frequency domains are jointly considered to improvethe utilization of unused spectrum in cellular networks, whichhas made the dynamic spectrum resource management andsharing approach more rational and effective. Furthermore, wehave discussed the massive sized and small sized TFB match-ing cases, and the “wholesale” sharing approach and resource“aggregation” sharing approach are proposed, respectively.

1056 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 13, NO. 2, FEBRUARY 2014

Finally, we have designed a distributed fast spectrum sharingalgorithm which can be applied in the real external sensingagents aided dynamic spectrum access scenarios. For futurework, the effects of imperfect sensing on the DSA serviceswill be considered. Furthermore, we will design the marketingcompetition scheme for DSA in the cellular networks.

ACKNOWLEDGMENT

This work was supported in part by the Major State Ba-sic Research Development Program of China (973 Program)(No. 2009CB320403), National Natural Science Foundationof China (61221001, 61201222,61100213), the 111 Project(B07022) , China Scholarship Council, Shanghai Key Lab-oratory of Digital Media Processing and Transmissions, thefunds of MIIT of China (Grant No. 2011ZX03001-007-03),and the Natural Sciences and Engineering Research Council(NSERC) of Canada.

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Haibo Zhou (S’11) received the M.Sc. degree inInformation and Communication Engineering fromUniversity of Electronic Science and Technology ofChina, Chengdu, China, in 2007. He is currently pur-suing his Ph.D. degree in Shanghai Jiao Tong Uni-versity, China. His current research interests includeresource management and performance analysis incognitive radio networks and vehicular networks.

Bo Liu (M’10) received the B.Sc. degree from theDepartment of Computer Science and Technology,Nanjing University of Posts and Telecommunica-tions, Nanjing, China, in 2004, the M.Sc. and Ph.Ddegree from the Department of Electronic Engi-neering, Shanghai Jiao Tong University, Shanghai,China, in 2007, and 2010 respectively. Now he is anassistant researcher in the Department of ElectronicEngineering of Shanghai Jiao Tong University. Hisresearch interests include HDTV and broadbandwireless communications.

ZHOU et al.: A COOPERATIVE MATCHING APPROACH FOR RESOURCE MANAGEMENT IN DYNAMIC SPECTRUM ACCESS NETWORKS 1057

Yongkang Liu received the Ph.D. degree from theDepartment of Electrical and Computer Engineering,University of Waterloo, Canada. He is currently aPost-Doctoral Fellow with the Broadband Commu-nications Research (BBCR) Group, University ofWaterloo. His research interests include protocolanalysis and resource management in wireless com-munications and networking, with special interest inspectrum and energy efficient wireless communica-tion networks.

Ning Zhang (S’12) received the B.Sc. degree fromBeijing Jiaotong University and the M.Sc. degreefrom Beijing University of Posts and Telecom-munications, Beijing, China, in 2007 and 2010,respectively. He is currently working toward thePh.D. degree with the Department of Electricaland Computer Engineering, University of Waterloo,Waterloo, ON, Canada. His current research inter-ests include cooperative networking, cognitive radionetworks, physical layer security, and vehicular net-works.

Lin Gui (M’08) received the Ph.D. degree fromZhejiang University, Hangzhou, China, in 2002.Since 2002, she has been with the Institute ofWireless Communication Technology, Shanghai JiaoTong University, Shanghai, China. Currently, Sheis a Professor in Shanghai Jiao Tong University.Her current research interests include HDTV andwireless communications.

Ying Li received her Ph.D degree from the De-partment of Electronic Engineering, National Uni-versity of Defence Technology. Currently, she is aresearcher and engineer in China Electronic SystemEngineering Company, Beijing. Her main researchinterests include: Signal Processing, MIMO, Cogni-tive Radio.

Shernman Shen (M’97-SM’02-F’09) received theB.Sc.(1982) degree from Dalian Maritime Univer-sity (China) and the M.Sc. (1987) and Ph.D. degrees(1990) from Rutgers University, New Jersey (USA),all in electrical engineering. He is a Professor andUniversity Research Chair, Department of Electricaland Computer Engineering, University of Waterloo,Canada. He was the Associate Chair for GraduateStudies from 2004 to 2008. Dr. Shen’s researchfocuses on resource management in interconnectedwireless/wired networks, wireless network security,

social networks, smart grid, and vehicular ad hoc and sensor networks. He isa co-author/editor of six books, and has published more than 600 papers andbook chapters in wireless communications and networks, control and filtering.Dr. Shen served as the Technical Program Committee Chair/Co-Chair forIEEE Infocom’14, IEEE VTC’10 Fall, the Symposia Chair for IEEE ICC’10,the Tutorial Chair for IEEE VTC’11 Spring and IEEE ICC’08, the TechnicalProgram Committee Chair for IEEE Globecom’07, the General Co-Chair forChinacom’07 and QShine’06, the Chair for IEEE Communications SocietyTechnical Committee on Wireless Communications, and P2P Communicationsand Networking. He also serves/served as the Editor-in-Chief for IEEENetwork, Peer-to-Peer Networking and Application, and IET Communications;a Founding Area Editor for IEEE TRANSACTIONS ON WIRELESS COMMU-NICATIONS; an Associate Editor for IEEE TRANSACTIONS ON VEHICULAR

TECHNOLOGY, Computer Networks, and ACM/Wireless Networks, etc.; andthe Guest Editor for IEEE JOURNAL ON SELECTED AREAS IN COMMUNI-CATIONS, IEEE Wireless Communications, IEEE Communications Magazine,and ACM Mobile Networks and Applications, etc. Dr. Shen received theExcellent Graduate Supervision Award in 2006, and the Outstanding Per-formance Award in 2004, 2007 and 2010 from the University of Waterloo,the Premier’s Research Excellence Award (PREA) in 2003 from the Provinceof Ontario, Canada, and the Distinguished Performance Award in 2002 and2007 from the Faculty of Engineering, University of Waterloo. Dr. Shen isa registered Professional Engineer of Ontario, Canada, an IEEE Fellow, anEngineering Institute of Canada Fellow, a Canadian Academy of EngineeringFellow, and a Distinguished Lecturer of IEEE Vehicular Technology Societyand Communications Society.

Quan Yu received the B.S. degree in informationphysics from Nanjing University in 1986, the M.S.degree in radio wave propagation from Xidian Uni-versity in 1988, and the Ph.D. degree in fiber opticsfrom the University of Limoges in 1992. Since 1992,he joined the faculty of the Institute of China Elec-tronic System Engineering Corporation, where hehas been a Senior Engineer, and currently a ResearchFellow. His main areas of research interest are thearchitecture of wireless networks, optimization ofprotocols and cognitive radios. Dr. Yu is a member

of Chinese Academy of Engineering (CAE).