a coordinated multi-point-based quality of service provision resource allocation scheme with...

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. (2014)

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/wcm.2559

RESEARCH ARTICLE

A coordinated multi-point-based quality of serviceprovision resource allocation scheme withinter-cell interference mitigationMing Gong1, Pin-Han Ho2 and Chih-Hao Lin3*

1Alcatel-Lucent Shanghai Bell Co. Ltd., Shanghai, China2 Electrical and Computer Engineering Department, University of Waterloo, Waterloo, Ontario, Canada3 Department of Information Management, Chung Yuan Christian University, Taoyuan, Taiwan

ABSTRACT

The paper investigates resource allocation via power control for inter-cell interference (ICI) mitigation in an orthogonalfrequency division multiple access-based cellular network. The proposed scheme is featured by a novel subcarrier assign-ment mechanism at a central controller for ICI, which is further incorporated with an intelligent power control scheme.We formulate the system optimization task into a constrained optimization problem for maximizing accepted usersrequirements. To improve the computation efficiency, a fast yet effective heuristic approach is introduced for divideand conquer. Simulation results demonstrate that the proposed resource allocation scheme can significantly improve thenetwork capacity compared with a common approach by frequency reuse. Copyright 2014 John Wiley & Sons, Ltd.

KEYWORDSOFDMA; coordinated multi-point; resource allocation; inter-cell interference

*CorrespondenceChih-Hao Lin, Dept. of Information Management, Chung Yuan Christian University, Taoyuan, Taiwan.E-mail: [email protected]

1. INTRODUCTION

One of the most challenging issues in the modern cel-lular network design is on interference management, andthis becomes more critical when each base station (BS)is getting closer (or the cells become more overlapped),which yields increased inter-cell interferences (ICIs). It hasbeen well recognized as an effective strategy for the ICImanagement by performing a tight coordination amongthe distributed BSs. In general, the geographically dis-tributed BSs are connected to a central office (CO) byhigh-speed cables/fibers where CO-domain is formed, andthe CO has the capability of implementing centralized net-work control and management functions. By taking thebest advantage of coordination among the BSs, an intelli-gent resource allocation mechanism that initiates multi-cellcollaboration should be in place to achieve quality of ser-vice (QoS) provisioning and efficient ICI management forbetter system capacity. This is particularly an issue for 4Gwireless networks such as long-term evolution advanced(LTE-Advanced) [1] and worldwide interoperability formicrowave access-release 2 (WiMAX2) [2] where the

orthogonal frequency division multiple access (OFDMA)technique is employed.

Spatial division multiplexing (SDM) via dynamic powercontrol is a widely employed approach to improve the radiospectrum utilization, in which common radio frequen-cies/bands are reused in different cells for independent andsimultaneous transmissions. It is the main technique forinterference management [3], based on which many previ-ously reported resource allocation algorithms were devel-oped. Other than this, coordinated multi-point (CoMP)transmission that takes advantage of multi-cell dynamicscheduling coordination and cooperative transmission hasalso been considered as another design dimension for theICI mitigation and enhancement in international mobiletelecommunications-advanced (IMT-Advanced) [4].

This paper studies ICI coordination resource alloca-tion in multi-cell OFDMA systems, aiming to achieveimproved network capacity, decreased blocking probabil-ity, and mitigated interferences. A novel ICI coordinationresource allocation scheme is introduced so as to maximizethe frequency reuse via a CoMP subcarrier assignmentscheme and a dynamic power control mechanism. We first

Copyright 2014 John Wiley & Sons, Ltd.

CoMP-based QoS provision resource allocation scheme M. Gong, P.-H. Ho and C.-H. Lin

formulate the task of resource allocation under multi-cellcollaboration into a mathematical programming problem.

Providing user demand for higher data rates and QoSis one of the main motivations of wireless broadband net-work, such as LTE [1]. The users concern is not only tomake a phone call but also to obtain a certain data rate tosupport some Internet broadband services such as multime-dia stream, Internet protocol television (IPTV), and videocall. The quality of these multimedia services definitelylies on the guarantee of allocated bandwidth. Consideringthe limitation of available network resource, a service levelagreement (SLA) between the service provider and net-work operator makes a trade off between increasing thesatisfaction level of the multimedia users and maximiz-ing the operators revenue. Therefore, a resource allocationscheme should be able to not only reserve sufficient band-width as users demands but also maximize the numberof accepted users. So, different from most conventionaloptimal resource allocation schemes that take networkthroughput as the optimization target, we focus on maxi-mizing the number of accepted users demands under thethroughput constraint of each connection.

Because of the huge computation complexity in solv-ing the formulated optimization problem, we introducea novel heuristic approach that sequentially performssubcarrier assignment and power control for each admit-ted connection. In specific, a subcarrier assignmentscheme is first invoked for ICI mitigation, where a CoMPmechanism that provides dynamic scheduling coordina-tion is assumed so as to determine the data rate of eachuser. Then, as the second stage of the heuristic approach,the transmission power of each user is inspected sequen-tially and adjusted such that the power is just sufficient tosupport the specified modulation level. Because the reduc-tion of the transmission power can reduce the interferenceson the neighbor cells, thus the signal-to-interference-plus-noise ratios (SINRs) of the other cells will be reducedaccordingly. The proposed power control scheme goesthrough multiple iterations in order to approach to theoptimal solution.

The contribution of the paper lies in the following twofolds. (1) Formulate the problem of call admission con-trol and resource allocation in cellular networks with ICIby maximizing the number of accepted users demands.(2) Develop a two-stage heuristic approach to the formu-lated problem, where subcarrier assignment is performedfirst followed by power control. We will show that theproposed strategy can significantly outperform the otherconventional spatial reuse schemes.

The rest of the paper is organized as follows. InSection 2, we review the existed several radio resourceallocation technologies of OFDM networks. Section 3describes a CO-domain system model, CoMP and prop-agation model used in this paper. In Section 4, we for-mulate the optimization problem of resource allocationunder multi-cell collaboration with the objective of maxi-mizing number of accepted users demands with the totaltransmission power constrained. Section 5 presents our

heuristic solution to the optimization problem and ournovel CoMP resource allocation algorithm. We verify theproposed schemes through extensive simulation and willdemonstrate its effectiveness, fairness, and efficiency inSection 6. Finally, we conclude this paper in Section 7.

2. RELATED WORK

A mobile user device in a cellular network can be generallyaffected by intra-cell interferences and ICI. The intra-cell interference is mostly due to the frequency-selectivemulti-path propagation that distorts the signal orthogonal-ity in each OFDM pulse, while the ICI occurs amongthe transmissions on common frequency bands within twogeographically adjacent cells. To achieve interference mit-igation, some research efforts have been reported in thedevice level via interference cancellation [5,6]. At the sys-tem level, methods of interference avoidance for single-cellinterference scenario have been extensively studied andfurther formulated as problems of resource allocation andscheduling.

Different from single cell schemes, such as that in [7],the design of a multi-cell resource allocation algorithmmust consider the interference as a key issue [8], and inmany cases, a centralized scheme can significantly outper-form a distributed version at the expense of computationcomplexity [9].

Frequency reuse (FR) [10] via SDM is a widelyemployed design principle to improve the spectrum uti-lization and avoid possible ICI by assigning different fre-quency bands to different cells. With an FR scheme, the fullbandwidth (B) is divided into X sub-bands (BX D B=X),and the parameter X is also known as FR factor. Each BSis assigned one of the sub-bands. The BSs using the samesub-band are defined in a common FR group (frgx). So,there are X FR groups in a FR scheme. Interference onlyexists among the BSs in the same frgx. In general, BSsthat are geographically close to each other should belongto different frgs in order to ensure interference free.

Obviously, FR1 leads to a universal reuse of frequencyspectrum where every cell accesses a common set offrequency bands in which serious ICI could be inducedespecially for those edge users. FR-M (with M as the num-ber of BSs in the CO-domain) is in the other extremeof the design spectrum where every BS uses a differ-ent frequency sub-band from the other, yielding an FRfactor as M. This deployment leads to the lowest inter-ference within a CO-domain at the expense of possiblyvery low frequency resource utilization. A classical inter-ference avoidance scheme that has been widely employedin the state-of-the-art cellular systems is a tradeoff of abovetwo, called reuse 3 (FR3), which divides the frequencyinto three equal sub-bands. FR3 can promise that all adja-cent cells always use different frequencies to avoid anyinterference between two adjacent cells.

FR3 can achieve better frequency spectrum utilizationthan FR1 and FR-M; however, there are only 1=3 total

Wirel. Commun. Mob. Comput. (2014) 2014 John Wiley & Sons, Ltd.DOI: 10.1002/wcm

M. Gong, P.-H. Ho and C.-H. Lin CoMP-based QoS provision resource allocation scheme

spectrum resources available in each cell. Thus, fractionalFR (FFR) design principle was developed to improve FRx,which involves partitioning the network spectrum into anumber of sub-bands and assigning a given sub-band toeach cell based on some carefully designed frequencyplanning and transmission power allocation manners thatminimize ICI. In [11], partial FR (PFR) is employed asthe means of ICI mitigation and load balancing in orderto improve cell edge performance and support a morebalanced data rate among all users.

It is clear that the users could be subject to serious ICIor low spectrum utilization by using the traditional FRxschemes; thus, FFR schemes were developed as a remedy,such as PFR [12] and soft FR (SFR) [13].

In [11], PFR is employed as the means of ICI mitigationand load balancing in order to improve cell edge perfor-mance and enable balanced data rates among all users. PFRemploys a zone-based reuse factor in central and edge areasof a cell, respectively. The full system bandwidth is dividedinto general and special sub-bands where the former canonly be used by central users and the reuse factor is 1,while the reuse factor of special sub-band is X.X > 1/,normally X D 3. Thus, each cell can only use no morethan 1=X special sub-band, and the edge users can onlyuse special sub-band. A fixed power control scheme canbe employed under PFR, by allocating lower transmissionpower to the general sub-band and a higher power to thespecial sub-band users.

Contrary to PFR, the idea of the SFR is to use all ofthe resources so that the whole spectrum can be used byevery cell. To regulate the spectrum usage and reduce inter-ferences, zone-based reuse factors are also employed inthe cell-center and cell-edge areas. First, FR3 is employedin the cell-center area, and the rest frequency is used inthe cell-edge area. Second, it uses a lower power for thecenter users because they are affected by lower ICI, andmeanwhile the ICI to the other cells can be minimal.

We envision that dynamic FR is a key to enlarge the cellbandwidth and increase the spectrum utilization. In [14]and [15], SFR schemes were developed for maximizingthe overall data rate, which aims to dynamically identifyedge users with different transmission power according tothe users distribution. The study in [16] allowed the net-work to reuse frequency with various reuse factors basedon the same design premises. The study in [17] presented adynamic interference avoidance scheme that makes use ofinter-cell coordination for interference mitigation withoutrelying on any frequency planning strategy. Their schemeoutperforms PFR in terms of cell throughput. In [18],a distributed game theory-based approach was developedto adaptively allocate the sub-channels and transmissionpower for each user in multi-cell OFDMA networks. Toregulate the competition for the resource usage, the pro-posed scheme employs a virtual referee scheme, whichmakes it outperform an iterative water-filling methodin terms of both transmission power consumption andoverall throughput.

To the best of our survey, most existing dynamicresource allocation algorithms took a common scenariothat aims to maximize network throughput under the mini-mum users data rate constraint, such as in [19]. However,if the network has no enough bandwidth resource to acceptall the users by satisfying the constraint of users mini-mum data rate requirement, the optimal solution becomesunsolvable. We argue that in real networks these kinds ofschemes may fail to satisfy the users requirements, andthis is particularly an issue when service requirement ofeach user is strictly defined, which can hardly addressed bymost existing researches. Further, most existing schemesuse Shannons formula to estimate the channel data rate;however, in real mobile networks, the channel data rateis achieved by a practical coding scheme. In [20], theOFDM technique combined with adaptive modulation andcoding (AMC) was presented to raise the overall systemthroughput. In [21], two AMC approaches were proposedfor OFDM systems, which achieved better performanceboth in terms of error probability and data throughput.

Another effective approach to resource allocation in cel-lular networks is via dynamic scheduling of each userspriority under a given design target, such as fairness, max-imum throughput, and maximum acceptance rate [22].Jointly considering all the tasks for scheduling, channelassignment, and power control techniques is expected toimprove capacity performance at the expense of increasedcomplexity.

3. SYSTEM MODEL

3.1. Network structure

The study considers a general system model that containsa CO-domain of a multi-cell OFDM network as shownin Figure 1, composed of a CO connected with multipleOFDM BSs as front-end wireless access stations.

The CO corresponds to the centralized resource allo-cation for all the users in the CO-domain. By assuminghigh-speed wired connection, the CO can dynamically

Figure 1. A central office domain. OFDMA, orthogonalfrequency division multiple access; BS, base station.



Table I. Notations.

Symbol Notation

N Number of total usersM Number of total BSsS Number of subcarriers in full bandwidth

pRen,m,sReceived signal power of user nfrom BS m in subcarrier s

pTrm,s Transmission power of BS m in subcarrier spIn,s Interference power of user n in subcarrier sn,s SINR of user n in subcarrier sdn,m Distance between user n and BS m Path-loss exponentN0 Thermal noise powerJ Number of MCS levelsMCSj The jth MCS levelj Required SINR of the jth MCSCj Data rate of the jth MCScn,s Channel data rate of user n in subcarrier sB Total wireless bandwidth (Hz)Bsc Bandwidth (Hz) of single subcarrierAm,n Associating indicator of BS m and user nvn,s Allocating indicator of user n in subcarrier sRn Data rate of user nRreqn Data rate required by user nPTrm Max transmission power of BS msreqn Number of subcarriers required by user ssn Number of subcarriers allocated to user sfrgx Frequency reuse group xPc, Pe Transmission power for central and edge users

BS, base station.

collect, store, and update the network status, such aschannel status information (CSI), subcarrier usage andtransmission power cost of each BS, and the data raterequirement of each end-user.

We assume OFDMA as the multiple access technol-ogy in each CO-domain where the network resource is interms of subcarriers, where the instantaneous bandwidth ofa user can be determined by the power allocated and themodulation-and-coding schemes (MCSs) employed. Thenotations of this paper are listed in Table I.

3.2. Coordination multiple pointtransmission

One of the most important features in 4G mobile sys-tems is CoMP transmission [4]. There are two types ofCoMP transmissions, namely inter-cell cooperative trans-mission and multi-cell dynamic scheduling coordinationtransmission, respectively. The former is used to improvethe users SNR by using signal from two or three BSs thatare synchronized to achieve cooperative transmissions tothe user. Although considered very effective in maximizingthroughput by exploring channel diversity, it is difficult inpractice because of huge control complexity and stringentprecision requirement. The latter uses a central coordinatorfor multiple cells that assigns radio channels to each user

in order to reduce ICI, which is subject to less implemen-tation difficulty, and is considered as a promising approachfor ICI mitigation in IMT-Advance. We will focus on thesecond type of CoMP transmission in the rest of this paper.

3.3. Propagation model

Assume each user can only be associated with a single BSat a moment. With BS m and an associated user n, thereceived signal power in subcarrier s at the user, denoted aspRen,m,s, is estimated by a function f ./ of the transmissionpower at the BS.

pRen,m,s D fpTrm,s

(1)The interference power to user n in subcarrier s, denoted

as pIn,s, is calculated as the summation of the receivedpower from the BSs other than the associated BS m.

pIn,s DMX

kD1,kmpRen,k,s (2)

The SINR (n,s) of user n associated with BS m insubcarrier s is expressed as follows:

n,s DpRen,m,s

N0 C pIn,s(3)

Without loss of generality, we assume that the ICIimposes the major vicious effect to the quality of receivedsignals of each user, and all the other fading effects, such asthose due to geographical limitations and malicious/illegalaccess of the license bands, are not considered. By takingthe path loss model in [10] for signal power of user n fromBS m, Equation (1) can be written as

pRen,m,s D f .pTrm,s/ D pTrm,s

d0dn,m

(4)

Then, the SINR in (3) can be calculated as follows:

n,s DpTrm,s

d0

dn,m

N0 C

MPkD1,km

pTrk,s

d0dn,k

(5)

4. PROBLEM FORMULATION

In this section, the resource allocation task in an OFDMAsystem is formulated as a mathematic programming prob-lem with a target of maximizing the number of admittedusers while reducing the BS consumed power in a commonCO-domain. Let the number of BSs and users in aCO-domain be denoted as M and N, respectively.



Suppose the total network bandwidth is BHz, and the num-ber of subcarriers of a BS is S; then, the bandwidth ofsingle subcarrier is Bsc(D B=S). Let n D 1, 2, : : : , N,m D 1, 2, : : : , M, and s D 1, 2, : : : , S be the set of users,BSs, and subcarriers, respectively.

Let the total number of modulation/coding levels bedenoted as J. In general, a modulation/coding level with ahigher rate can be employed only with a larger SINR undera specific bit error rate (BER) threshold and requires lesssubcarriers to achieve a certain throughput. There is a min-imum required j to support the transmission using the jthlevel of modulation denoted as MCSj, 81 j J, with therate Cj in bits per second per hertz (b/s/Hz). The MCS levelof user n (MCSn,s) is selected based on the SINR (n,s) ofthe subcarrier s allocated to the user from its associated BSm, and the channel data rate cn,s is obtained as

cn,s D fmcs.n,s/ D Cj D C.MCSj/jC1 > n,s j

(6)

In (6), the correlative rate of each modulation level isorganized in the order such that Cj < CjC1, for 1 j n,s j

(14)

Above SINR n,s is obtained by (5), which isrewritten as:

n,s D

MPmD1

Am,nvn,spTrm,s

d0dn,m

N0 CMP

mD1.1 Am,n/pTrm,s

d0

dn,m

(15)

(7) Transmission power constraint:

n,s j (16)

(8) VariablesAm,n 2 f0, 1g (17)

vn,s 2 f0, 1g (18)

pTrm,s 2 0, PTrm (19)

In the objective function (8),MP

mD1Am,n D 1 means user n

is accepted in the network, and the rejected user is indi-cated by

MPmD1

Am,n D 0. So, the target maximum number



of accepted users equals N. The users QoS requirementsare guaranteed by Constraint (1). Constraint (2) ensureseach user to be served only by one BS. Constraints (3) and(4) are the upper bounds on the number of subcarriers ofa network and a BS, respectively. Constraint (5) ensureseach subcarrier of a BS is allocated to one user. Constraint(6) represents the relation between allocated power andmodulation schemes. From (14), to support a channel datarate Cj, the SINR (n,s) has to be no lower than j. TheSINR (n,s) is controlled by the transmission power fromthe associated BS and the interference power from otherBSs in (15). Constraint (7) defines that the transmissionpower should yield a receiving SINR (i.e., n,s) no lessthan the minimum required SINR required for the specificmodulation/code level.

The above mathematic program is nonlinear and canhardly be used for the task of dynamic resource allocation.Thus, the rest of the paper will focus on the introduction ofa novel heuristics to solving the formulated problem.

5. THE HEURISTIC COMPRESOURCE ALLOCATION SCHEME

5.1. Analysis of optimization to heuristic

Although the formulated optimization problem can yieldoptimal solution, it is not tractable to solve because of thenonlinear nature and discrete solution space. Thus, we turnto design of a heuristic approach aiming at a systematicapproach that can practically obtain effective solutions.

From (14), we know the higher SINR(n,s), the highercn,s can be selected, then network can cost less subcarri-ers (s) to accept a users requirement. Equation (15) showsthat the subcarrier s is allocated to the user n and s is notused in its neighbor cells, then a higher SINR(n,s) canbe yielded. Equation (12) shows that the upper-boundednumber of subcarriers of a cell is the full bandwidth, soan efficient scheme should be able to use whole band-width to accept more users requirements. According tothe aforementioned analysis, we need to design a scheme,which can avoid neighbor cell to use same subcarrier andallow each cell to use whole bandwidth at the same time.So, we proposed an enhanced SFR scheme (SFRE) inthe first stage. Equation (12) also shows that SINR canbe increased by decreasing the interference power that isthe neighbor cells transmission power pTrm,s. So, we pro-posed a power control scheme in the second stage todecrease interference.

The proposed heuristic approach is performed in twostages. In a nutshell, the first stage is for subcarrier alloca-tion, which strategically assigns subcarriers to each user ina BS with the highest possible transmission power. Obvi-ously, by doing so, the number of accepted calls is thelower bound; thus, in the second stage, we try to reducethe transmission power such that each call can be suf-ficiently supported under the corresponding modulation

levels and possibly accept more calls. This leads to aniterative transmission power reduction process due to thefollowing fact: when any BS takes a smaller transmissionpower, its interferences upon the neighbor BSs are reducedaccordingly. Thus, the neighbor BSs can reduce their trans-mission powers, which further make other BSs to be ableto reduce their transmissions powers.

5.2. Fairness analysis

The optimization problem in (8) is cast such that, intu-itively, the solution may result in too many low throughputrequirements being selected. This may lead to starvation ofthe users that demand high throughput, which may even-tually degrade the provided service. Because the heuristicscheme deals each users requirement according to theorder processing, the user with high priority can be allo-cated resource early. In order to avoid the unfair situation,fairness should be considered in designing the prioritystrategy of users sorting progress before the first stage. Thepriority of the calls could be due to the type of service ofeach user, the throughput of each requirement, the historyof each requirement, and whether the requirement being ahandover call or a new call.

5.3. The heuristic scheme

Based on the aforementioned heuristic approach, wedesign a novel CoMP-based resource allocation scheme,denoted as SubOpt, in two stages. We enhance the SFRscheme, called SFRE for the first stage. The second stagetries to increase users SINR by reducing the interferenceBSs transmission power. In the case that a users SINRis increased and a higher MCS is allowed, some subcarri-ers that were consumed in the first stage can be releasedand used by some users that were rejected in the previousstage. At the same time, Stage II trims each BSs transmitpower to the minimal while supporting the MCS level ofeach user. The two stages of the proposed heuristic schemeare detailed as follows.

Stage I: SFRE for subcarriers assignment

With a set of given calls, the proposed heuristic firstlysorts the calls according to their priorities. The first step ofthe proposed heuristic tries to accept as many calls in eachBSs signal range as possible by assuming the maximumtransmission power for each user. Formally, the SINR ofuser n, which is associated with BS m, can be calculatedby follows:

n,m,s DPTrm

d0

dn,m

N0 C

MPkD1,km

PTrk

d0dn,k

(20)



The channel data rate cn,m,s of user n can be obtained by(14). Then, the number of subcarriers required by user ncan be obtained by follows:

sreqn D

RreqnBsc cn,m,s

(21)

If the total number of subcarriers assigned to all users ofa BS is no more than the number of subcarriers of the BSP

n2m sn S, all users of BS m are accepted. Otherwise,

some users will be rejected.System tries to assign subcarriers to each user n (n D

1, N) by the following steps.

Step 1: Assign user n to BS m with the strongest sensedsignal.

Step 2: Initiate interference BSs group I for each user,which contains all BSs besides the associatingBS m, I D f1..i..Mji mg. Initiate non-interference BSs group Hi D . The number ofnon-interference BSs in the group is 0, D 0.

Step 3: Estimate SINR of user n using the followingequations, which are obtained from (20). Supposethe BS m is in the frgx (m 2 frgx)

IF n is an edge user dn,m > Dc, s frgy

n,m,sDPe

d0

dn,m

N0C

MPk2frgy&k2I

Pc

d0dn,k

C MPkfrgy&k2I

Pe

d0dn,k

(22)

IF n is a central user dn,m 6 Dc

n,m,sDPc

d0

dn,m

N0C

MPk2frgx&k2I

Pc

d0dn,k

C MPkfrgx&k2I

Pe

d0dn,k

(23)

Step 4: Obtain the channel data rate per subcarrier of usern by cn,m,s D Cj D C.MCSj/ D fmcs.n,m,s/.Then, calculate the number of subcarriers to berequired to satisfy the data rate requirement of usern by (21).

Step 5: Find the highest interference BS i to user n in theinterference BSs group I. Renew the group I byremoving the BS i, I D I i. Then, add the BSi into non-interference BSs group Hi D Hi [ i.Renew the number of non-interference BSs by D C 1.

Step 6: Estimate a new SINR ( 0n,m,s) of the user n fromthe BSs in the renewed interference BSs group Iby (22) and (23).

Step 7: According 0n,m,s, the channel data rate per sub-carrier of user n is obtained by c0n,m,s D Cj DC.MCSj/ D fmcs. 0n,m,s/. Then, calculate the new

number of subcarriers s0n to be required to satisfythe data rate requirement of user n by (21).

Step 8: Compare the subcarriers cost (sn and s0n) as fol-lows: Let

SCcost D sreqn

s0n(24)

If

SCcost C , 2 0.7, 1 (25)

Then, sreqn D s0n and GOTO Step 5, else GOTOStep 9.

Step 9: Check whether BS m has sufficient available sub-carriers (sreqn). IF the user is an edge user,SWITCH to Step 9.1. IF the user is a central user,SWITCH to Step 9.2.

Step 9.1: For edge user. Check whether the BSm has enough available subcarriers offrequency group frgy. If YES, thenGOTO Step 10. If NO, check whetherall the frequency groups of the BS medge have been tried; If YES, then theuser will be rejected, If NO, GOTOStep 2 then try to use next frequencygroup.

Step 9.2: For central user. Check whether theBS m has enough available subcarriers.If YES, then GOTO Step 10; If NO, set nas an edge user then GOTO Step 2.

Step 10: Check whether all non-interference BSs in groupHi have sufficient available subcarriers (sreqn ),AND these available subcarriers must be at samepositions.

YES, then the user will be accepted by BS m. Thenetwork updates the subcarriers usage information ofthe BSs.

NO, for edge user, check whether all the frequencygroups of the BS m edge have been tried. If YES, thenthe user will be rejected. If NO, GOTO Step 2 then tryto use next frequency group. For central user, set n asan edge user then GOTO Step 2.

The first step yields a tentative solution, which couldbe far from optimal, because some users are granted withmore capacity than required. In the second step, all the BSscooperatively adjust their transmission powers on everysubcarrier to reduce the ICI and then to accept more users.Then, the other BSs may be able to select higher modula-tion levels because of reduced ICI. As a result, these BSs



can save some free subcarriers for accepting more userswho are rejected in stage I.

Stage II: power adjustment and subcarriersreassignment

The aforementioned subcarriers assignment stage,which assumes maximum transmission power at each BS,certainly achieves the maximum transmission range butalso the maximum interference to adjacent BSs. Thus, itis important to minimize the transmission power to eachuser in each subcarrier such that the SINR of the corre-sponding MCS level at the subcarrier can be just supported.With the reduced transmission power at a BS of a sub-carrier, the other users that are not associated with the BSmay become subject to less interferences at the subcarrier,whereby the users could possibly take a higher level ofMCS. Alternatively, some rejected users requirement inStage I Step 9 could be admitted by reassigning the savedsubcarriers.

The power adjustment and subcarriers re-assignmentprocess is described as follows:

Step 1: Initiate a BS group P , put the BS(s), which haveaccepted all their associating users, into the groupP . M0 is number of BSs in the P . If M0 D 0, puta BS, which rejects the least number of demands,into group P , then M0 D 1.

Step 2: Reset the transmission power of all BSs in P .Let jth MCS be allocated on subcarrier s of BSm; the minimum SINR to support jth MCS isj, which requires minimum transmission power(PREQ). Then, the transmission power of BS mallocated to subcarrier s, denoted as pTrm,s, can bereset by following:

for all BS m 2 P dofor all subcarrier s of BS m do

PREQ .1C/j

N0C

MPkD1,km

pTrk,s

d0dn,k

!

d0dn,m

(26)

pTrm,s

PREQ pTrm,s PREQ > pTrm,s otherwise

(27)

end forend for

Step 3: If the transmission power on any subcarrier isupdated at Step 2, then repeat Step 2.

Step 4: Try to accept the rejected users of BS m in Stage Iif M0 D M then

FINISH Stage II.else

for all BS m P dofor all user n associated with BS m do

n,m,s DpTrm,s

d0

dn,m

N0 C

MPk2I

pTrk,s

d0dn,k

(28)

pTrm,s D

Pe s edge frequency groupsPc s central frequency group

pTrk,sD(

Pe k P , s edge frequency groupsPc k P , s central frequency grouppTrk,s k 2 P

cn,m,s D Cj D C.MCSj/ D fmcs.n,m,s/

sreqn D

RreqnBsc cn,m,s

if srest_frgm > sreqn thenuser n is accepted;s

rest_frgm srest_frgm sn;

elseuser n is rejected;

end ifend for

end forend if

srest_frg means the number of remaining avail-able subcarriers of the frequency group, which thesubcarrier s is belonged to.

Step 5: Renew the group P . Check whether there is somenew BS(s), which have accepted all users in Step 4.

IF YES, put the BS(s) into P . IF NO, put a BS, which rejects least number of

demands, into P .

Update M0, then GOTO Step 2.

6. SIMULATION RESULTS

6.1. Simulation environment

In this section, the performance of the proposedresource allocation scheme are evaluated and com-pared with four benchmark schemes. In order to showthe enhanced performance of our subcarriers allocationscheme (SFPE), we introduce the performance of ourCoMP resource allocation schemes without and with thepower control step, respectively. The scheme without



(a) 19 cells

(b) 9 cellsFigure 2. A snapshot on the mobile user distribution in the two

test networks.

power control step is denoted as SFPE; the scheme withpower control is denoted as SubOpt. The first and secondbenchmark schemes are conventional FR schemes FR1 andFR3. The third scheme is the partial reuse scheme (denotedas PFR), and the fourth one is the SFR scheme.

The simulation program was coded in C++, and the sim-ulation was conducted on a computer with Intel PentiumDual-Core T2390 (1.86GHz) CPU and 2GB memory. Theperformance metrics of interest in the evaluation of the sixschemes include the total capacity in terms of the num-ber of rejected demands, total channel usage of each BS,total power usage of each BS, and the computation timeto obtain feasible solutions. Without loss of generality,

Table II. Simulation parameters setting.

Parameters T B (MHz) PTrm (W) N0 (W)

Value 250 20 20 1 109 2.5The thermal noise power (N0) is assumed equivalent at any point inthe network.

(a) 19 cells

(b) 9 cellsFigure 3. Assignments of frequency sub-bands.

two CO domain contains 19 cells (M D 19) and 9 cells(M D 9) with an equal distance of 1000 m between everypair of adjacent cells as illustrated in Figure 2.

The simulation parameters are summarized in Table II.The FR factors of PFR, SFR, and SFPE are set to threeto make sure that no conjoined cells are using same



frequency sub-band. The assignments of frequency sub-bands are shown in Figure 3.

Table III shows the 10 levels of MCS considered inthe simulation. For each simulation trial, the CO domainis randomly placed with 200 users (N D 200), and allthe users have a common data rate requirement, whichis either 0.5, 0.75, 1, 1.25, or 1.5 Mbps, respectively.Each datum was obtained by averaging the results of50 trials based on a resource allocation scheme under aspecific scenario of data rate requirement. Figure 2 illus-trates the user distribution in a simulation trial, where the

Table III. MCS Levels and related SINR.

Cj MCS levels Rate SINR

C1 QPSK 1/2 R6 0.128 0.32C2 QPSK 1/2 R4 0.192 0.48C3 QPSK 1/2 R2 0.384 0.96C4 QPSK 1/2 0.768 1.93C5 QPSK 3/4 1.152 4.23C6 QAM16 1/2 1.536 7.19C7 QAM16 3/4 2.304 18.48C8 QAM64 2/3 3.072 48.61C9 QAM64 3/4 3.456 62.63C10 QAM64 5/6 3.84 97.00

The bit error is less than 1 106.

(a) 19 cells

(b) 9 cellsFigure 4. Percentage of rejected demands. FR, frequencyreuse; PFR, partial frequency reuse; SFR, soft frequency reuse;SFRE, enhanced SFR scheme; SubOpt, CoMP-based scheme.

big spots represent the BSs and the small ones representthe users.

6.2. Capacity performance

We use the percentage of the rejected demands to showthe capacity performance of the six schemes in Figure 4.The best performance scheme should reject the least num-ber of demands; in other words, it can accept the mostnumber of users requirements, and the network can yieldthe highest capacity. By observing Figure 4, we can findthe proposed resource allocation scheme (SubOpt) rejectsthe least demands, so it has the best capacity performanceamong the six schemes. And both of two new schemes out-perform the other four existing FR schemes. FR1 is basedon the universal FR strategy that uses the maximal band-width resources in each cell; however, it causes high ICIfor edge users, which reduces whole network capacity seri-ously. FR3 uses FR factor as 3, which can make sure eachcell have no ICI among all its neighbor cells, which canincrease the cell capacity; however, each cell in the networkcan only use 1/3 bandwidth. PFR and SFR are improvedfrom FR schemes. PFR avoids using some sub-band fre-quency for ICI mitigation to edge user, so it doesnt usefull spectrum. However, SFR uses fixed power control andfrequency planning for the edge user, so it uses full avail-able frequency. Figure 4 shows that SFR outperforms PFR.

(a) 19 cells

(b) 9 cellsFigure 5. Percentage of subcarriers usage. FR, frequency reuse;PFR, partial frequency reuse; SFR, soft frequency reuse; SFRE,

enhanced SFR scheme; SubOpt, CoMP-based scheme.



Because SFRE is improved from SFR by adding a dynamiccoordinate ICI mitigation mechanism among the BSs, itcan accept more users than SFR. At the same time, shownin Figure 5, SFRE costs the least subcarriers among thesix scheme. The SubOpt scheme uses SFRE as its StageI and a dynamic power control scheme as its Stage II.So, SubOpt can not only efficiently use spectrum but alsodecline the effect of ICI; thus it has the best capacity per-formance. However, in order to accept more users, it costsmore subcarriers than the SFRE scheme.

6.3. Power usage performance

By observing Figure 6, because SubOpt employs a powercontrol stage, the power usage performance of SubOptis much better than other five schemes, which has effec-tively reduced the transmission power usage by more than60%. Because the other five schemes use fixed transmis-sion power, their power consumptions are proportional totheir subcarrier consumptions.

6.4. Computation time

The performance of computation efficiency for eachscheme is shown in Figure 7. The observations are sum-marized as follows. (1) The SubOpt scheme takes a littlebit more computation time than other five schemes because

(a) 19 cells

(b) 9 cellsFigure 6. Percentage of power usage. FR, frequency reuse;PFR, partial frequency reuse; SFR, soft frequency reuse; SFRE,

enhanced SFR scheme; SubOpt, CoMP-based scheme.

(a) 19 cells

(b) 9 cellsFigure 7. Computation time. FR, frequency reuse; PFR, partialfrequency reuse; SFR, soft frequency reuse; SFRE, enhanced

SFR scheme; SubOpt, CoMP-based scheme.

of its power control stage. (2) The computation complex-ities of all the other five schemes, including FR1, FR3,PFR, SFR, and the new SFRE scheme, are similar. And(3) from Figures 4 and 7, we find when the required datarate increases, the rejected number of demands increases,then the computation time of SubOpt also increases. Thereason is that if the number of rejected demands increases,the Stage II of SubOpt would take more time in con-sideration of admitting those originally rejected demands.Overall, the proposed SubOpt scheme can yield a validsolution to the proposed resource allocation problem ina short time. However, if it is under a computation timesensitive scenario, the SFRE scheme may be a betterselection.

7. CONCLUSION

This paper has investigated the problem of resource allo-cation for OFDMA cellular networks by fully exploringICI mitigation. We first formulated the proposed resourceallocation problem into a nonlinear optimization problem,which is nonetheless subject to very high computationcomplexity. Thus, we turned to heuristic approach, wherethe resource allocation problem is divided into two sub-tasks referred to as subcarrier assignment and AMC-basedpower control. The two tasks are solved sequentially and



iteratively until convergence is achieved, which was foundto be capable of effectively resolving the complex rela-tion between the two tasks. Based on the heuristics, wepresent a new heuristic CoMP resource allocation schemefor OFDMA wireless networks. Simulation results showedthat the network capacity can be significantly increased byusing the new scheme compared with four conventionalschemes, FR1, FR3, PFR, and SFR. In particular, the trans-mission power of each BS can be significantly reducedin the power control stage, which effectively reduces theICI. We conclude that the proposed scheme is expectedto contribute to real-time resource allocation operations inpractical cellular networks. In this paper, we assume allusers have the same priority and data rate requirement,which matches a simple practical network operation. In ourfurther research, we may give concern to more complicatedscenario with different priorities and data rate requirementsin the network.

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AUTHORS BIOGRAPHIES

Ming Gong is working as a consul-tant engineer for LTE R&D in Alcatel-Lucent Shanghai Bell Co., Ltd. Hereceived the PhD degree of Electri-cal and Computer Engineering fromthe University of Waterloo in 2013,received the ME degree of ControlEngineering of communication from

Lakehead University in 2007, and received the BS ofElectrical Engineering from Fudan University in 1998. Heworked as an engineer and supervisor at Alcatel ShanghaiBell from July 1998 to August 2005. His research interestsinclude LTE RF fading performance, radio resource man-agement in wireless networks, medium access control, androuting algorithms.

Pin-Han Ho received his BSc andMSc degree from the ElectricalEngineering Department in NationalTaiwan University in 1993 and 1995,respectively, and PhD degree fromQueens University at Kingston at2002. He is now a full professorin the Department of Electrical and

Computer Engineering, University of Waterloo, Canada.Professor Pin-Han Ho is the author/coauthor of more than300 refereed technical papers and several book chapters,and the coauthor of a book on optical networking and sur-vivability. His current research interests cover a wide rangeof topics in broadband-wired and wireless communication

networks. He is the recipient of Distinguished ResearchExcellent Award in the ECE Department of Univer-sity of Waterloo, Early Researcher Award (PremierResearch Excellence Award) in 2005, the Best PaperAward in SPECTS02, ICC05 Optical Networking Sym-posium, and ICC07 Security and Wireless Communi-cations Symposium, and the Outstanding Paper Awardin HPSR02.

Chih-Hao Lin received the BS degreein Computer Science from TamkangUniversity in 1994, the MS degreein Computer Science from NationalCheng Kung University in 1996, andthe PhD degree in Information Man-agement from National Taiwan Uni-versity, Taipei, Taiwan, in 2003. He

is currently an Associate Professor of the Department ofInformation Management, Chung Yuan Christian Univer-sity, Taoyuan, Taiwan. He was the (i) chief consultantof the Automobile Association Division in Yulon MotorCompany, (ii) project leader of Telematics PromotionConsortium in Taiwan, and (iii) visiting researcher ofthe Department of Electrical and Computer Engineering,University of Waterloo, Canada. His research inter-ests include mobile intelligence, network optimization,resource management, and ubiquitous computing as wellas their applications in telematics. He was a recipient ofthe Acer Dragon Thesis Award in 1996, the K. T. Li Foun-dation on the Development of Science and Technologyin 2001, and the Outstanding Academic Research Awardfrom Acer Foundation in 2003.


A coordinated multi-point-based quality of service provision resource allocation scheme with inter-cell interference mitigationABSTRACTIntroductionRelated WorkSystem ModelNetwork structureCoordination multiple point transmissionPropagation model

Problem FormulationThe Heuristic CoMP Resource Allocation SchemeAnalysis of optimization to heuristicFairness analysisThe heuristic scheme

Simulation ResultsSimulation environmentCapacity performancePower usage performanceComputation time

ConclusionREFERENCES

a coordinated multi-point-based quality of service provision resource allocation scheme with...

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