Cost-Minimized Design for TWDM-PON-Based 5G Mobile Backhaul Networks

Hao Chen, Yongcheng Li, Sanjay K. Bose, Weidong Shao, Lian Xiang,Yiran Ma, and Gangxiang Shen

Abstract—Dense deployment of small cells would be re-quired to provide for high capacity and universal accessto future fifth-generation (5G) mobile networks. However,this would require cost-effective and reliable backhaul con-nectivity between these small cells. Time and wavelengthdivisionmultiplexedpassive optical networks (TWDM-PONs)are considered a promising choice for this purpose. In thispaper, we consider the cost-minimized design of a backhaulnetwork for a 5Gmobile systemusing TWDM-PON. For this,equipment and deployment costs are considered, and thedesign is based on satisfying network constraints such asthe maximum number of subscribers per optical line termi-nal and themaximumnumber of subscribers perwavelength.Considering the fact that many small cell base stations aredispersed over an extensive wireless coverage, a K-meansclustering-based algorithm is proposed for the optimal sol-ution. The strategies of using multistage remote nodes andcable conduit sharing are applied to further reduce thelabor cost of trenching and laying fibers. Our simulation re-sults show that the proposed approaches can substantiallyreduce the backhauling cost in comparison with the tradi-tional intuitive random-cut sectoring approach.

Index Terms—Cost minimization planning; Fiber conduitsharing; K-means clustering; Mobile backhaul.

I. INTRODUCTION

D ue to the tremendous growth of mobile data traffic, itis expected that link data rates in the forthcoming

fifth-generation (5G) cellular networks will reach the Gb/slevel [1,2]. Extensive deployment of small cells is acknowl-edged as the only technically viable way to provide the indi-vidual Gb/s access rates promised by the 5G cellularnetworks [3–5]. A small cell is a base station (BS) with areduced network reach compared with a typical macro-cellBS and is expected to cost significantly less. By deploying alarge number of small cells in a microcell network, it wouldbe possible to cater to the expected high bandwidth de-mands of individual users. However, the backhauling costrequired for this, including labor cost, would be high and

should be optimized. As shown in Fig. 1, the backhaul net-work (also known as mobile backhaul) provides communi-cations between multiple BSs and their associated basestation controller (BSC) (also known as the mobile switch-ing node). Here the BSs provide radio coverage to mobileusers in a geographical area, and the BSC is responsiblefor relaying the signals between the BSs and the publicswitched telephone system (PSTN) or the public data net-work (PDN). Current backhaul networks are implementedover copper, fiber, andmicrowave radio links [6–9], but fibersare expected to be the dominant choice in the future becauseof their high bandwidth and long transmission range.

Between the different fiber-based access techniques,time and wavelength division multiplexed passive opticalnetworks (TWDM-PONs) [10–13] have been chosen by thefull service access network (FSAN) community as the pri-mary broadband access solution for future optical accessnetworks. Moreover, in next-generation passive optical net-work stage 2 (NG-PON2) [12,13], the maximum transmis-sion rate per wavelength for TWDM-PON is 10 Gb/s, whichis compatible with the sub-10-Gbps maximum bandwidthper user equipment required by 5G small cells. Because ofthese reasons, techniques of employing TWDM-PON toprovide broad bandwidth for future 5G backhaul traffichas drawn extensive recent interest [11,14].

In this paper, we focus on TWDM-PON-based fiber back-haul for dense deployment of 5G small cells. Becausedeploying these would require enormous investments,efficient and economical solutions for the optimal designof such 5G mobile backhaul networks would be essential.To tackle this, we propose a K-means clustering-basedplanning algorithm for the TWDM-PON-based fiber back-haul network. Because the overall deployment cost is domi-nated by the cost of trenching and laying fibers [15,16], wepropose to further reduce these component costs both byusing adaptive multistage remote nodes (RNs) [17,18]and by maximizing cable conduit sharing [15]. Simulationresults show that the proposed approaches will be moreeffective to substantially reduce the backhauling cost com-pared with a standard benchmark scheme based on anintuitive random-cut sectoring strategy [15]. By taking intoaccount different user bandwidth requirements, the pro-posed design approaches also offer a good compromisebetween deployment cost and average bandwidth per user.

The rest of this paper is organized as follows. InSection II, we review related works. In Section III, wehttp://dx.doi.org/10.1364/JOCN.99.099999

Manuscript received June 6, 2016; revised August 28, 2016; acceptedAugust 29, 2016; published 0, 0000 (Doc. ID 267815).

H. Chen, Y. Li, W. Shao, L. Xiang, and G. Shen (e-mail: shengx@suda.edu.cn) are with the School of Electronic and Information Engineering, SoochowUniversity, Suzhou, Jiangsu Province 215006, China.

Sanjay K. Bose is with the Department of Electronics and ElectricalEngineering, Indian Institute of Technology Guwahati, India.

Y. Ma is with the Beijing Research Institute, China Telecom Co. Ltd.,China.

Chen et al. VOL. 8, NO. 11/NOVEMBER 2016/J. OPT. COMMUN. NETW. 1

1943-0620/16/110001-01 Journal © 2016 Optical Society of America

present the architecture of TWDM-PON-based backhaul-ing deployment and the problem considered in this paper.In Section IV, we propose a K-means clustering-basedalgorithm for this deployment and the strategies of adap-tive multistage RNs and maximal cable conduit sharing.Simulation conditions and test cases are described inSection V. Section VI provides the results of simulationsand analyses on the performance of the system based onthese results. Section VII concludes the paper.

II. LITERATURE REVIEW AND DISCUSSION

Due to the high capacity of a PON, it is a good choice as abackhaul or front-haul for radio access networks to seam-lessly provide wireless and wireline services [9,19–28].Shen et al. [19] devised four basic architectures employingan EPON as a backhaul to interconnect multiple base sta-tions. The integration architecture can take advantage ofthe bandwidth benefit of fiber communications and signifi-cantly reduce the overall design and operational costs fornext-generation broadband access networks. Chung et al.[20] proposed applying two-upstream-wavelength PON(2W-PON) as the backhaul of a wireless network. In [21],Shea and Mitchell demonstrated the feasibility of an archi-tecture that integrates PON infrastructure with DWDMbackhaul to further increase equipment sharing amongusers so that significant cost savings can be made.Cvijetic et al. [25] experimentally demonstrated the firstdownstream/upstream OFDMA/TDMA-PON techniquefor last-mile optical mobile backhaul with 200 cells perfiber and showed promising results for future optical back-haul networks. Similarly, in [26] a novel wireless/opticalnetwork architecture was demonstrated to provide high-capacity backhauling links for mobile WiMAX. A simpleand cost-efficient PON-based mobile backhaul networkarchitecture for the emerging 5G system was proposedin [27]. In these works, a PON-based wireless backhaulingarchitecture not only brings economy in capital expendi-ture but also satisfactorily supports the bandwidth requiredby the subscribers.

Considering the huge investments required for PON-based backhauling deployment, optimal design and plan-ning of these networks would be of paramount importance.There have been extensive studies considering the optimalplacement of optical network units (ONUs) and RNs andplanning of deployed optical fibers [22,23,28–34]. Sarkaret al. [28] proposed ananalyticalmodel called “primalmodel

(PM)” and a heuristic called “cellular heuristic (CH)” for op-timum placement of BSs and ONUs so that the overall de-ployment cost was minimized. Orphanoudakis et al. [29,30]presented the basic steps for converged network design andplanning and evaluated algorithms for wireless backhauloptimization based on the PON architecture. As a less com-putationally complex alternative to the integer linear pro-graming (ILP)-based optimization, Larrabeiti et al. [31]proposed simple heuristic algorithms for this. For small celldeployment with optical fibers as backhaul, Ranaweeraet al. [32–34] developed an optimization framework to opti-mally plan a small cell network and its backhaul networkto satisfy network requirements based on an existing fiberinfrastructure, which is sparsely located.

Various other research works have also been reported onthe optimal design and planning for PON networks in[15–18,35–40]. Li and Shen [15] planned greenfield PONnetworks to minimize their total deployment costs and pro-posed twoheuristic algorithms for the complex optimizationproblem. Zhang and Ansari [18] investigated the issue ofminimizing the cost of optical cables and arrayedwaveguidegratings (AWGs) in WDM-PONs. Ouali and Poon [35] mod-eled the planning problem as a novel mixed integer linearprogramming (MILP) model, with which good solutionscanbe obtained quickly. Chen et al. [16] proposed aK-meansclustering-based approach to plan for such networks. Formultistage planning scenarios, Eira et al. [17] addressedthe problem of optimally designing PONs using multiplestages of optical splitting. An ILP model and an efficientheuristic were also presented. In [36], a multihierarchyPON planning problem considering system attenuationand equipment constraints was addressed, and an ILPmodel and a genetic algorithm were developed. Optimalplacement of RNs was also studied in [37,38]. Jaumardand Chowdhury [37] investigated the network dimension-ing problem and the placement of remote nodes for WDMPONs and formulated the problem as a large-scale optimi-zation model. The authors also optimized the placement ofswitching equipment in a hybrid WDM/TDMPON networkso as to satisfy the demand for given geographical locationsof theONUs [38]. Studies considering real geographical testcases have also been put forward in [39–41]. Agata andNishimura [39] proposed a novel suboptimal design for aPON that connects every end user to the central office(CO) under realistic restrictions involving a real roadmapand a realistic number of forecasted demands. Kipouridiset al. [40] presented a geographic information system basedmodel for the cost efficientplanningofPONs.Zukowski et al.[41] targeted the problem of finding a least cost solution tothe FTTH deployment problem in rural areas, consideringrealistic roadmap scenarios.

Despite all these efforts, significant challenges stillremain in designing and deploying a cost-effective PON-basedmobile backhaul network. There are very few studiesfocusing on planning TWDM-PON-based backhaul net-works in which wavelength, as a new dimension, shouldbe considered jointly. In this paper, we propose to leveragethe architecture for 5G small-cell backhauling. Given thelocations of an optical line terminal (OLT) and BSs, we planthe TWDM-PON topology to backhaul 5G small cells with

Backhaul

PSTN/PDNBS

BS

BSC

Coverage area

Mobile Switching Nodes

Fig. 1. Simplified example of cellular radio access system for dataor voice service.

2 J. OPT. COMMUN. NETW./VOL. 8, NO. 11/NOVEMBER 2016 Chen et al.

the objective of minimizing the total deployment cost. Thekey novel aspects of this work are as follows:

1) The existing studies have implemented BS clusteringbased on the K-means algorithm [39–41], but the RNpositions foundmay not be the optimal locations. In thisstudy, after doing the K-means clustering, we furtheremploy Weiszfeld’s algorithm to decide the optimal po-sitions for RNs. Moreover, as an important novelty, weemploy an iterative process to decide the optimal RNstage number, which is flexible and optimal to allowa PON to have a different number of stages so that aminimum total deployment cost can be guaranteed.

2) Reference [16] is a conference preliminary study ofthe current paper. We have extended [16] to incorporateone more dimension, i.e., wavelength, and includedthat in the optimal network planning for TWDM-PONs. The consideration of the wavelength dimensionis another important novel aspect different from theother existing studies such as [39–41]. Moreover, tominimize the total deployment cost, we maximize cableconduit sharing by jointly considering the strategies ofmultistage RNs and cable conduit sharing among dis-tribution fibers (DFs).

There are still some limitations of our approach. First,the current optimization approach is not globally optimalbecause it does not consider all the steps jointly. Instead,the proposed approach ensures efficiency by making eachstep efficient, as it will be extremely complicated to imple-ment a joint optimization. Second, our test cases are realBS location topologies from an industrial project, wherea direct connection is allowed to be established betweenany pair of nodes. As a limitation, we do not consider somepossible geographical scenarios such as roadmap, detour,and established fiber links.

III. PROBLEM STATEMENT

In this section, we introduce the basic concept and archi-tecture of TWDM-PON and state the related optimizationproblem.

A. TWDM-PON-Based Mobile Backhaul

Using TWDM-PONs for 5G small cell backhauling isidentified as a cost-effective solution. Figure 2 shows sucha backhauling example. The overall system contains anOLT, usually placed at the CO, and several dispersedONUs. The OLT and ONUs are interconnected by an opti-cal distributed network (ODN)-based tree topology withthe OLT as the root node. To backhaul the 5G small cells,each ONU connects to a BS providing network access formobile terminals (i.e., user equipment). Note that, thoughthis study focuses only on the backhaul scenario, the mid-haul links with fiber extension from macro-cell BS to smallcell sites may also be considered. The optimization consid-ering the latter scenario would be a further study, as itwould require some extensions to the current approach.

Depending on the network scenario, the ODN may containeither single-stage or multistage RNs. The RNs connect tothe OLT through feeder fibers (FFs) and to each ONUthrough DFs. The RNs can be either optical splitters (OSs)or arrayed waveguide gratings (AWGs). An optical splitterequally splits the power of an optical signal into multipleportions based on its split ratio (SR), while an AWG iscapable of filtering a specific wavelength at each outputport. When multistage RNs are used, each output port ofan AWG can be further connected to an optical splitter,as shown in Fig. 2. Through the AWG, the wavelength di-vision multiplexing access mode is enabled, while the timedivision multiplexing access (TDMA) mode is applied tomultiple BSs/ONUs that commonly share the bandwidthof a wavelength, i.e., the BSs/ONUs that are attached toa common optical splitter.

B. Optimization Objective and Constraints

For a system as shown in Fig. 2, where the locations ofthe OLT and the BSs/ONUs are given, a major goal is todetermine the planned topology for a TWDM-PON-basedmobile backhaul, which minimizes the total deploymentcost. The total deployment cost specifically comprises ofa set of component costs, which would include 1) the equip-ment costs of the OLT and RN (i.e., the optical splitter andAWG), 2) the labor cost for trenching and laying fibers, and3) the fiber cable cost. In this paper, we do not consider thecost of the ONUs and BSs because these would be fixedgiven the set of dispersed BSs that are to be connected.We also assume that the expense of each OLT is dependenton the number of wavelengths that it supports. In general,an OLT with more wavelengths would cost more.

In general, the cost of trenching and laying fibers isdominant in the total deployment cost; thus we shouldpay more attention to reducing this cost component.Moreover, because this cost is closely related to the totaldistance of laid fibers, minimizing this cost can be modeledto minimize the total distance of trenching and layingfibers. It may be noted that trenching and laying fibers also

Fig. 2. Example of TWDM-PON-based mobile backhaul.

Chen et al. VOL. 8, NO. 11/NOVEMBER 2016/J. OPT. COMMUN. NETW. 3

will have other associated costs such as trenching permitcost and traffic/pedestrian interruption cost. Because theseexpenses are not usually quantifiable or determinablebeforehand, we have chosen to ignore them in this study.

The constraints for PON deployment generally includei) the maximum number of ONUs that each OLT can con-nect to (we assume this to be 16 in this study), ii) the maxi-mum number of ONUs allowed to share the commonbandwidth of a wavelength in the TDM mode so as to sat-isfy the minimum bandwidth guarantee per ONU, iii) themaximum coverage (reach) of a PON, which is defined asthe maximum transmission distance from an OLT to anONU, and iv) the maximum differential distance of a PON,which is defined as the maximum differential distancebetween different ONUs and a common OLT within thesame PON. However, with the maturity of long-reachPONs available today [26], the maximum coverage canbe over 100 km and the maximum differential distanceis up to 80 km, which is long enough to cover a typical met-ropolitan city. Thus, we ignore constraints iii) and iv) andonly consider constraints i) and ii) in this paper.

In addition to the above system constraints, there alsocould be other restrictions, such as a real roadmap, a de-tour factor, and the use of already installed fibers. For thefactors of roadmap and detour, a calculation is needed tofind the shortest distance between any pair of nodes basedon geographic information system information. Then wecan use this distance to replace the direct distance betweeneach node pair. Following this conversion, the same optimi-zation approaches proposed in this paper can be appliedbased on the converted distances and topology. For theconstraint of established fiber links, to minimize the totaldeployment cost, we may need to maximally reuse theseexisting dark fibers. Thus, when planning for the topology,an extra step needed before the optimization is to pre-fixthe edges in a tree topology if these edges already haveexisting dark fibers. Next, we implement the optimizationto find other potential connected links to form a tree top-ology. The pre-established links may affect the overall op-timality of the tree topology compared with the case thatdoes not have this type of constraint. However, consideringthe reuse of the existing fiber links and their conduits, wecan still expect to significantly save cost compared with thecase without such reuse. A study considering these con-straints such as roadmap, detour, and the reuse of existingfiber links would be interesting to explore further based onthe current approach.

The solution to the optimization problem includes the to-tal number of required RNs, the optimal locations of thedeployed RNs, and the association relationships betweenthe BSs/ONUs, RNs, and OLTs. This will decide the totaldistance of laid fibers and the total deployment cost.

We can divide the overall optimization problem into twosub-problems, i.e., i) clustering BSs to determine thegroups of BSs that should be connected to a commonRN, and ii) deciding optimal locations for the RNs thatare associated with different BS clusters. We refer to thefirst subproblem as the BS clustering problem and thesecond subproblem as the RN locating problem.

IV. HEURISTIC APPROACH

In this section, we present a heuristic approach to min-imize the total deployment for the TWDM-PON-based 5Gbackhaul network. The approach consists of three key sub-steps, including BS clustering, RN position locating, andfiber cable conduit sharing. For the BS clustering problem,we employ an extended K-means clustering-based ap-proach [42] to divide many dispersed BSs into differentgroups. For the RN position locating problem, we employWeiszfeld’s algorithm [43] to find an optimal location thatensures the sum distance of each link between a commonRN and each BS to be a minimum. Because the labor costfor trenching and laying fibers is dominant, we maximizecable conduit sharing by employing the multistage RN andminimum spanning tree strategies while laying DFs. Notethat, due to the complexity of considering multiple (notfixed) stages of RNs, it is challenging to develop a MILPmodel for the current optimization problem.

A. BS Clustering

Clustering is a process to group a set of points (whichcorresponds to the location of each BS) into multiplegroups, in each of which the contained points are closeto each other. These groups are called clusters. Figure 3illustrates an example of dividing 10 points into twogroups, in which one cluster contains four points and theother contains the remaining six points. The K-meansalgorithm is a popular approach for the clustering purpose.We can extend this approach to cluster BSs into multiplegroups. In Fig. 3, the cluster centroids (called K-meanscluster centroids) are highlighted as stars. These centroidsare the location for each cluster that guarantees theminimum sum of squares of the distances between eachcontained cluster point and its cluster centroid.

The flow chart in Fig. 4 illustrates the procedure of theproposed K-means clustering heuristic. In Step 1, we inputthe design parameters including the coordinate informa-tion of the CO and all BSs and the constraint informationsuch as the maximum number of ONUs supported by eachOLT, SOLT, and the maximum number of ONUs supportedby each wavelength, Sw. In Step 2, we set M to be thesquare root of the total number of BSs. The number of clus-ters that can achieve the best design is found by theK-means clustering algorithm, which is within the range

Cluster 1

Cluster 2

PointCentroid 1 (K-means)Centroid 2 (Weiszfeld’s algorithm)

Fig. 3. Example of K-means clustering.

4 J. OPT. COMMUN. NETW./VOL. 8, NO. 11/NOVEMBER 2016 Chen et al.

from one to M. Step 3 randomly generates i initial clustercentroids from all BS locations and then partitions all BSsinto i clusters, after which i K-means cluster centroids areupdated in Step 4. If the current number of considered clus-ters i is smaller than M, then, in Step 5, for each cluster j,we find the maximum distance between BSsDmax

ij , 1 ≤ j ≤ i,and calculate the average maximum distance asDmax

av;i � 1i

Pij�1 D

maxij . Furthermore, we increase the total

number of considered clusters by one and repeat Steps3, 4, and 5 until i > M. In Step 6, according to the averagemaximum distance information found in Step 5, we decide

the final number of clusters as K � argmaxi

jDmaxav;i−1−D

maxav;i j

jDmaxav;i�1−D

maxav;i j

,

which implies that the average maximum distance is sta-bilized after all the BSs are grouped intoK clusters. In Step7, we partition all the BSs into K clusters and find thecorresponding centroid for each.

B. RN Position Locating

For efficient mobile backhauling, our objective is to min-imize the sum fiber distance between each BS and OLT inthe PON system. However, the centroids found by theK-means clustering approach may not be optimal becauseeach of them only guarantees a sum minimum of squaresof the distances instead of the direct distances betweeneach BS and its associated centroid. Therefore, after clus-tering, we need to further apply Weiszfeld’s algorithm tofind a new centroid for each RN that ensures a minimumsum distance. This new location is called Weiszfeld’s cent-roid, as shown by a triangle in Fig. 3. It is evidentthat Weiszfeld’s centroid is different from the K-means

clustering centroid. For each given cluster found by the pre-vious K-means clustering approach, the steps of finding itscorresponding Weiszfeld’s centroid are given in [43].

Based on the above two steps, we can obtain a treedesign that connects all the BSs to a CO. For example,Fig. 5(a) shows a part of a TWDM-PON-based design fora 5G backhauling scenario with 200 BSs and one CO con-nected through the RNs. Here, the BSs are clustered intoeight groups, and each BS is connected to its associated RNby a dotted line while feeder fibers connect all RNs backto the CO as shown by solid lines. At each RN position,AWGs (denoted as round squares) and/or optical splitters(denoted as stars) are deployed. Also, because each OLT isassumed to support a maximum of 16 ONUs, if a BS clustercontains more than 16 BSs, then more than one opticalsplitter is required at that location. Based on the above treeconfiguration, we can calculate all the component costs,including the labor cost of trenching and laying fibers, fibercable cost, and equipment cost, and, consequently, the totalbackhauling cost.

C. Multistage RNs

Until now, we have only considered a single stage of RNin the backhauling design. However, it is possible to furtherreduce the backhauling cost by using multistage RNswhere FFs can share a common fiber route so that trench-ing and fiber laying costs can be significantly reduced evenfurther. Figure 6 illustrates an example how multistageRNs can reduce the backhauling cost. For the single-stagescenario, we partition all five BSs into two clusters, and twoRNs are placed to connect all the BSs back to the OLT, inwhich each RN connects to the OLT through an indepen-dent FF. In contrast, considering a multistage design (e.g.,a two-stage scenario in this example), we can enable thetwo RNs to share their fiber conduits by bringing in onemore RN stage when they connect back to the OLT throughFFs, as shown in Fig. 6. Comparing the two design scenar-ios, it is evident that the multistage scheme can signifi-cantly reduce the total length of trenching and layingfeeder fibers.

Typically in the multistage design, we place opticalsplitters at the RN locations that directly connect ONUsand AWGs at intermediate-stage RNs. The flow chart inFig. 7 shows the detailed steps on the multistage RN place-ment. Specifically, given K RN locations obtained after theprevious two steps, we further partition these RN positionsinto a certain number of groups using the K-means cluster-ing algorithm (here the exact number is decided by the clus-tering algorithm) and then apply Weiszfeld’s algorithm todetermine the optimal locations for the second-stage RNs.For the case of design with more than two stages, we canrepeat the same process for the second-stage RNs to findthe optimal locations for the third-stageRNs.We iterativelycontinue this process until the total deployment costbecomes higher when one more stage of RNs is inserted.

To demonstrate the benefit of cost reduction after em-ploying the multistage RN strategy, Fig. 5(b) illustrates

N

1. Input the location information of CO and BSs, and system

parameter and

i++

i MY

2. Set the square root of total number of BSs as the maximal

iteration times

3. Randomly select i initial cluster centroids from all dispersed BSs

7. Partition all BSs into K clusters and generate K K-means cluster

centroids

4. Partition all BSs into i clusters and update all cluster centroids

Start

End

i=05. Find out the maximal distance between BSs in each cluster and get the average maximal distance

6. Compare these average maximal distances and decidethe best number of clusters K

Fig. 4. Flow chart of BS clustering.

Chen et al. VOL. 8, NO. 11/NOVEMBER 2016/J. OPT. COMMUN. NETW. 5

an example of multistage design in contrast with thesingle-stage design obtained earlier [see Fig. 5(a)]. In thisdesign, three second-stage RNs (i.e., AWGs) are placedto connect all the first-stage RNs, and one third-stageRN is used to connect all the second-stage RNs. It is evi-dent that, compared with the single-stage scenario, the

Fig. 5. Backhauling examples for a scenario with 200 BSs.

OLT RN

BS

RN

RN

A

B

C

Stage 2 Stage 1

D

Fig. 6. Comparison of single-stage and multistage RNs.

Start

1. Input the single-stage RN design information and its total network deployment cost C1

j=2

2. Based on the (j-1)th stage RN design, employ the

previous BS clustering and RN locating algorithms to decide

the j th stage RNs

3. Find the cost Cj of the design with the j th stage RNs

Cj - Cj-1> 0

4. Return the optimal design with (j-1) stages and calculate the final deployment cost Cj-1

j++

End

N

Y

Fig. 7. Flow chart of multistage RN placement.

6 J. OPT. COMMUN. NETW./VOL. 8, NO. 11/NOVEMBER 2016 Chen et al.

multistage design can significantly reduce the total lengthof feeder fibers.

D. Cable Conduit Sharing of DF

Each ONU is connected to its associated RN (first-stageRN) directly using an independent distributed fiber (DF).Because the labor cost for trenching and laying fibers ismore expensive than other component costs, it would behelpful (if possible) to have these DFs share common con-duits that are built for other fiber links. We refer to this ascable conduit sharing of DF. Figure 8 illustrates such asharing example. Consider TWDM-PON2. Instead of build-ing a direct conduit for cabling between B and its associ-ated RN (i.e., optical splitter S1), only a shorter conduitBC is required and the fiber cable connecting B and S1can share conduit between S1 and C. Through this configu-ration, though the length of fiber cable between B and S1 issomewhat increased, the length of newly constructed fiberconduit can be significantly reduced; therefore, the total de-ployment cost can be reduced because the cost of trenchingand laying fibers tends to be the dominant cost.

The cable conduit sharing problem can be defined as fol-lows: given a set of BS locations and the location of a RN,we need to find a tree topology that connect to all theselocations and has a minimum sum link distance. This isa minimum spanning tree problem. We can apply thewell-known Prim’s algorithm [44] to solve it.

We have considered cable conduit sharing of DFs. Suchsharing can also be implemented for FFs. The previousmultistage RN strategy itself has implied conduit sharingwhen finding the locations for intermediate-stage RNs.Thus, for FFs, we do not need to explicitly implementthis step.

V. SIMULATION CONDITIONS AND TEST CASES

We evaluated the performance of the proposed backhaul-ing approaches through simulations. Different test cases

were considered, as shown in Table I, in which the secondcolumn is the total number of BSs, and the third column isthe maximum number of BSs that can be connected to acommon OLT. The locations of the CO and BSs/ONUsare given, which are the data from a real industrial project.In this study, we assume that, at most, 16 ONUs can beattached to a common OLT. For all three test cases, we as-sume that we have only one CO. To guarantee the averagebandwidth per user, we also set a limit on the number ofONUs that can be supported by each wavelength. Themaximum number of ONUs per OLT is different fromthe maximum number of ONUs per wavelength, but theyhave their respective constraints to limit the number ofONUs that can be connected to a commonOLT.We considerthree scenarios of ONUs per wavelength, i.e., 2, 4, and 8,respectively, which corresponds to the numbers of wave-lengths used per OLT to be 8, 4, and 2, given that the totalnumber ONUs supported by each OLT is 16. Thus, theaverage bandwidth per user varies provided that themaximum transmission capacity of each PON system isfixed. We also assume that there are three types of opticalsplitters with available split ratios of 1:4, 1:8, and 1:16.Note that if there is only one BS in a cluster, then no opticalsplitter would be needed.

For performance comparison, we also implemented therandom-cut sectoring approach of [15] as a benchmark.Figure 9 uses an example to illustrate the approach.Assume that the maximum optical split ratio is 1:4.Starting from the vertical y axis (or any other axis arbitrar-ily chosen), we rotate the radius line clockwise until thereare four BSs covered in the traversed (radial) sector. TheseBSs make up a cluster, and we can use an optical splitter toconnect all the BSs to the CO. We repeat the same processuntil all the BSs are grouped. Note that the last sector mayhave fewer than four BSs. The performance of the random-cut sectoring approach is closely dependent on the initial

Fig. 8. Example of cable conduit sharing among DFs.

TABLE ITEST NETWORKS (#S: NUMBER OF ONUS)

Net #ONUs Max. #S per OLT

Case 1 146 16Case 2 193 16Case 3 200 16

Central Office (CO

CO

)

ONU

SR: 1:4

Fig. 9. Example of the random-cut sectoring approach.

Chen et al. VOL. 8, NO. 11/NOVEMBER 2016/J. OPT. COMMUN. NETW. 7

cut. Thus, to achieve good performance, we start from 10different random initial cuts and then implement theclustering approach as previously described. We comparethe results of all these cuts to choose the one with the bestperformance.

Because the actual component costs are proprietary andunavailable, our cost calculations were based on the follow-ing assumptions: a) labor cost for trenching and layingfibers is $16,000/km; b) fiber cable cost is $4000/km;c) the OLT cost for each TDM-PON is $2500, and the costof each TWDM-PON OLT is calculated as

�����W

p� α, where α

is the OLT cost of each TDM-PON, and W is the number ofsupported wavelengths per OLT; d) the cost of each opticalsplitter port is $100; and e) the cost of each AWG port is$150. Note that, in the equipment component costs, theportions of maintenance costs are considered. If actualcosts are available, then these assumptions can be easilymodified to use the values given. We use Java to implementboth the random-cut sectoring and theK-means clustering-based algorithms. Both of the algorithms are highly time-efficient and obtain their solutions within seconds for allthree test cases.

VI. RESULTS AND DISCUSSION

For performance comparison, two PON technologies,including TDM-PON and TWDM-PON, are considered.Different legends are used to denote different optimizationscenarios, which are as follows: 1) random-cut sectoringapproach (denoted as “RandomCut” in legend), 2) TDM-PON-based K-means clustering approach [16] (denotedas “K-means_T” in legend), 3) TWDM-PON-basedK-meansclustering approaches with single-stage and multistageRNs (denoted as “K-means_TW” and “K-means_TW + M”

in legend, respectively), 4) TDM-PON-based K-meansapproach with cable conduit sharing (denoted as“K-means_T + SC” in legend), 5) TWDM-PON-basedK-means clustering approaches with cable conduit sharing(denoted as “K-means_TW + SC” in legend), and 6) TWDM-PON-based K-means clustering approach jointly withmultistage RNs and cable conduit sharing (denoted as“K-means_TW + M + SC”).

A. Performance Comparison of DifferentApproaches

Figure 10 shows the total backhauling costs of the twodifferent methods (i.e., random-cut sectoring approach andK-means clustering based approach) considering two differ-ent types of PON technologies, i.e., TDM-PON and TWDM-PON. The x axis represents the number of BSs in each testscenario, and the y axis is the total backhauling cost in unitsof million dollars. For the random-cut sectoring approachbased on the TDM-PON architecture, we show all theresults for the 10 different initial cuts, from which themaximum, minimum, and average costs are calculated.As expected, in all the test cases, the proposed K-meansclustering approaches perform much better, reducing the

total backhauling deployment cost by more than 50% com-pared with the random-cut sectoring approach.

Figure 11 compares the performance of the variousK-means-based approaches. TWDM-PON can achieve bet-ter performance than TDM-PON because of its greater flex-ibility in using different wavelengths. Also, we see that thestrategy of multistage RNs can further lower the totalbackhauling cost compared with the case of single-stageRNs due to the distance reduction of FFs after using multi-stage RNs. Moreover, comparing the strategies of multi-stage RNs and cable conduit sharing of DFs, it seemsthat the latter can play a more important role in reducingthe total cost because the resulting points corresponding tothe effort of DF cable conduit sharing are below those withmultistage RNs. Of course, among all the schemes, the caseof K-means_TW + M + SC can achieve the lowest costbecause it uses both multistage RN and conduit sharing.

In addition, we evaluate the impact of the number ofONUs supported by each OLTon the total deployment cost.Based on the proposed K-means clustering approach,Fig. 12 shows the total deployment costs under differentnumbers of ONUs supported by each OLT. It is clear thatthe total deployment cost decreases with the increase of thenumber of ONUs supported by each OLT. This is reason-able because a larger number of ONUs supported by

146 193 2000

20

40

60

Tot

al c

ost (

Mill

ion

$)

Scenario (Number of BSs)

10

30

50

70

RandomCutRandomCut

55.0%

K-means_T+SC

60.9%

63.4%

MaxRandomCutAverage

Min

K-means_T

K-means_TW+M+SC

K-means_TW+SC

K-means_TWK-means_TW+M

Fig. 10. Total deployment costs of different approaches.

146 193 2005

10

15

20

25

Tot

al c

ost (

Mill

ion

$)

Scenario (Number of BSs)

K-means_TK-means_TWK-means_TW+MK-means_T+SCK-means_TW+SCK-means_TW+M+SC

TDM-PON

TWDM-PONCable conduit sharing of DFs

Multi-stageRNs

Fig. 11. Total deployment costs of K-means clustering-basedapproaches.

8 J. OPT. COMMUN. NETW./VOL. 8, NO. 11/NOVEMBER 2016 Chen et al.

OLTallows more ONUs to share FFs, therefore leading to alower deployment cost. Of course, this is at the cost of alower average bandwidth per ONU user. Thus, for theoptimization design, we need to balance the required userbandwidth and the total deployment cost.

B. Analyses of Component Costs

With the K-means clustering-based approach, the per-centage distribution of backhauling component costs isshown in Fig. 13. We can see that the labor cost for trench-ing and laying fibers is dominant, representing more than50% of the total cost; for some scenarios, the percentage canbe even higher, up to 80%. The fiber cable cost is the secondhighest cost component, covering about 40% of the totaldeployment cost. The cost of all the equipment, includingOLTs, AWGs, and optical splitters, is comparatively negli-gible at less than 1% of the total cost.

For the dominant labor cost, we show the distribution oftrenching and laying fiber costs in Fig. 14, in which the firstgroup is the distribution of the total costs for trenching andlaying all the fibers, the second group is the distribution forFFs, and the third group is the distribution for DFs. We cansee that cable conduit sharing plays a vital role in reducingthe cost of trenching and laying DFs, while the multistageRN strategy is also efficient for lowering the cost of trench-ing and laying FFs.

C. Impact of Required Bandwidth per BS

Wealso evaluate how the number of supportedONUs perwavelength can affect the total deployment cost. Figure 15shows the results for two test cases with 193 BSs and200 BSs, respectively, in which all the schemes employthe TWDM-PON technique. We can see that, with an in-creasing number of supported ONUs per wavelength, thetotal deployment cost decreases for the approaches ofK-means_TW +M and K-means_TW +M + SC. This obser-vation is reasonable, as each wavelength can support moreONUs, which corresponds to lower hardware requirements

Equipment Cables Labor

146 193 200

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Scenario (Number of BSs)

(1) (2) (4) (5) (6)(3) (1) (2) (4) (5) (6)(3) (1) (2) (4) (5) (6)(3)

(1) K-means_T

K-means_TWK-means_T+SC(2)

(3) K-means_TW+M+SC(6)K-means_TW+MK-means_TW+SC(4)

(5)

Equipment

0.9%

Fig. 13. Percentage distribution of component costs.

0

4

8

12

16

20

Labor Labor_FF Labor_DF Labor Labor_FF Labor_DF Labor Labor_FF Labor_DF

146 193 200Scenario (Number of BSs)

K-means_TK-means_TWK-means_TW+M

K-means_TW+SCK-means_T+SC

K-means_TW+M+SC

Fig. 14. Labor costs for trenching and laying fibers.

(a) 193 ONUs

(b) 200 ONUs

10

14

18

22

26

2 4 6 8

K-means_TW+M+SC

K-means_TW+MK-means_TW+SC

K-means_TW

Number of ONUs per wavelength

Tot

al c

ost (

Mill

ion

$)T

otal

cos

t (M

illio

n $)

193 ONUs

5

7

9

11

13

15

17

2 4 6 8Number of ONUs per wavelength

K-means_TW+M+SC

K-means_TW+MK-means_TW+SC

K-means_TW 200 ONUs

Fig. 15. Total cost versus number of ONUs per wavelength.

split ratio: 8

split ratio: 16split ratio: 32

0

8

16

24

32

TWDM TDM TWDM TDM TWDM TDM

146 193 200Scenario (Number of ONUs)

Tot

al c

ost (

Mill

ion

$)

Fig. 12. Total deployment cost versus number of ONUs per OLT.

Chen et al. VOL. 8, NO. 11/NOVEMBER 2016/J. OPT. COMMUN. NETW. 9

and more flexibility in connecting these ONUs. It is alsointeresting that, for the remaining fourapproaches, it seemsthat there is no significant cost variance with the change ofthe number ofONUsperwavelength. Thismeans that, fromthe cost perspective, these approaches are insensitiveto the number of ONUs per wavelength. As these show,the K-means_TW + M + SC scheme can achieve the lowesttotal deployment cost. This again confirms the efficiency ofthe proposed K-means clustering-based approach, and thebenefits of using multistage RNs and using conduit sharingstrategies.

VII. CONCLUSION

Deploying a large number of 5G small cells close to endusers has been identified as a promising solution for han-dling the high bandwidth requirements in upcoming 5Gmobile networks. However, deploying many small cells ischallenging because this significantly increases the back-hauling cost. In this paper, based on the fiber-based mobilebackhaul network architecture, we propose to employTWDM-PON to interconnect the CO andBSs. TheK-meansclustering-based approach is applied to plan the topology ofthemobile backhaul network.Due to the dominance of laborcost for trenching and laying fibers, the strategies of multi-stage RNs and cable conduit sharing are further incorpo-rated to reduce the total length of trenching and layingfibers. Simulation studies show that compared with thebenchmark random-cut sectoring approach, the proposedK-means clustering-based approach is effective in signifi-cantly reducing the total backhauling cost by over 50%.Moreover, the multistage RN and cable conduit sharingstrategies are effective to further lower the labor cost fortrenching and laying fibers.

ACKNOWLEDGMENT

This work was supported by the National Natural ScienceFoundation of China (NSFC) (61322109, 61671313), theNatural Science Foundation of Jiangsu Province(BK20130003), and the Science and Technology SupportPlan of Jiangsu Province (BE2014855). Part of this workwas presented at ACP 2015 [16].

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