dimensioning of the lte access network

Telecommun Syst (2013) 52:2637–2654DOI 10.1007/s11235-011-9593-2

Dimensioning of the LTE access network

Xi Li · Umar Toseef · Dominik Dulas · Wojciech Bigos ·Carmelita Görg · Andreas Timm-Giel · Andreas Klug

Published online: 3 September 2011© Springer Science+Business Media, LLC 2011

Abstract This paper proposes efficient analytical models todimension the necessary transport bandwidths for the LongTerm Evolution (LTE) access network satisfying the QoStargets required by different services. In this paper, we con-sider two major traffic types: elastic traffic and real time traf-fic. For each type of traffic, individual dimensioning modelsare proposed for both the S1 interface and the X2 interface.For elastic traffic the dimensioning models are based on theProcessor Sharing models; while for real time traffic the di-mensioning models are based on the fundamental queuing

X. Li (�) · U. Toseef · C. GörgTZI-ikom, Communication Networks, University of Bremen,Otto-Hahn Allee NW1, 28359 Bremen, Germanye-mail: [email protected]

U. Toseefe-mail: [email protected]

C. Görge-mail: [email protected]

D. Dulas · W. BigosNokia Siemens Networks Sp. z o.o., Strzegomska 56A,53-611 Wroclaw, Poland

D. Dulase-mail: [email protected]

W. Bigose-mail: [email protected]

A. Timm-GielInstitute of Communication Networks,Hamburg University of Technology, Schwarzenbergstr. 95E,21073 Hamburg, Germanye-mail: [email protected]

A. KlugNokia Siemens Networks GmbH & Co. KG,Lise-Meitner-Str. 7/2, 89081 Ulm, Germanye-mail: [email protected]

models. For validating these analytical dimensioning mod-els, a developed LTE system simulation model is used. Ex-tensive simulations are performed for various traffic and net-work scenarios. The analytical results derived from the pro-posed dimensioning models are compared with the simula-tion results. The presented results demonstrate that the pro-posed analytical models can appropriately estimate the re-quired performances for different service classes and priori-ties. Hence they are suitable to be used for dimensioning ofthe LTE access network with different traffic and networkconditions.

Keywords LTE access network · S1 Interface · X2Interface · Handover · Dimensioning · QoS

1 Introduction

The roadmap of Next Generation Mobile Network (NGMN)is to provide mobile broadband services. Services like Mo-bile TV, multimedia online gaming, Web 2.0, and high-speed Internet will produce tremendous traffic in the futuremobile networks. To make this happen, 3GPP introduces anew radio access technology, known as Long Term Evolu-tion (LTE) to ensure the competitiveness of the 3GPP tech-nology family for the long term. LTE supports extensivelyhigh throughput and low latency, improved system capacityand coverage performance.

LTE introduces a new air interface and radio access calledas Evolved UMTS Terrestrial Radio Access Network (E-UTRAN), which is specified in the new 3GPP Releases8 and 9. To support the LTE radio interfaces and the E-UTRAN, 3GPP also specifies a new Packet Core, the En-hanced Packet Core (EPC) network architecture. This paperis only focused on dimensioning the transport network of

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2638 X. Li et al.

Fig. 1 LTE E-UTRAN

the E-UTRAN (i.e. the LTE access network), which is basedon IP. E-UTRAN is designed to support high data rates, lowlatency, and hence to bring improved user experience withfull mobility. This is achieved by introducing a new, fully IP-based flat architecture with the enhanced Node B (eNode B)directly connected to the access gateway (aGW). The logicalarchitecture of the LTE access network is shown in Fig. 1.The eNode B (denoted as eNB in this paper) is responsi-ble for Radio Resource Management (RRM) decisions, han-dover (HO) decisions, scheduling of users as well as radioand transport bearers, etc. The aGW provides terminationof the LTE bearer and acts as a mobility anchor point forthe user plane. The eNB is connected to the aGW with theS1 interface. Between the eNBs the X2 interface is defined,which is used to connect the eNBs with each other in thenetwork. The X2 interface is needed for the case of HO toforward the traffic from a source eNB to its target eNB.

Due to a significantly improved air interface providingmuch higher throughput and radio interface capacity, LTEwill result in a much higher demand on the transport capac-ities in the access network than 3G UMTS networks. Thus,how to properly dimension the transport resources for a cost-efficient LTE access network (on the S1 and the X2 inter-faces), considering the fast growing traffic and new servicesprovided by the LTE, becomes a critical network planningproblem.

This paper is aimed to propose efficient analytical mod-els to dimension the bandwidths of the transport links at theeNB side (called eNB transport link in this paper) requiredfor the S1 and the X2 interfaces. The objective of the di-mensioning is to minimize the transport network costs (forleasing IP bandwidth) while being able to fulfill the QoS re-quirements of various services. In this paper, we considertwo fundamental types of traffic: elastic traffic and real timetraffic. Elastic traffic is generated by non real time (NRT) ap-plications and is typically carried by the TCP protocol. Typ-ical applications are Internet services like web browsing andFTP. Real time (RT) traffic is associated with real time appli-cations, which are delay-sensitive and have strict packet de-

lay requirements over the transport networks. Typical appli-cations in this traffic class are VoIP, streaming or video con-ferencing. In this work, for dimensioning the defined QoSrequirement for elastic traffic is the end-to-end applicationthroughput or transfer delay (which specifies the amount ofdata that can be transferred in a certain time period); whilefor real time traffic the considered QoS is the transport net-work delay, i.e. the end-to-end packet delay through the ac-cess network: S1 delay on the S1 interface and X2 delay onthe X2 interface.

In this paper, we propose two individual kinds of ana-lytical models for each traffic type for dimensioning of theLTE S1 and X2 interfaces to meet their individual QoS re-quirements. The proposed analytical dimensioning modelfor elastic traffic is based on the M/G/R-Processor Shar-ing (M/G/R-PS) model, which characterizes TCP traffic atflow level and is often used to calculate the mean transac-tion time or throughput for TCP flows. For real time traf-fic, we propose basic queuing model on the packet level toestimate the transport network delay performance. On de-veloping these analytical methods, special efforts are madeto properly model the main features and functionalities ofthe LTE radio interface, the IP-based LTE access transportnetwork using the Differentiated Service (DiffServ) QoSscheme, and model the impact of HO (for dimensioning ofthe X2 interface). Furthermore, we present how to apply theproposed analytical models for carrying out the bandwidthdimensioning for the S1 and X2 interfaces. For validatingthe applicability of the proposed analytical models, in thiswork a LTE simulation model is developed to verify the an-alytical results with the simulation results. This LTE simu-lator models in detail the important LTE network entities,protocol layers, required radio and transport scheduling andQoS functions, etc.

The remainder of the paper is organized as follows:Sect. 2 gives an overview of the developed LTE simula-tor. Section 3 describes the main dimensioning tasks andpresents a general dimensioning framework. Section 4 in-troduces the proposed analytical models for dimensioningthe S1 interface for elastic traffic and real time traffic, andSect. 5 presents their extensions for dimensioning the X2interface. Section 6 shows the detailed validation results anddimensioning results. At the end, conclusions and futurework are given.

2 LTE simulation model

The LTE simulator is implemented using OPNET simu-lation software. The developed LTE simulation model isshown in Fig. 2. It includes all basic E-UTRAN and EPCnetwork entities. The main focus of this simulation model ison the LTE access network.

Dimensioning of the LTE access network 2639

The LTE access transport network consists of eNBs, anda number of routers which connect the eNBs with the aGWand with each other. Figure 2 shows an example scenariowith two eNBs that connect with each other via an in-termediate router. The EPC user-plane and control planenetwork entities are represented by the aGW network en-tity. The aGW includes the functionalities of the eGSN-C(evolved SGSN-C) and eGSN-U (evolved SGSN-U). The re-mote node represents an Internet server or any other nodesthat provide the corresponding data services. To model thepossible delays between the aGW and the remote node,the simulation model allows configuration of certain delaydistributions for the data transmission between them (e.g.20 ms between an Internet server and the aGW). We assumethat the delays between the core network entities are negli-gible.

Figure 3 shows the LTE user-plane protocol structurewhich is developed within this LTE simulator. The proto-

Fig. 2 LTE simulation model

cols are categorized into three groups: radio (Uu), transport,and end-user protocols.

The radio (Uu) protocols include the peer to peer pro-tocols, such as PDCP (Packet Data Convergence Protocol),RLC (Radio Link Control), MAC (Medium Access Con-trol) and PHY (Physical), between the UE entity and theeNB entity. The PDCP, RLC and MAC (including air inter-face scheduler) layers are modeled in detail according to the3GPP specifications in this simulator. But the PHY (phys-ical) layer is not detailed modeled. However the effect ofthe radio channels and PHY characteristics are modeled atthe MAC layer in terms of the data rates of individual userperformance. For the UE mobility, general mobility modelssuch as random directional and random way points are used.

The LTE transport network is based on IP technology.The user-plane transport protocols as shown in Fig. 3 areused at both S1 interface and X2 interfaces. It mainly in-cludes the GTP (GPRS Tunneling Protocol), UDP, IP andlayer 2 protocols. Ethernet is used as the layer 2 pro-tocol for the current implementation. IP protocol is theone of the key protocols which handles routing, security(IPsec), services differentiation and scheduling functional-ities. The LTE transport network applies the DiffServ-basedQoS framework and it is established by connecting a numberof IP DiffServ routers between the eNBs and the aGW. Diff-Serv is developed by the IETF [1], which defines the threemost common Per Hop Behavior (PHB) groups correspond-ing to different service levels: Expedited Forwarding (EF),Best Effort (BE), and Assured Forwarding (AF). In the LTEtransport network, each PHB is assigned to a transport prior-ity and has its own buffer in the transport scheduler. To servedifferent PHBs, Weighted Fair Queuing (WFQ) schedulingis used. The definition of WFQ discipline is given in [2]. Letwk be the weight of the kth PHB queue and BW the totalavailable IP bandwidth. If there are in total N PHB queuesand all queues are transmitting data, then the kth queue ob-

Fig. 3 LTE protocol structure(user-plane)

2640 X. Li et al.

Fig. 4 LTE access networkdimensioning framework

tains a fraction of the total capacity BWk as calculated in (1).It shall be noticed that if one priority queue is empty (i.e. notutilizing its allocated bandwidth) then its bandwidth shall befairly shared by the other queues according to their weights.

BWk = wk∑N

i=1 wi

BW (1)

For modeling the end-user protocols, the standard OP-NET protocols such as application and TCP/UDP are used.They are located at the remote Internet server and each UEentity. Furthermore, the control-plane is not directly mod-eled within the LTE simulation model. However the effect ofsignaling such as their overhead and delays are consideredat the respective user-plane protocols upon specific require-ments.

3 LTE access network dimensioning framework

In the framework of this paper, the main task of dimension-ing is to decide minimum required bandwidths for the trans-port links of the S1 and X2 interface in the LTE access net-work, for given traffic load of various services and definedQoS targets. For carrying out the dimensioning, we proposea general framework for the LTE access network dimension-ing as shown in Fig. 4.

As illustrated in Fig. 4, this framework includes threetypes of input parameters:

(1) User parameters define (i) the traffic demand whichspecifies the total amount of the offered traffic, the traf-fic classes with respective to different services, and traf-fic mix; (ii) user or service priority classes and their dis-tributions; (iii) mobility and handover parameters.

(2) QoS targets that need to be satisfied with the dimen-sioning.

(3) System parameters such as radio configurations (e.g.cell capacity), transport network functions (e.g. Diff-Serv QoS scheme, WFQ scheduling), resource controlfunctions (e.g. admission control), etc. The configuredsystem parameters will have considerable impact on theperformance of the networks and on the end users, andthus on the dimensioning results.

The dimensioning process is the core of this dimension-ing framework. In this paper, we design the dimensioningprocess for elastic traffic and real time traffic individually(will be explained in Sects. 4 and 5). The output of dimen-sioning is the required transport bandwidths for the S1 andX2 interfaces, which should support the offered traffic de-mand of all traffic types and meet the defined QoS targets.In this paper we derived the bandwidth in kbps, however inpractice the dimensioned link capacity will be mapped toavailable physical line(s).

4 Dimensioning model for the S1 interface

In this section, we present two analytical dimensioning mod-els for the dimensioning of the S1 interface: one for elastictraffic and one for real time traffic [8]. For elastic traffic, theconsidered QoS is the end-to-end application delay and theproposed dimensioning models are based on Processor shar-ing models. For real time traffic the QoS target is the trans-port network delay (i.e. S1 delay and X2 delay), accordinglywe propose essential queuing models for the dimensioning.

4.1 S1 dimensioning model for elastic traffic

The elastic traffic is typically carried by the TCP protocol.Due to TCP flow control, the rate of TCP flow adjusts itselfto adapt to the available bandwidth in the network. If TCPworks ideally (i.e. instantaneous feedback), all elastic traffic


flows going over the same link will share the bandwidth re-sources equally and thus the system is essentially behavingas a Processor Sharing (PS) system [3]. This important prop-erty enables the applicability of M/G/R-PS model for esti-mating the end-to-end application performance of the elas-tic traffic. The M/G/R-PS model characterizes the TCP traf-fic at flow level. It is assumed that there are a large numberof independent TCP flows, each respect to downloading anInternet object of an arbitrary file size distribution. The ex-pected sojourn time of the M/G/R-PS model represents theaverage object transfer delay.

The M/G/R-PS model has become a popular approach fordimensioning of different fixed (e.g. ADSL) and mobile net-works (e.g. UMTS). An introduction of the basic M/G/R-PSmodel can be found in [3]. In [4], an extension of the basicM/G/R-PS model was proposed which considers the impactof TCP slow-start. In [5–7] the author applied the M/G/R-PSmodel to dimension the Iub transport links in the UMTS net-work for elastic traffic. In this paper the M/G/R-PS model isfurther extended for dimensioning the IP-based LTE S1/X2interfaces, by properly modeling the key features of the LTEradio interface, the IP based transport network using Diff-Serv QoS scheme and WFQ scheduling, and HO aspects (forthe X2 interface).

By analyzing the LTE system model, it can be concludedthat the end-to-end application performance is essentially in-fluenced by both air interface and S1 interface. They are thetwo major bottlenecks through the end-to-end path. The airinterface determines the radio resource each UE can get. Thehigher the air interface utilization, the lower will be the aver-age UE throughput as a result of the congestion over the airinterface. The S1 interface is the second capacity bottleneck.A congested S1 link can result in significant increase of theend-to-end transfer delay. Thus, in order to estimate the end-to-end performance, we need to model these two bottlenecksindividually in the analytical dimensioning model. There-fore we propose an end-to-end method for dimensioning forelastic traffic as shown in Fig. 5, which consists of the airinterface model and the S1 model.

Given the traffic models and the number of UEs in thecell, the air interface model calculates the maximum aver-age UE throughput but without considering any congestionin the S1 transport network. Then the S1 model will takethe maximum UE throughput obtained from the air interfacemodel as an input parameter and then estimate the impactcaused by the S1 link on the overall end-to-end performance.In the following, we will introduce the detailed modelling ofthe air interface and the S1 interface individually.

Fig. 5 Dimensioning method for elastic traffic

4.1.1 Modeling of the air interface

The air interface scheduler will have important impact onthe achievable UE throughput. In this work, we consider thecase of scheduling all UEs in a cell in a round robin man-ner, i.e. all UEs are equally served with the same priority.That means, all elastic flows can share the common radioresources equally and in this context the air interface can bemodeled as a Processor Sharing (PS) system. Furthermore,there is no maximum bearer rate limitation for each LTEbearer (i.e. for each individual flow). That means, one elasticflow can take the complete radio resource for itself if thereare no other UEs active in the cell. In this case, the M/G/1-PS model can be used to model the air interface, because theM/G/1-PS model is defined for the situations where the flowrate is not limited, i.e. each flow has the ability to fully uti-lize the whole capacity when no other flow is present in thesystem [5].

Let CUu denote the cell capacity (in bps) and LoadUu bethe average traffic load in a cell (in bps). Here LoadUu canbe calculated from the given traffic models and total num-ber of active UEs in the cell. In this work, elastic traffic isgenerated by non real time (NRT) services which are basedon TCP/IP protocol. For a certain NRT service s (e.g. ftp),given ns number of UEs having this service with a meanflow arrival rate λs and mean object size ls (including air in-terface overhead, i.e. PDCP, RLC, MAC protocol overhead)in a cell, then the average traffic load of this NRT servicein the cell is load(NRT)s = λs · ls · ns . For a certain RT ser-vice m (e.g. VoIP), given nm number of UEs of this RT ser-vice with a codec rate δm(including air interface overhead),activity factor αm (defined as the ratio of duration of ONperiod to the total time of a call), call duration dm, and callarrival rate λm, then the average traffic of this RT servicein the cell is load(RT)m = δm · αm · dm · λm · nm. Thus bysumming up the traffic load of all elastic traffic flows of dif-ferent NRT services and the traffic load of all RT services inthe cell, the total traffic load of the cell is:

LoadUu ={∑

s

load(NRT)s +∑

m

load(RT)m

}

.

As a result, the average utilization of the air interface is cal-culated as pUu = (LoadUu/CUu).

Based on the sojourn time formula of the M/G/1-PSmodel, we can derive the delay factory fUu with (2). The de-lay factor is larger or equal to 1. It quantifies the increase ofthe transfer time (or decrease of the effective throughput) ofindividual flows as a result of the air interface congestion. Itis noted that when the air interface utilization pUu is higherthe delay factor fUu also becomes higher, which implies thatthe application delay (or file transfer time) will be increased.It is noted that here the delay factor fUu for elastic trafficalso considers the traffic load of real time services. As real

2642 X. Li et al.

time traffic contributes to the total traffic load and also sharesthe available radio resources with the elastic traffic, and thusit as well has impact on the total congestion and influencesthe application performance of the elastic traffic. Therefore,we also need to take the real time traffic into considerationwhen estimating the end-to-end performance of the elastictraffic in our model.

fUu = 1/(1 − pUu) (2)

With fUu we can derive the average UE throughput r asa result of air interface utilization in (3). It is noted that r

is only limited by the air interface capacity, assuming thatthere is no congestion through the transport network (givensufficient capacity). If the air interface capacity is fixed, r

represents the maximum average UE throughput given anideal transport network. Thus, in the next step we take r asthe peak UE data rate for dimensioning the S1.

r = CUu/fUu (3)

4.1.2 Modeling of the S1 interface

As shown in Sect. 2, the LTE S1 transport network is basedon IP using DiffServ QoS framework together with the WFQscheduling. The main idea of the proposed analytical mod-els for dimensioning the S1 link is to apply the M/G/R-PS model per PHB class (i.e. per transport priority), whiletaking the potential multiplexing gain of bandwidth sharingamong different PHB classes into account. The basic modelfor dimensioning an IP-based transport link in the UMTSnetworks (which also deploy IP DiffServ QoS structure) ispresented in [7]. This paper extends this basic model forelastic traffic to model the LTE S1/X2 interfaces and alsofurther to consider the TCP slow start behavior.

In this analytical model, let CS1 be the S1 bandwidth. Foreach PHB class, we define LS1(k) be the mean offered traf-fic of the PHB class k (including both RT and NRT traffic)and wk denotes its WFQ weight. The following gives thedetailed steps to calculate the average application delay ofelastic traffic flows transmitted over the PHB class k.

Step 1: given the CS1, estimate the available bandwidth thatcan be used for the PHB class k, denoted as CS1(k), us-ing (4). It shows that CS1(k) has a minimum bandwidththat equals to the allocated bandwidth assigned by theWFQ transport scheduler according to its weight wk (see(1)), and also consider any additional bandwidth if theother PHBs do not fully utilize their allocated bandwidthshare.

CS1(k) = max

⎧⎨

⎩

(

CS1 · wk∑

i wi

)

,

⎛

⎝CS1 −∑

j �=k

LS1(j)

⎞

⎠

⎫⎬

⎭

(4)

Step 2: With the CS1(k) the normalized traffic load of thePHB class k, denoted as ρk , can be derived with ρk =LS1(k)/CS1(k), for the given mean offered traffic over thePHB class k.

Step 3: For the PHB class k, apply the M/G/R-PS modelto estimate the application performance. Here R is deter-mined by Rk = �CS1(k)/r�. Here r , which is the maximumaverage UE data rate, is the result of the air interface modelcalculated from (3). It is noted that r is used for each PHBclass, since at the air interface there is no QoS prioritiza-tion, thus all PHB classes have the same maximum averageUE data rate.

Step 4: For the PHB class k, the expected sojourn time (oraverage transfer time) for transferring a file of length xk canbe derived from the basic M/G/R-PS model [3], as givenin (5).

EM/G/R−PS{T (xk)} = xk

r

(

1 + E2(Rk,Rkρk)

Rk(1 − ρk)

)

= xk

rfk (5)

Here E2 denotes Erlang’s second formula (Erlang C for-mula), which is given in (6) with Ak = Rkρk . It is knownthat the Erlang C formula calculates the delay probability(i.e. the probability that a job has to wait) of Erlang’s delaysystem. fk is the delay factor of the TCP flows of the PHBclass k over the S1 interface.

E2(Rk,Ak) =A

Rkk

Rk ! · Rk

Rk−Ak

∑Rk−1i=0

Aik

i! + ARkk

Rk ! · Rk

Rk−Ak

(6)

The basic M/G/R-PS model assumes ideal capacity sharingamong active flows. However, the TCP flows are not alwaysable to utilize their fair share of the available bandwidth.During the TCP slow start phase the available bandwidth cannot be completely utilized at the beginning of transmission,and thus the resulting transfer delay is longer than the the-oretically computed one from the basic M/G/R-PS model.For small file transactions and longer round trip times, theimpact of the TCP slow start is more significant. If we doneed to consider the impact of TCP slow start, then an ex-tended M/G/R-PS model proposed in [4, 5] can be appliedto calculate the average transfer delay.

Eext{T (xk)} =

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

�log2

(� xk

MSS�)� · rttk

+ EM/G/R−PS{T (xk − xstartk )}xk < xslow-startk

n∗ · rttk + EM/G/R−PS{T (xk − xslow-startk )}xk ≥ xslow-startk

(7)


In this extended M/G/R-PS model, the computation of theexpected transfer time includes two parts: the first part es-timates the total time needed for the slow start phase; thesecond part estimates the time of sending the rest of the datawith the available share capacity using the basic M/G/R-PSmodel. In (7) n* represents the required number of round triptime (RTTs) before utilizing the available share capacity andxslow-start denotes the amount of data sent within n∗ RTTs.If the file size is smaller than xslow-start , then the amountof sent data (which are only in the slow start phase) is de-noted as xstart . It can be seen that this calculation requiresthe information of TCP segment size (MSS) and RTT. In ourapproach, a minimum RTT rttmin is estimated by summingup all delays through the end-to-end path: all node process-ing delays, propagation delays (over the air interface, the S1transport links and the core network), and additional exter-nal network (e.g. Internet) delays. When the air interface orthe S1 links are congested, the caused extra delays will betaken into account to the estimated RTT. For the PHB classk the overall estimated RTT, denoted as rttk , is estimatedwith (8).

rttk = rttmin · fUu · fk (8)

4.1.3 S1 dimensioning process for elastic traffic

For a certain NRT service s (e.g. http or ftp) with a definedQoS target, the objective of the S1 dimensioning is to de-termine necessary S1 bandwidths which satisfy the definedapplication delay QoS target of each PHB class of this NRTservice. The dimensioning process for the elastic traffic isdescribed in the following.

Step 1: define an initial S1 link capacity C0;Step 2: use the air interface model (refer to Sect. 4.1.1) to

calculate the delay factor of the Uu interface fUu;Step 3: for the given S1 capacity, estimate the average

transfer delay for each PHB class of the NRT ser-vice s with the S1 model (refer to Sect. 4.1.2). Ifthe obtained transfer delay of one PHB class cannot meet its QoS target, then the S1 capacity needsto be increased. Thus for each PHB class k the re-quired S1 link bandwidth is derived numerically byperforming delay calculations for a range of band-widths until the resulting average transfer delayfrom a certain S1 bandwidth reaches the defined ap-plication delay QoS target of that PHB class. Thisstep will be done for each PHB class of this NRTservice. Let CS1(k)s denote the derived S1 band-width for the PHB class k of the NRT service s.

Step 4: we take the maximum bandwidth of all PHB classesto be the required S1 capacity for the NRT services: CS1(s) = max{CS1(k)s}, as it satisfies the QoSrequirements of every PHB class.

Step 5: If there are several NRT services, we shall we re-peat steps 1–4 to derive the bandwidth for eachNRT service and then take the maximum one to bethe required S1 capacity which meets the QoS tar-gets of all NRT services: S1_BW = max{CS1(s)}.It needs to be noticed that this calculated S1 band-width also includes the traffic load of RT servicesif there is any. If there is certain amount of RT traf-fic, in order to derive the bandwidth only for NRTservices, we shall subtract the RT traffic load fromS1_BW as shown in (9).

SI_BWNRT = SI_BW −∑

j

load(RT )j (9)

4.2 S1 dimensioning model for real time traffic

Different than the elastic traffic, the QoS target for the realtime (RT) traffic is the average transport network delay (inthis case the S1 delay), which is on the packet level insteadof on the flow level. To calculate the S1 delay for each realtime service, we propose the queuing model based on M/D/1model in this paper. Moreover, due to the strict delay re-quirements of the RT traffic, the RT traffic is usually givenhigher transport priority than the NRT traffic. Thus we candimension the S1 interface for the RT traffic separately.

The RT applications or services, such as VoIP and video,typically send packets at a fixed rate (according to the givencodec rate) with a fixed frame size and frame rate. More-over, when loss occurs they do not retransmit packets andhave any flow control mechanisms to adjust the data rate un-der congestion situations. Thus, when a number of RT traf-fic flows are transported over the S1 interface, the aggre-gated real time traffic can be modeled as a superposition offixed-rate packet streams. When the number of RT users islarge enough, it can be assumed that they create a large num-ber of independent packets at the S1 interface. Thus we canmodel the packet arrival process as Poisson process. Further-more, for certain RT service its packet size is usually fixed.So given certain S1 bandwidth, the service rate of this RTservice is also constant. Therefore, we can apply the M/D/1model to estimate the S1 delay for the RT services, assumingPoisson arrival process and a deterministic service rate. TheM/D/1 model can be directly used if there is only one trans-port priority class (DiffServ PHB class) used. In the caseof transporting the RT service with multiple PHB classes,we propose to apply the M/D/1 model per PHB class tocope with the applied IP DiffServ QoS scheme and the WFQscheduler at the S1 interface.

4.2.1 Estimation of the S1 delay for real time traffic

The detailed analytical modeling for the RT traffic is ex-plained as follows. For a certain RT service m (e.g. VoIP),

2644 X. Li et al.

we define LS1−RT (k)m as the mean traffic load of this RTservice over the PHB class k at the S1 interface. HereLS1−RT(k)m can be derived by calculating the correspond-ing application load including additional protocol overheads(as explained in Sect. 4.1.1). Let CS1−RT(m) be the totalS1 bandwidth needed for this RT service m. The followinggives the full steps to calculate the average S1 delay of thisRT service transmitted over the PHB class k, where the LTEtransport network uses the WFQ scheduler to serve differenttransport priorities.

Step 1: Firstly we estimate the available bandwidth that canbe used for the RT service m over the PHB class k, denotedas CS1−RT(k)m with (10). It shows that it contains a mini-mum bandwidth that equals to the allocated bandwidth as-signed by the WFQ scheduler according to its weight wk

(see (1)), and also consider any additional bandwidth ifother PHBs do not fully utilize their allocated bandwidthshare. It is noted that if RT traffic is mapped to an EFPHB with a strict transport priority over the elastic traf-fic, then let CS1−RT(k)m = CS1−RT(m) since k = 1 in thiscase.

CS1-RT(k)m

= max

{(

cS1-RT(m) · wk∑

i wi

)

,

(

cS1-RT(m) −∑

j �=k

LS1–RT(j)m

)}

(10)

Step 2: With CS1−RT(k)m and the given mean offered traf-fic LS1−RT(k)m, the normalized traffic load of the PHBclass k, denoted as ρRT(k), can be derived with ρRT(k) =LS1−RT(k)m/CS1−RT(k)m.

Step 3: For the PHB class k, we apply the M/D/1 modelto estimate the S1 delay of the RT service m. Firstlywe estimate the average queue length of the M/D/1model:

WS1−RT(k) = ρRT(k) + 0.5 · ρRT(k)2

1 − ρRT(k)(11)

Step 4: Then with Little’s law, the mean S1 delay of this RTservice on the PHB class k denoted as dS1−RT(k) can bederived with (12).

dS1−RT(k) = WS1−RT(k)/αm(k) (12)

Here αm(k) is the packet arrival rate of this RT service overthe PHB class k, which can be derived from its offered S1traffic load on the PHB class k and the packet length of thisRT service θm in (13).

αm(k) = LS1−RT(k)m/θm (13)

4.2.2 S1 dimensioning process for real time traffic

For RT traffic the objective of the dimensioning is to findthe necessary S1 bandwidth for a mean S1 delay target. Thedimensioning process for the RT traffic is explained in thefollowing steps.

Step 1: define an initial S1 capacity for RT service m;Step 2: for the RT service m, estimate its mean S1 delay

for each PHB class of this service with the above method(refer to (10)–(13). If the obtained S1 delay of one PHBclass can not meet the required S1 delay target, then the S1capacity needs to be increased. Thus for each PHB class k

the required S1 link bandwidth is derived numerically byperforming delay calculations for a range of bandwidthsuntil the resulting average S1 delay from a certain S1 band-width reaches the defined S1 delay target. This step will bedone for each PHB class of this RT service. At the end,the bandwidth required for the PHB class k is denoted asS1_BWRT(k)m.

Step 3: we take the maximum bandwidth of all PHBclasses to be the required S1 capacity for RT service m:S1_BWRT(m) = max{S1_BWRT(k)m}, as it will satisfythe S1 delay requirements of every PHB class.

Step 4: if there are several RT services, we repeat steps 1–3to derive the bandwidth for each RT service, and then sumup their dimensioned bandwidths to be the required S1 ca-pacity for carrying all RT traffic in the network.

S1_BWRT =∑

m

S1_BWRT(m) (14)

4.3 Dimensioning of the S1 interface

Usually the network transmits both elastic and real timetraffic, where the objective of the S1 dimensioning needsto fulfill both the end-to-end application delay or through-put of elastic traffic (of the NRT services) and the mean S1delay for real time traffic. In this case, the S1 dimension-ing should combine the dimensioning process of both traffictypes. From the proposed dimensioning procedure for elas-tic traffic (explained in Sect. 4.1.3), we can derive the re-quired S1 bandwidth for transporting all NRT services, i.e.S1_BWNRT . And for real time traffic, we apply the dimen-sioning steps described in Sect. 4.2.2 to derive the requiredS1 bandwidth S1_BWRT for transporting all RT services inthe network. Finally, the total required bandwidth for the S1interface is calculated as the sum of the bandwidth requiredfor individual traffic types in (15).

SI_BW = SI_BWNRT + SI_BWRT (15)

It is noted that this bandwidth is only for the S1 user plane.If there are additional IP bandwidths reserved for the controland signaling traffic, then these extra bandwidths also need


to be added to compute the total S1 bandwidth. It shall bealso noticed that the proposed dimensioning approach canbe used for dimensioning of both uplink and downlink cases.

5 Dimensioning model for the S1/X2 interface

Different than the S1 interface, the X2 interface transmitsonly HO traffic which is sent from the source eNB to thetarget eNB when an inter-eNB HO occurs. In this paper, weconsider the network structure shown in Fig. 2, where theX2 interface uses the same physical transport link as usedby the S1 interface to transmit the X2 traffic. That means,at the eNB transport link the X2 traffic is multiplexed withthe S1 traffic. In this case, the dimensioning of the X2 inter-face firstly requires dimensioning of the eNB transport linkto transmit both S1 and X2 traffic, i.e. S1/X2 dimension-ing.

5.1 S1/X2 dimensioning model for elastic traffic

The main challenges of dimensioning the X2 interface forelastic traffic are: (1) The amount of the HO traffic (calledX2 traffic in this paper): when the X2 traffic and the S1 traf-fic are carried over the same transport link, the X2 traffic isconsidered as additional link load, which may lead to morecongestion on the transport link. (2) The caused X2 delay(the delay for one IP packet sent from the source eNB toits target eNB) increases the packet delay in the LTE accessnetwork, which results in the increase and also variation ofthe TCP RTT and thus in turn degrades the end user appli-cation performance. (3) Packet losses: there are two mainreasons for packet losses: (i) the packets which are stored inthe RLC buffer of the source eNB can be lost, as these pack-ets are usually not forwarded to the target eNB; (ii) if theadditional X2 traffic leads to high link congestions, it canlead to packet drops in the PHB buffers, which usually ap-ply dropping functions like Random Early Detection (RED)or tail drop functions to prevent link overload. The packetlosses will result in TCP retransmissions and degradation ofthe user throughput. It shall be noticed that there can be ad-ditional delays due to HARQ over the air interface, but theseadditional HARQ delays can be ignored for the calculationas their values are very small compared to the transport net-work delays. When dimensioning the X2 interface, the ana-lytical models shall consider the above challenges to be ableto properly estimate the end user application performancefor the extra link load, additional transport network delaysand potential packet losses caused by the HO. The follow-ing parts present the proposed analytical methods to esti-mate the X2 traffic load and the overall average applicationdelays including those experiencing HO, and then explainhow to dimension the X2 interface.

5.1.1 Estimation of the X2 load

For one UE, the HO traffic to be sent on the X2 interfaceconsists of two parts: (1) forwarding traffic: data, which hasbeen received at the source eNB before the HO and is storedin the PDCP buffer, needs to be forwarded to the target eNB;(2) rerouted traffic: during the path switching period (timeperiod to inform the aGW of the HO event of a UE), theaGW continues sending data to the source eNB, which isrerouted to the target eNB.

The total amount of the X2 traffic strongly depends onthe number of UEs and the user mobility, the time requiredfor an HO, the application traffic models and their servicerates. The defined traffic model and service rate of differentapplications determine the average traffic load of a UE. Theuser mobility determines inter-eNB HO rate, i.e. frequencyof inter-eNB HO per eNB. In this study, we use RandomDirection mobility model for UEs to move randomly withinor across the cells. We consider 3 sites (cells) per eNB andassume a hexagonal shaped site where each side is of lengthl meter, and in average n users per cell who are distributeduniformly in the site area with average velocity of v m/s. Theinter-eNB HO rate Rinter−eNB is calculated with equation(16). It defines the number users crossing the site boundaryper second by integrating the user flux density over velocityspace and all site boundaries.

Rinter-eNB = 4

π√

3 · l · n · ν (16)

Let tHO define the path switching time for an HO, dpdcp

denote the average amount of data stored in the PDCP bufferwhich needs to be forwarded to the target eNB due to theHO. LUE_NRT and LUE_RT depict the NRT traffic load andthe RT traffic load of a UE on the IP level respectively,and TPOH denote the transport overhead (i.e. the trans-port UDP/IP and the layer 2 protocol overheads). The av-erage amount of X2 traffic LX2 is estimated with (17). Itis seen that here the X2 traffic load consists of both RTtraffic and NRT traffic. However for NRT traffic we re-duce the TCP rate by half as a consequence of the TCPfast retransmit mechanism to deal with the lost packets inHO; while for RT traffic the data rate has no change causedby the HO.

LX2 = Rinter -eNB ·(

LUE_RT · tHO

+ 1

2· LUE_NRT · tHO + dpdcp

)

· TPOH (17)

5.1.2 Estimation of the application delay

For dimensioning the eNB transport link and in turn the X2interface, we extend the M/G/R-PS approach for the dimen-sioning of the X2 interface for elastic traffic [9], by taking

2646 X. Li et al.

the additional X2 traffic, X2 delay and packet losses causedby the HO into consideration.

The first extension is to add the estimated X2 traffic withthe S1 traffic to calculate the total amount of traffic of eachPHB class carried on the transport link. It is assumed that thepercentage of traffic of each PHB class at the X2 interfaceis as same as at the S1 interface, thus the average amount ofthe X2 traffic of PHB class k can be approximated in (18).

LX2(k) = LX2 · S1(k)∑

i LS1(i)(18)

Thus, for the PHB class k, the total traffic carried on the eNBtransport link LS1+X2(k) is calculated below.

LS1+X2(k) = LX2(k) + LS1(k) (19)

To estimate the average application delay of elastic traf-fic flows of the PHB class k, we apply the same M/G/R-PSapproach as presented in Sect. 4.1, but the (4) in the step 1(used to calculate the available bandwidth that can be usedfor the PHB class k) is replaced by (20). Here CS1+X2 de-notes the total eNB transport link bandwidth which carriesboth the S1 and the X2 traffic.

CS1+X2(k)

= max

{(

CS1+X2 · wk∑

i wi

)

,

(

CS1+X2 −∑

j �=k

LS1+X2(j)

)}

(20)

With CS1+X2(k), then in the step 2, for the PHB classk the normalized traffic is ρk = LS1+X2(k)/CS1+X1(k).Furthermore in the step 3, Rk is replaced with Rk =�CS1+X2(k)/r�.

With the new ρk and Rk the overall average transfer de-lay can be obtained by (5) using the basic M/G/R-PS model,or by (7) when choosing the extended M/G/R-PS model. Inthe latter case, the estimated TCP RTT will also include theadditional X2 delay whenever HO happens. Let TX2(k) de-note the X2 delay of the PHB class k, then the estimation forits average RTT including the X2 delay is:

rtt∗k = rttk + TX2(k) · Rinter-eNB (21)

The further extension is to model the impact of packetloss during HO. As explained before, packet loss will trig-ger TCP retransmission and in turn result in degradation ofthe TCP throughput. To model these impacts, we need todetermine the additional traffic caused by the TCP retrans-missions and the resulting TCP throughput as a function ofpacket loss. Let ploss(k) denote the packet loss ratio of thePHB class k, then the additional traffic (caused by the TCP

retransmissions) over the PHB class k is estimated with (22):

LT CP _retran(k) = (LS1(k) + LX2(k)) · ploss(k) (22)

For the elastic traffic flows over the PHB class k, the de-graded TCP throughput thT CP _retran(k) is approximatedwith (23) as a function of the packet loss ratio ploss(k) andestimated TCP RTT rtt∗k , based on the packet level model byKelly [10].

thT CP _retran(k) = MSS

rtt∗

√2 · (1 − ploss(k))

ploss(k)

(ploss(k) > 0) (23)

Thus, due to the additional retransmission traffic and the re-duced TCP throughput, the overall end-to-end applicationdelay will be increased. For the PHB class k, the averageapplication delay is calculated in (24), as the sum of the de-lay derived from the M/G/R-PS model and the extra delayfor TCP retransmissions.

T (xk) = EM/G/R−PS{T (xk)} + LTCP_retran(k)

thT CP _retran(k)(24)

5.1.3 S1/X2 dimensioning process for elastic traffic

Using the above method to estimate the application delayfor each PHB class by considering the impact of HO, wecan dimension the eNB transport link (S1 + X2 interface)following the same steps used for dimensioning the S1 in-terface which is presented in Sect. 4.1.3. However, in theprocess of S1/X2 dimensioning, the allowed X2 delays andpacket loss ratios are also considered as input parameters,because generally the LTE transport network shall guaran-tee a defined transport network delay budget and packet lossratio. Therefore, when dimensioning the eNB transport link,we use the defined X2 delay threshold TX2_target and packetloss ratio Ploss_target in (21)–(24) to calculate the applica-tion delays of different PHB classes. Then through perform-ing delay calculations for a range of bandwidths, we derivethe transport link bandwidth which meets the application de-lay targets of different PHB classes with this numerical ap-proach.

The complete dimensioning of the S1 and the X2 inter-faces for elastic traffic requires the following steps:

(1) Dimension the required S1 bandwidth for elastic trafficusing the approach presented in Sect. 4.1. The dimen-sioned S1 bandwidth is denoted as C(S1)NRT ;

(2) Dimension the eNB transport link bandwidth (S1 + X2interface) using the proposed approach presented in thisSect. 5.1. The dimensioned eNB transport link band-width is denoted as C(S1 + X2)NRT ;


(3) After dimensioning the S1 bandwidth and eNB trans-port link bandwidth, we derive the bandwidth for theX2 interface for elastic traffic (of all NRT services) with(25).

C(X2)NRT = C(S1 + X2)NRT − C(S1)NRT (25)

5.2 S1/X2 dimensioning model for real time traffic

When dimensioning the X2 interface for the RT traffic, weonly need to consider the amount of the HO traffic (X2 traf-fic). As mentioned in Sect. 4.2, the RT traffic does not triggerany retransmissions when there are packet losses in case ofHO, thus the packet loss ratio does not have any impact onthe X2 delays. For the RT traffic, the X2 dimensioning shallmeet a defined X2 delay target. The following parts presentthe proposed analytical methods to estimate the X2 trafficload (only RT traffic) and the resultant X2 delays, and thenexplain the corresponding X2 dimension process.

5.2.1 Estimation of the X2 delay for real time traffic

Firstly, we need to estimate the RT traffic carried on the X2interface. It can be derived from (17) in Sect. 5.1. As seen,the X2 traffic load for RT traffic LX2−RT depends on the RTtraffic load of a UE, the eNB HO rate, the time required foran HO, and the forwarding data in the PDCP buffer of thesource eNB.

LX2−RT = Rinter-eNB · (LUE_RT · tHO + dpdcp) · TPOH (26)

With the estimated X2 traffic load, we can calculate the totalamount of RT traffic (including both S1 and X2 part) of eachPHB class carried on the eNB transport link. We can assumethat the portion of RT traffic of each PHB class at the X2interface is as same as at the S1 interface, thus the averageX2 traffic load of PHB class k for the RT service m can beapproximated in (27).

LX2−RT(k)m = LX2−RT · LS1−RT(k)m∑

i LS1−RT(i)m(27)

For the RT service m, the total traffic of the PHB class k

carried on the eNB transport link (including both S1 and X2part) L(S1+X2)−RT(k)m is calculated below.

L(S1+X2)−RT(k)m = LX2−RT(k)m + LS1−RT(k)m (28)

To estimate the X2 delay of the RT traffic of the PHBclass k, we apply the same M/D/1 approach as presentedin Sect. 4.2.1, but (10) in the step 1 (used to calculate theavailable bandwidth that can be used for the PHB class k)

is replaced by (29). Here C(S1+X2)−RT(m) denotes the totaleNB transport link bandwidth needed for this RT service m.

C(S1+X2)−RT(k)m

= max

{(

c(S1+X2)−RT(m) · wk∑

i wi

)

,

⎛

⎝c(S1+X2)−RT(m) −∑

j �=k

L(S1+X2)−RT(j)m

⎞

⎠

⎫⎬

⎭

(29)

With C(S1+X2)−RT(k)m, then in the step 2, for the PHB classk the normalized traffic load is ρRT(k) = L(S1+X2)−RT(k)m/

C(S1+X2)−RT(k)m. With the new ρk , we can estimate theone-way transport link delay of the PHB class k dlink−RT(k)

with (11)–(12) based on the M/D/1 model. But here forthe RT service m, its packet arrival rate αm(k) on the PHBclass k will be derived from the traffic load on the link, i.e.αm(k) = L(S1+X2)−RT(k)m/θm.

Based on the calculated one-way link delay, we estimatethe X2 delay of the PHB class k dX2−RT(k) as below.

dX2−RT(k) = 2 · dlink−RT(k) (30)

According to the given network structure in Fig. 2, the X2traffic is firstly forwarded from the source eNB to the inter-mediate IP router (uplink direction) and then sent from therouter to the target eNB (downlink direction). In this case,the X2 delay consists of one-way link delay on the uplinkand one-way link delay on the downlink. If assuming sym-metric RT traffic on uplink and downlink, then the resultantlink delay will be same on uplink and downlink. Thus, theX2 delay will be equal to two times of the one-way link de-lay.

5.2.2 S1/X2 dimensioning process for real time traffic

With the above method to estimate the X2 delay for eachPHB class for every RT service, we dimension the eNBtransport link (i.e. S1 + X2 interface) for the RT traffic fol-lowing the procedure used for dimensioning the S1 interfaceas presented in Sect. 4.2.2.

The dimensioning of the S1 and X2 interfaces for RT traf-fic requires the following procedure:

(1) Dimension the required S1 bandwidth for RT traffic us-ing the approach presented in Sect. 4.2. The dimen-sioned S1 bandwidth is denoted as C(S1)RT;

(2) Dimension the eNB transport link bandwidth (S1 + X2interface) using the proposed approach presented inSect. 5.2. The dimensioned eNB transport link band-width is denoted as C(S1 + X2)RT ; (3) After dimen-sioning the S1 bandwidth and eNB transport link band-width, we derive the bandwidth for the X2 interface forRT traffic with (31).

C(X2)RT = C(S1 + X2)RT − C(S1)RT (31)

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5.3 Dimensioning of the S1/X2 interface

Finally, the total required bandwidth for the eNB transportlink carrying both RT and NRT traffic is calculated as thesum of the link bandwidth required for individual traffictypes in (32).

C(S1 + X2) = C(S1 + X2)RT + C(S1 + X2)NRT (32)

And the total required bandwidth for the X2 interface is cal-culated as the sum of the X2 bandwidth required for individ-ual traffic types in (33).

C(X2) = C(X2)RT + C(X2)NRT (33)

It shall be noticed that the calculated bandwidth is only forthe user plane. If there are additional IP bandwidths used forthe control and signaling traffic, then these extra bandwidthsneed to be added in addition.

6 Results analysis

This section validates the applicability of the proposed an-alytical models for the dimensioning of the S1 and the X2interface, by comparing the analytical results with the LTEsystem simulation results for different scenarios.

6.1 Validation of the S1 dimensioning model

This section presents validations of the S1 dimensioningmodels for each traffic type. At the end of this section, sev-eral S1 dimensioning examples are given. For the followingvalidations, we investigate a single eNB scenario withoutmobility (i.e. no handover) on the downlink direction. TheeNB consists of 3 cells, each cell with a capacity of 10 Mbps.

Firstly, we validate the proposed dimensioning model forelastic traffic. In the following, we investigate the scenariowith FTP traffic. The FTP traffic model is defined with aconstant file size of 2 Mbyte or 5 Mbyte, and with exponen-tially distributed inter-arrival time between files. Each cellhas 10 FTP users and in total there are 30 users in the eNB.In the first example, all users have the same QoS priority,i.e. there is no prioritization in the transport network. Theconfigured S1 link bandwidth is 10 Mbps. Figure 6 showsthe average FTP transfer delay in seconds over different S1utilizations. Figure 6(a) gives the results for 2 Mbyte file and(b) gives the results for 5 Mbyte file.

In Fig. 6, both analytical results derived from the pro-posed S1 dimensioning model based on M/G/R-PS (seeSect. 4.1) and the simulation results obtained from the LTEsystem simulations are presented and compared against eachother. It is seen that for both cases the calculated averageapplication delays match properly with the simulated delaysfor different S1 utilizations.

Fig. 6 Average FTP application delay over S1 utilization (no priority)

In the second example the LTE network defines two usergroups: 50% premium users and 50% basic users. The pre-miums UEs have higher priority and mapped to AF PHB inthe S1 transport network, while the basic UEs are mappedto BE PHB. For the applied WFQ transport scheduler, theweight of BE PHB is 1 whereas the weight of AF PHB isset to 10. Figure 7 shows the average FTP transfer time todownload a 5 Mbyte file for different S1 link utilizations peruser priority. Figure 7(a) shows the mean transfer delays ofpremium UEs and (b) shows the delays of basic UEs. Fig-ure 7 also demonstrates that the proposed analytical modelcan provide a suitable estimation for the average applicationdelays for each user priority for the elastic traffic.

Secondly, we validate the dimensioning model for realtime traffic. In the following examples, we investigate thescenario with only VoIP traffic. The applied voice trafficmodel is defined in Table 1. The configured S1 bandwidthis 5 Mbps. Figure 8 presents the mean S1 delay over S1link utilization for two cases: 10 VoIP UEs per cell (i.e.30 UEs per eNB) and 20 VoIP UEs per cell (i.e. 60 UEsper eNB). In these two cases, all VoIP users are transmit-


Fig. 7 Average FTP application delay over S1 utilization (2 priorities)

ted with the same priority. Figure 8 shows that the M/D/1model can give proper evaluation for the average S1 delay(in ms) compared to the simulations in both cases. Further-more, Fig. 9 presents the VoIP only scenario with 10 UEsper cell where there are 50% premium users (mapped to AFPHB) and 50% basic users (mapped to BE PHB). We applythe approach described in Sect. 4.2, using the M/D/1 modelper priority class. The results in Fig. 9 verify the applicabil-ity of the M/D/1 model for dimensioning for the RT trafficwith multiple priorities.

In the following we show S1 dimensioning results. In thefollowing dimensioning examples, the eNB carries 17.4%RT traffic (VoIP, video and streaming) and 82.6% elastic In-ternet traffic (FTP and web traffic). The applied traffic mod-els are described in Table 1. There are in total 30 UEs pereNB (10 UEs per cell), which are divided to premium andbasic user groups. On the S1 transport, the premium usertraffic is mapped to AF PHB and the basic user traffic ismapped to BE PHB. For the dimensioning, the defined QoStarget for the elastic traffic is defined by the delay factor,which defines the tolerance level of the increased applica-

Table 1 Traffic models

FTP Traffic Model

File size constant dist. 5 Mbyte

Inter-arrival time exponential dist.

Web Traffic Model

Page size constant dist. 100.1 Kbyte

reading time exponential dist.

VoIP Traffic Model

Voice Codec G.729 A Codec (8kbps coding rate)

Call activity Speech period: exp. dist. (3 seconds)

Silence period: exp. dist. (3 seconds)

Call duration 90 seconds

Streaming Traffic Model

Frame size Incoming: constant dist. 1166bytes

Outgoing: constant dist. 20 bytes

Frame rate 15 frames /second

Duration 600 seconds

Video Traffic Model

Frame size Incoming/outgoing: 800 bytes

Frame rate 10 frames /second

Duration 90 seconds

tion delay. In the given examples, we set the QoS target ofthe elastic traffic to 10% increase of the application delay(i.e. delay factor = 1.1); and for RT traffic the average S1delay shall be less than 15 ms.

Figure 10 shows the dimensioned S1 bandwidth (inMbps) for (a) different distribution of user groups: 20% pre-mium users with 80% basic users and 50% premium userswith 50% basic users; and (b) for different offered load lev-els: S1 load = 8 Mbps and S1 load = 12 Mbps. The analyt-ical results are derived from the proposed S1 dimensioningmodels, which satisfies the defined application delay (forelastic traffic) as well as S1 delay (for RT traffic) QoS tar-gets. For elastic traffic, we use the proposed M/G/R-PS ap-proach as presented in Sect. 4.1. For real time traffic, weuse the proposed M/D/1 approach as presented in Sect. 4.2.The total S1 bandwidth is derived from (15). The calcu-lated bandwidths are compared with the simulation results.It is clearly seen from Fig. 10(a) and (b) that the analyticalresults from the proposed dimensioning model give quiteaccurate estimations for the required S1 bandwidth for dif-ferent situations compared to the simulations. The given di-mensioning examples demonstrate a suitable applicabilityof the proposed S1 dimensioning models for the dimen-sioning of the S1 interface supporting both elastic and RTtraffic.

2650 X. Li et al.

Fig. 8 Average S1 delay over S1 utilization (VoIP)—no priority

6.2 Validation of the S1/X2 dimensioning model

For validating the X2 dimensioning model, we consider abasic scenario with two eNBs, which are connected to theaGW as shown in Fig. 2. In this network setup, the logicalX2 interface is actually realized by connecting the two trans-port links via an IP router. In this case, the offered S1 trafficis multiplexed with the X2 traffic over the transport link.

The impact of mobility and HO is shown in Fig. 11. Itshows the average application delays of the web traffic ofeach user priority for different link utilizations, with or with-out HO, from the LTE system simulations.

It is seen from Fig. 11 that for the premium UEs, thereis no significant impact of HO on the application delay per-formance because of higher transport priority. However, forbasic UEs the application performance is influenced by theHO, especially under high link utilizations. Due to the HO,there are additional X2 traffic load over the link and more-over each IP packet experiences additional X2 delays overthe transport network. Therefore, the resulting applicationdelay is increased compared to the case of no HO. Fur-thermore, for the lower priority traffic of basic users under

Fig. 9 Average S1 delay over S1 utilization (VoIP)—2 priorities

high link utilization (i.e. high link congestions) more packetdrops are caused in the case of HO, which lead to more TCPthroughput reduction and TCP retransmissions and hencemore degradation of the application throughput.

In the following example we validate the X2 dimension-ing model for the scenario with different transport priori-ties. In this example, there are 40% premium users (mappedto AF PHB) and 60% basic users (mapped to BE PHB).Each user transmits both FTP (2 Mbyte) and web traffic(100.1 Kbyte). In the network, there are two eNBs, each con-sisting of 3 cells. Each cell has a cell capacity of 10 Mbpsand has 10 UEs. All UEs are moving with an average inter-eNB HO interval of 41 seconds. Figure 12 compares thecalculated average FTP transfer delay (including HO sit-uations) with the S1/X2 dimensioning model and the re-sults obtained from the LTE system simulations for differenttransport link utilizations. Furthermore, the results are com-pared to the ones from the scenario without HO. In case ofno HO, we apply the S1 dimensioning model to estimate theaverage application delays. Figure 12(a) gives the averagetransfer delay of premium users and (b) gives the delays ofthe basic users.

It can be clearly seen from Fig. 12 that the calculatedaverage FTP application delays with the proposed analytical


Fig. 10 Dimensioning results for the S1 interface

Fig. 11 Analysis of the impact of HO

dimensioning models (both S1 and X2 dimensioning model)match well with the simulated delays for both basic and pre-mium user priorities in scenarios with or without HOs. Fig-ure 12(a) shows that for the premium users there is no sig-nificant impact of HO on the application delay due to itshigher transport priority. However for basic users, as shownin Fig. 12(b), the application delays increases under highlink utilizations compared to the scenario without HO, due

Fig. 12 Average FTP application delay over link utilization with HO

to additional X2 delays and packet losses. And the increaseof delays is captured well by the proposed X2 dimensioningmodel. Also Fig. 13 demonstrates that the X2 dimensioningmodel can also properly estimate the application delay forthe web traffic in case of HO.

The following gives a number of examples of dimension-ing of the S1 and X2 bandwidth. Firstly we present the di-mensioning for elastic traffic only. We take the above ex-ample with 40% premium users and 60% basic users eachtransmit FTP (2 Mbyte) and web traffic (100.1 Kbyte). Inthis example, per eNB the carried traffic load on the linkis 7230 kbps and the generated X2 load is 17.4 kbps. Fordimensioning, the QoS target for elastic traffic is the appli-cation delay, which is defined by delay factor that indicateshow much increase of the application delay. Table 2 showsthe dimensioned S1, X2 and link bandwidth from the pro-posed S1 and X2 dimensioning models and the simulationsfor two different delay factor QoS targets. For dimension-ing of the S1 bandwidth alone, we use the proposed S1 di-mensioning approach as presented in Sect. 4.1. For dimen-sioning the eNB transport link (i.e. S1 + X2 interface), we

2652 X. Li et al.

Table 2 S1/X2 dimensioning results for elastic traffic

Simulation Calculation Rel. Error

Case 1: delay factor = 1.1 (10% increase of the delay)

S1 BW 16874 kbps 16980 kbps 0.63%

X2 BW 131 kbps 127 kbps 3.05%

eNB transport link BW (S1 + X2) 17005 kbps 17107 kbps 0.6%

Case 2: delay factor = 2 (100% increase of the delay)

S1 BW 10565 kbps 9930 kbps 6.0%

X2 BW 36 kbps 37 kbps 2.78%

eNB transport link BW (S1 + X2) 10601 kbps 9967 kbps 5.9%

Fig. 13 Average HTTP application delay over link utilization with HO

use the proposed S1/X2 dimensioning model presented inSect. 5.1. Table 2 shows that estimated bandwidths from theproposed analytical models are close to the ones obtainedfrom simulations. It can be further observed that when thereis higher delay factor target (i.e. lower application delay re-

Table 3 S1/X2 dimensioning results for mixed elastic & RT traffic

Simulation Calculation Rel. Error

S1 BW 17010 kbps 18208 kbps 7.04%

X2 BW 95 kbps 99 kbps 4.21%

eNB transport link BW 17105 kbps 18307 kbps 7.03%

(S1 + X2)

quirement), the required S1 and X2 bandwidth is lower andthus the obtained link utilization becomes higher.

Table 3 gives an example of dimensioning of the S1 andX2 interfaces for a mixed RT and NRT traffic scenario. Inthis example, there are 10% RT traffic (VoIP, video andstreaming) and 90% NRT traffic (FTP and web traffic). Thetraffic models are as same as in Table 1, except the FTPservice with 2 Mbyte file and the VoIP service using AMRcodec (12.2 kbps coding rate). There are 40% premium usersand 60% basic users per eNB. It is seen from Table 3 that thecalculated S1, X2 and the eNB transport link bandwidths aresimilar to the simulated bandwidths.

Based on the results shown in Table 2 and Table 3, itis found that the relative errors of the proposed analyticalS1/X2 dimensioning models are all within the range of 10%.This well demonstrates that the proposed analytical mod-els are suitable for the bandwidth dimensioning for the S1and X2 interface as well as the eNB transport link band-width.

7 Conclusion

In this paper, we present two different analytical models todimension the S1 and X2 bandwidths for elastic traffic andreal time traffic in the LTE access transport network. The an-alytical models are validated by comparing with simulationresults for various traffic scenarios. The presented analyticalresults match properly with the simulation results. It demon-strates that the proposed analytical models can appropriately


estimate the application performances of different traffic andQoS priorities and thus can be used to dimension the LTEaccess transport network.

Acknowledgement This work is supported by the research project:Mature (Modeling and Analysis of the Transport Network Layer in theUTRAN Access Network REsearch). The partner of this work is theNokia Siemens Networks GmbH & Co. KG.

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6. Li, X., Schelb, R., Görg, C., & Timm-Giel, A. (2006). Dimension-ing of UTRAN IUB links for elastic Internet traffic with multipleradio bearers. In Proc. of the 13th GI/ITG conference measuring,modelling and evaluation of computer and communication sys-tems, Nürnberg.

7. Li, X., Bigos, W., Goerg, C., Timm-Giel, A., & Klug, A. (2008).Dimensioning of the IP-based UMTS radio access network withDiffServ QoS support. In Proc. the 19th ITC specialist seminaron network usage and traffic (ITC SS 19). Technische UniversitätBerlin, October, 2008.

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9. Li, X., Toseef, U., Weerawardane, T., Bigos, W., Dulas, D., Goerg,C., Timm-Giel, A., & Klug, A. (2010). Dimensioning of the LTEaccess transport network for elastic Internet traffic. In Proc. of the6th IEEE WiMob 2010 conference, Canada, Oct. 2010.

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Xi Li received her bachelor degree(B.Sc.) in Electrical Engineering atSun Yat-sen University, China in1999, and her master degree (M.Sc.)in Electronics and Telecommunica-tion engineering at Dresden Uni-versity of Technology, Germany in2002, and her PhD degree in Com-munication and Information Tech-nology at University of Bremen,Germany in 2010.Right after finishing her masterstudies, she joined the Communi-cation Networks Group at the Uni-versity of Bremen as a scientist re-

searcher and PhD candidate. In 2003, she worked in European IST-Xmotion project. Since September 2003, she is leading an industrialresearch project funded by Nokia Siemens Networks (NSN) on dimen-sioning of 3G UMTS/HSPA radio access networks. The main respon-sibilities are simulation model design and performance analysis, anddeveloping analytical models for dimensioning the access network. Atpresent she is leading the industrial research project on dimension-ing the LTE (Long Term Evolution) system funded by Nokia SiemensNetworks. From 2009 she starts working within the innovation teamof “Adaptive Communications” of the Center for Computer Scienceand Information Technology (TZI) in Bremen. Dr. Xi Li has publishedmany scientific papers in the field of dimensioning of mobile networks.She is a member of IEEE and VDE/ITG (Information Technology So-ciety, Germany).

Umar Toseef received his Bachelorof Science in Electrical Engineeringdegree from the University of En-gineering and Technology, Lahore,Pakistan, in 2004. He worked as adesign engineer for more than a yearin And-Or Logic Pakistan (PVT)LTD—one of the leading System-on-a-chip (SoC) solution providersin Pakistan. In 2007 he receivedhis Master of Science in Communi-cation and Information Technologydegree from University of Bremen,Bremen, Germany. Right after com-pleting his master studies he was ap-

pointed as a research assistant at Center for Computer Science and In-formation Technology (TZI) at the University of Bremen in the Com-munication Networks (ComNets) group. From 2006 till 2008, he con-tributed in research programs under ScaleNet project funded by theGerman Ministry of Education and Research (BMBF). Since 2009 hehas been working in two research projects on LTE (Long Term Evo-lution) system level studies and LTE transport network dimensioningfunded by Nokia Siemens Networks. He is pursuing his PhD at Com-Nets, University of Bremen. His contributions to research literature in-clude several scholarly articles and patents (pending for approval).

2654 X. Li et al.

Dominik Dulas completed uniformmaster degree (M.Sc.) studies in thefield of Teleinformatics (Informa-tion and Communication Technolo-gies) with specialization in Telein-formation Networks Design at theWrocław University of Technology,Poland in 2008.In 2008, after completing his mas-ter study he joined the NetworkEngineering department in NokiaSiemens Networks (NSN) as a Net-work Engineer for Access TransportNetworks. Subject of his work inNSN is dimensioning and configu-

ration of access transport networks for WCDMA and LTE systems.The main responsibilities are specification of simulation model anddevelopment of methods, tools and documentation for dimensioningof transport networks. In addition, he is also responsible for prepara-tion and conducting of trainings on transport networks dimensioningand configuration. He has published several scientific papers in thefield of dimensioning of LTE network.

Wojciech Bigos received the M.S.degree (2001) from the AGH Uni-versity of Technology in Krakow,Poland and the Ph.D. degree (2006)from the University of Rennes 1,France, in co-operation with theR&D Division of France Télécom.Until end of 2010 he has been work-ing as a network engineer at NokiaSiemens Networks DevelopmentCentre in Wroclaw, Poland. His pro-fessional interests include networkplanning and optimization incl. di-mensioning algorithms, traffic flowcontrol methods and performance

optimization of access and core network architectures.

Carmelita Görg received herdiploma degree from the Depart-ment of Computer Science, Univer-sity of Karlsruhe and the Dr. rer. nat.degree and the appointment as lec-turer from the Department of Elec-trical Engineering, RWTH AachenUniversity. From 1985 until 1989she worked as a consultant in thefield of communication networks.Since 1989 she has been workingas a group leader and since 1997 asAssistant Professor at the Commu-nication Networks Institute, RWTHAachen University. Since 1999 she

is leading the Communication Networks Group (ComNets) at the Uni-versity of Bremen within TZI (Center for Computer Science and In-formation Technology). Her research interests include: PerformanceAnalysis of (Wireless) Communication Networks, (Rare Event) Simu-lation, Mobility Support, Network Virtualization. Prof. Görg has beenactive in European projects starting with the RACE program. She hasbeen an evaluator and auditor for the European Commission. Theresearch group in Bremen consists of 5 postdocs and 10 Ph.D. stu-

dents/research assistants, which are funded by the state of Bremen andthird-party projects (EU, DFG, BMBF, industry). Prof. Görg has pub-lished a large number of scientific papers in the field of communicationnetworks. She is a member of the board of the ITG (Information Tech-nology Society, Germany).

Andreas Timm-Giel received hisDipl.-Ing. in Electrical Engineer-ing/Information Technology (EE/IT)at University of Bremen, Germany,1994, Dr.-Ing. (PhD) in EE/IT onradio channel modeling, Universityof Bremen, 1999.From 1994 to 1999 he led the Mo-bile Systems group at the Universityof Bremen participating in severalR&D projects on mobile and satel-lite communications.In January 2000 he joined Medi-aMobil Communication GmbH asProject Manager. Besides of Euro-

pean R&D projects, he was involved in the technical and commercialset up of the mobile satellite network and service provider M2sat Ltd.In December 2002 he joined the Communication Networks Group atthe University of Bremen as senior researcher and lecturer. He wasleading several industrial, national and EC funded research projects atthe university. From October 2006 he was additionally directing theinterdisciplinary concerted activity “Adaptive Communications” of theCenter for Computer Science and Information Technology (TZI) inBremen.Since November 2009 he is full professor at Hamburg University ofTechnology and head of the Institute of Communication Networks. Hisresearch interests are the Future Internet, mobile and wireless commu-nication networks and sensor networks.

Andreas Klug received his Dipl.-Ing. (M.Sc.) in Electrical Engineer-ing/Telecommunications Technolo-gies at University of Dortmund,Germany, 1989.1990 he joined Siemens, until 1993in the Central Research Laborato-ries in Munich with focus on anal-ysis and development of broadbandaccess pilot projects.From 1994–2000 he took over var-ious responsibilities in SiemensR&D Broadband Networks focusedon concept for fixed access transportnetworks in research, standardiza-

tion, development and verification labs in Munich and Ottawa, Canada.Since 2000 he changed to Mobile Communication business, being re-sponsible in System Engineering as team leader for GERAN/UTRANnetwork architecture and Performance Management of Mobile Radiosystems.Since 2007 Andreas Klug joined Network Engineering department inNokia Siemens Networks (NSN), first heading team for Mobile AccessTransport Networks and E2E Network Performance, since 2009 lead-ing international team in Ulm and Wroclaw for LTE Radio & AccessNetworks covering LTE network dimensioning, configuration, plan-ning, field performance, customer related simulation and SON (SelfOrganized Network) concept analysis.

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