AbstractThis paper presents analytical models to dimension

the transport bandwidths for the S1 interface in the Long Term Evolution (LTE) Network. In this paper, we consider two major traffic types: elastic traffic and real time traffic. For each type of traffic, individual dimensioning models are proposed. For validating these analytical dimensioning models, a developed LTE system simulation model is used. The simulation results demonstrate that the proposed models can properly estimate the required performances and thus be able to be used for link dimensioning for various traffic and network scenarios.

I. INTRODUCTION The roadmap of Next Generation Mobile Network

(NGMN) is to provide mobile broadband services. Services like Mobile TV, multimedia online gaming, Web 2.0, and high-speed Internet will produce tremendous traffic in the future mobile networks. To make this happen, 3GPP introduces a new radio access technology, known as Long Term Evolution (LTE) to ensure the competitiveness of the 3GPP technology family for the long term. LTE supports extensively high throughput and low latency, improved system capacity and coverage performance.

LTE introduces a new air interface and radio access called as Evolved UMTS Terrestrial Radio Access Network (E-UTRAN), which is specified in the new 3GPP Releases 8 and 9. To support the LTE radio interfaces and the E-UTRAN, 3GPP also specifies a new Packet Core, the Enhanced Packet Core (EPC) network architecture. This paper is only focused on dimensioning the transport network of the E-UTRAN (i.e. the LTE access network), which is based on IP. E-UTRAN is designed to support high data rates, low latency, and hence to bring improved user experience with full mobility. This is achieved by introducing a new, fully IP-based flat architecture with enhanced Node B (eNode B) directly connected to access gateway (aGW). The eNode B (denoted as eNB in this paper) is in charge of Radio Resource Management (RRM) decision, scheduling of users, etc. The aGW provides termination of the LTE bearer and acts as a mobility anchor point for the user plane. The eNB is connected to the aGW with the S1 interface. Between the eNBs the X2 interface is defined, which is used to connect the eNBs with each other in the network. The X2

X. Li, U. Toseef, T. Weerawardane, and C. Goerg are with TZI-ikom,

Institute of Communication Network, University of Bremen, Germany (e-mail: xili | umr | tlw @ comnets.uni-bremen.de, cg@c-b-g.de).

W. Bigos, D. Dulas are with Nokia Siemens Networks Sp. z o.o., Wroclaw, Poland (e-mail: wojciech.bigos | dominik.dulas @nsn.com).

A. Timm-Giel is with Institute of Communication Networks, Hamburg University of Technology, Germany (email: timm-giel@tuhh.de)

A. Klug is with Nokia Siemens Networks GmbH & Co. KG, Mnchen, Germany (e-mail: andreas.klug@nsn.com).

interface is needed for the case of handover to forward the traffic from a source eNB to its target eNB.

This paper is aimed to propose efficient analytical models to calculate the necessary bandwidths for the S1 interface. The objective of the dimensioning is to minimize the transport network costs (for leasing IP bandwidth) while being able to fulfill the QoS requirements of various services. In this paper, we consider two fundamental types of traffic: elastic traffic and real time traffic. Elastic traffic is generated by non real time (NRT) applications and is typically carried by the TCP protocol. Typical applications are Internet services like web browsing and FTP. Real time (RT) traffic is associated with real time applications, which are delay-sensitive and have strict packet delay requirements over the transport networks. Typical applications in this traffic class are VoIP, streaming or video conferencing. In this work, for the dimensioning of the S1 interface the defined QoS requirement for the elastic traffic is the end-to-end application throughput or transfer delay (which specifies the amount of data that can be transferred in a certain time period); while for real time traffic the considered QoS is the transport network delay, i.e. the end-to-end packet delay through the S1 interface (called S1 delay).

In this paper, we propose two individual analytical models for each traffic type for the dimensioning of the S1 interface to meet their individual QoS requirements. The proposed analytical dimensioning model for elastic traffic is based on the M/G/R-Processor Sharing (M/G/R-PS) model, which characterizes TCP traffic at flow level and is often used to calculate the mean transaction time or throughput for TCP flows. For real time traffic, we propose simple queuing model on the packet level to estimate the transport network delay performance. Furthermore, we present how to apply these two proposed analytical models for carrying out the bandwidth dimensioning for the S1 interface. In this work, a LTE simulation model is developed to validate the analytical results from the proposed dimensioning models. This LTE simulator models in detail the important LTE network entities, protocol layers, required scheduling and QoS functions, etc.

The remainder of the paper is organized as follows: section II gives an overview of the developed LTE simulation model. Section III presents the analytical dimensioning model for elastic traffic, and section IV presents the analytical model for real time traffic. In Section V, the complete dimensioning procedure is summarized. In section VI, the proposed analytical models are validated by simulations.

II. LTE SIMULATION MODEL The LTE simulator is implemented using OPNET

simulation software. The developed LTE simulation model is shown in Fig. 1. It includes all basic E-UTRAN and EPC network entities. The main focus of this simulation model is

Dimensioning of the LTE S1 Interface X. Li, U. Toseef, T. Weerawardane, W. Bigos, D. Dulas, C. Goerg, A. Timm-Giel and A. Klug

on the LTE access network.

Figure 1. LTE Simulation Model

Fig. 1 shows an example scenario with two eNBs, and a number of IP routers between the eNBs in the E-UTRAN network and also connecting the eNBs with the aGW. The EPC user-plane and control plane network entities are represented by the aGW network entity. The aGW includes the functionalities of the eGSN-C (evolved SGSN-C) and eGSN-U (evolved SGSN-U). The remote node represents an Internet server or any node that provides the Internet services.

Fig. 2 shows the LTE user-plane protocol structure which is developed within this LTE simulator. The protocols are categorized into three groups: radio (Uu), transport, and end-user protocols. The radio (Uu) protocols include the peer to peer protocols such as PDCP, RLC, MAC and PHY between UE entity and eNB entity. The PDCP, RLC and MAC (including air interface scheduler) layers are modeled in detail according to the 3GPP specifications in this simulator. But the PHY (physical) layer is not detailed modeled since our focus lies on the LTE access network. However the effect of the radio channels and PHY characteristics are modeled at the MAC layer in terms of the data rates of individual user performance. For the UE mobility, general mobility model such as random directional and random way points are used.

Figure 2. LTE Protocol Structure (user-plane)

The LTE transport network is based on IP technology. The user-plane transport protocols as shown in Fig. 2 are used at both S1 interface and X2 interfaces. It mainly includes the GTP, UDP, IP and L2 protocols. Ethernet is used as the layer 2 protocol for the current implementation. IP protocol is the one of the key protocols which handles routing, security (IPsec), services differentiation and scheduling functionalities. The LTE transport network applies the DiffServ-based QoS framework and it is established by connecting a number of IP DiffServ routers between the eNBs and the aGW. DiffServ is

developed by the IETF [1], which defines the three most common Per Hop Behavior (PHB) groups corresponding to different service levels: Expedited Forwarding (EF), Best Effort (BE), and Assured Forwarding (AF). In the LTE transport network, each PHB is assigned to a transport priority and has its own buffer in the transport scheduler. To serve different PHBs, Weighted Fair Queuing (WFQ) scheduling is used. The definition of WFQ discipline is given in [2]. Let wk be the weight of the kth PHB queue and BW the total available IP bandwidth. If there are in total N PHB queues and all queues are transmitting data, then the kth queue obtains a fraction of the total capacity BWk as calculated in equation (1). It shall be noticed that if one priority queue is empty (i.e. not utilizing its allocated bandwidth) then its bandwidth shall be fairly shared by the other queues according to their weights.

BWw

wBW N

ii

kk =

=1

(1)

For modeling the end-user protocols, the standard OPNET protocols such as application and TCP/UDP are used. They are located at the remote Internet server and each UE entity. Furthermore, the control-plane is not directly modeled within the LTE simulation model. However the effect of signaling such as their overhead and delays are considered at the respective user-plane protocols upon specific requirements.

III. 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 itself to adapt to the available bandwidth in the network. If TCP works ideally (i.e. instantaneous feedback), all elastic traffic flows going over the same link will share the bandwidth resources equally and thus the system is essentially behaving as a Processor Sharing (PS) system [3].

The M/G/R-PS model has become a popular approach for dimensioning of different fixed (e.g. ADSL) and mobile networks (e.g. UMTS). An introduction of the basic M/G/R-PS model can be found in [3]. In [4], an extension of the basic M/G/R-PS model was proposed which considers the impact of TCP slow-start. In [5, 6, 7] the author applied the M/G/R-PS model to dimension the Iub transport links in the UMTS network for elastic traffic. In this paper, the M/G/R-PS model is further extended for dimensioning the LTE S1 interface. Special efforts are to use the M/G/R-PS model and extend it to model both the LTE radio interface and the S1 interface using Differentiated Service (DiffServ) QoS scheme; and based on that we propose a framework for estimating the end-to-end application performance of the elastic traffic, which consists of the air interface model and the S1 model.

A. Framework of the Dimensioning Model By analyzing the LTE system model, it can be concluded

that the end-to-end application performance is essentially influenced by both air interface and S1 interface. They are the two major bottlenecks through the end-to-end path. The air interface determines the radio resource each UE can get. The higher the air interface utilization, the lower will be the

average UE throughput as a result of the congestion over the air interface. The S1 interface is the second capacity bottleneck. A congested S1 link can result in significant increase of the end-to-end transfer delay. Thus, in order to estimate the end-to-end performance, we need to model these two bottlenecks individually in the analytical dimensioning model. Hence, the proposed framework of the dimensioning model consists of the air interface model and the S1 model.

Given the traffic models and the number of UEs in the cell, the air interface model calculates the maximum average UE throughput but without considering any congestion in the S1 transport network. Then the S1 model will take the maximum UE throughput obtained from the air interface model as an input parameter and then estimate the impact caused by the S1 link on the overall end-to-end performance. In the following, we will introduce the detailed modelling of the air interface and the S1 interface individually.

B. Modeling of the Air Interface The air interface scheduler will have important impact on

the achievable UE throughput. In this work, we consider the case of scheduling all UEs in a cell in a round robin manner, i.e. all UEs are equally served with the same priority. That means, all elastic flows can share the common radio resources equally and in this context the air interface can be modeled as a Processor Sharing (PS) system. Furthermore, there is no maximum bearer rate limitation for each LTE bearer (i.e. for each individual flow). That means, one elastic flow can take the complete radio resource for itself if there are 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 the M/G/1-PS model is defined for the situations where the flow rate is not limited, i.e. each flow has the ability to fully utilize the whole capacity when no other flow is present in the system [5].

Let CUu denote the cell capacity (in bps) and LoadUu be the average traffic load in a cell (in bps). Here LoadUu can be calculated from the given traffic models and total number of active UEs in the cell. It is the sum of traffic of all services. As a result, the average utilization of the air interface is calculated as pUu = ( LoadUu / CUu ). Based on the sojourn time formula of the M/G/1-PS model, we can derive the delay factory fUu with equation (2). The delay factor is larger or equal to 1. It quantifies the increase of the transfer time (or decrease of the effective throughput) of individual flows as a result of the air interface congestion. It is noted that when the air interface utilization pUu is higher the delay factor fUu also becomes higher, which implies that the application delay (or file transfer time) will be increased. It is noted that here the delay factor fUu for elastic traffic also considers the traffic load of real time services. As real time traffic contributes to the total traffic load and also shares the available radio resources with the elastic traffic, and thus it as well has impact on the total congestion and influences the application performance of the elastic traffic. Therefore, we also need to take the real time traffic into consideration when estimating the end-to-end performance of the elastic traffic in our model.

)1/(1 UuUu pf = (2)

With fUu we can derive the average UE throughput r as a result of air interface utilization in equation (3). It is noted that r is only limited by the air interface capacity, assuming that there is no congestion through the transport network (given sufficient capacity). If the air interface capacity is fixed, r represents the maximum average UE throughput given an ideal transport network. Thus, in the next step we take r as the peak UE data rate for dimensioning the S1.

UuUu fCr /= (3)

C. Modeling of the S1 Interface As shown in section II, the LTE S1 transport network is

based on IP using DiffServ QoS framework together with the WFQ scheduling. The main idea of the proposed analytical models for dimensioning the S1 link is to apply the M/G/R-PS model per PHB class (i.e. per transport priority), while taking the potential multiplexing gain of bandwidth sharing among different PHB classes into account. The basic model for dimensioning an IP-based transport link in the UMTS networks, which deploy the IP DiffServ QoS structure, is presented in [7]. This paper will further extend this basic model to model the LTE S1 interface and also to capture the detailed TCP slow start behavior.

For the analytical model, let CS1 be the S1 bandwidth. For each PHB class, we define LS1(k) be the mean offered traffic of the PHB class k (including both RT and NRT traffic) and wk denotes its WFQ weight. The following gives the detailed steps to calculate the average application delay of elastic traffic flows transmitted over the PHB class k. Step 1: given the CS1, estimate the available bandwidth that can be used for the PHB class k, denoted as CS1(k), using equation (4). It shows that CS1(k) has a minimum bandwidth that equals to the allocated bandwidth assigned by the WFQ transport scheduler according to its weight wk (see equation (1)), and also consider any additional bandwidth if the other PHBs do not fully utilize their allocated bandwidth share.

= kj SS

ii

kSS jLCw

wCkC )(,max)( 1111

(4)

Step 2: With the CS1(k) the normalized traffic load of the PHB class k, denoted as k, can be derived with k = LS1(k)/ CS1(k), for the given mean offered traffic over the PHB class k. Step 3: For the PHB class k, apply the M/G/R-PS model to estimate the application performance. Here R is determined by

rkCR Sk /)(1= . Here r, which is the maximum average UE data rate, is the result of the air interface model calculated from equation (3). It is noted that r is used for each PHB class, since at the air interface there is no QoS prioritization, thus all PHB classes have the same maximum average UE data rate. Step 4: For the PHB class k, the expected sojourn time (or average transfer time) for transferring a file of length xk can be derived from the basic M/G/R-PS model [3], as given in equation (5).

{ } kkkk

kkkkkPSRGM fr

xR

RRErxxTE =

+= )1(

),(1)( 2// (5)

Here E2 denotes Erlangs second formula (Erlang C formula),

which is given in equation (6) with Ak = Rk k. It is known that the Erlang C formula calculates the delay probability (i.e. the probability that a job has to wait) of Erlangs delay system. fk is the delay factor of the TCP flows of the PHB class k over the S1 interface.

=

+

=1

0

2

!!

!),(k k

k

R

i kk

k

k

Rk

ik

kk

k

k

Rk

kk

ARR

RA

iA

ARR

RA

ARE (6)

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

{ } { }{ }

+

interface. Here LRT(k) can be derived by calculating the corresponding application load (as explained in section III) including additional protocol overheads. Let CRT be the total S1 bandwidth needed for this RT service. The following gives the full steps to calculate the average S1 delay of this RT service transmitted over the PHB class k, where the LTE transport network applies the WFQ scheduler to serve different transport priorities. Step 1: With CRT we can estimate the available bandwidth that can be used for this RT service over the PHB class k, denoted as CRT(k), with equation (10). It shows that CRT(k) has a minimum bandwidth that equals to the allocated bandwidth assigned by the WFQ transport scheduler according to its weight wk (see equation (1)), and also consider any additional bandwidth if other PHBs do not fully utilize their allocated bandwidth share. It is noted that if all RT traffic is mapped to an EF PHB with a strict transport priority over the elastic traffic, then let CRT(k) = CRT since k = 1 in this case.

= kj RTRT

ii

kRTRT jLCw

wCkC )(,max)( (10)

Step 2: With CRT(k) and the given mean offered traffic LRT(k), the normalized traffic load of the PHB class k, denoted as RT(k), can be derived with RT(k) = LRT(k)/ CRT(k). Step 3: For the PHB class k, we apply the M/D/1 model to estimate the S1 delay performance. Firstly, we estimate the average queue length of the M/D/1 model:

)(1)(5.0)()(

2

1//kkkkL

RT

RTRTDM

+= (11)

Step 4: Then with Littles law, the mean S1 delay of this RT service on the PHB class k, can be derived with equation (12).

kDMDM kLkd /)()( 1//1// = (12) Here k is the packet arrival rate of this RT service over the PHB class k, which can be derived from its offered S1 traffic load on the PHB class k, i.e. LRT(k), and the packet length of this RT service with equation (13).

/)(1 kLSk = (13)

B. Bandwidth Dimensioning for Real Time Traffic For RT traffic the objective of the dimensioning is to find

the necessary S1 bandwidth for a mean S1 delay target. The dimensioning procedure is given in the following steps. Step 1: define an initial S1 capacity for RT service j; Step 2: for RT service j, estimate its mean S1 delay for each PHB class of this service with the above method (refer to equation 10-13). If the obtained S1 delay of one PHB class can not meet the required S1 delay target, then the S1 capacity needs to be increased. Thus for each PHB class k the required S1 link bandwidth is derived numerically by performing delay calculations for a range of bandwidths until the resulting average S1 delay from a certain S1 bandwidth reaches the defined S1 delay target. This step will be done for each PHB class of this RT service. At the end, the bandwidth required for the PHB class k is denoted as BWRT(k) j. Step 3: we take the maximum bandwidth of all PHB classes to be the required S1 capacity for the RT service j: BWRT(j)=

max.{BWRT(k)j}, as it will satisfy the QoS requirements of every PHB class. Step 4: If there are several RT services, we repeat step 1-3 to derive the bandwidth for each RT service, and then sum up their dimensioned bandwidths to be the required S1 capacity for carrying total RT traffic in the network.

=j

RTRT jBWBWS )(_1 (14)

V. BANDWIDTH DIMENSIONING FOR S1 Usually the network transmits both elastic and real time

traffic, where the objective of the S1 dimensioning needs to fulfill both the end-to-end application delay or throughput of elastic traffic (or NRT services) and a mean packet delay through the S1 interface for real time traffic. In this case, the S1 dimensioning shall combine the dimensioning procedure for both traffic types. From the proposed dimensioning procedure for elastic traffic (explained in section III part D), we can derive the required S1 bandwidth for supporting all NRT services, i.e. S1_BWNRT. And for real time traffic, we apply the dimensioning steps described in section IV to derive the required S1 bandwidth S1_BWRT for supporting all RT services in the network. At the end, the total required bandwidth for the S1 interface is calculated as a sum of the bandwidth required for individual traffic type in equation (15)

RTNRT BWSBWSBWS _1_1_1 += (15) It is noted that this bandwidth is only for the S1 user plane.

If there are additional IP bandwidths reserved for the control and signaling traffic, then these extra bandwidths also need to be added to compute the total S1 bandwidth. It shall be also noticed that the proposed dimensioning approach can be used for dimensioning of both uplink and downlink bandwidth.

VI. RESULTS ANALYSIS This section validates the applicability of the proposed

analytical models by comparing the analytical results with the LTE system simulation results for different traffic scenarios. For the following validations, we investigate a single eNB scenario without mobility (i.e. no handover) on the downlink direction. The eNB consists of 3 cells, each cell with a capacity of 10Mbps.

Firstly, we validate the proposed dimensioning model for elastic traffic. In the following, we investigate the scenario with FTP traffic. The FTP traffic model is defined with a constant file size of 2Mbyte or 5Mbyte, and with exponentially distributed inter-arrival time between files. Each cell has 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. The configured S1 link bandwidth is 10Mbps. Fig. 3 shows the average FTP transfer delay in seconds over different S1 utilizations. Both analytical results derived from the proposed model based on M/G/R-PS (see section III) and the simulation results obtained from the LTE system simulations are presented and compared against each other. The left diagram gives the results for the case of 2Mbyte file and the right one gives the results for 5Mbyte file. It is seen that for both cases

the calculated average application delays match properly with the simulated delays for different S1 utilizations.

0 0.2 0.4 0.6 0.8 12

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Figure 3. Average FTP application delay over S1 utilization (no priority)

In the second example the LTE network defines two user groups: 50% premium users and 50% basic users. The premiums UEs have higher priority and mapped to AF PHB in the S1 transport network, while the basic UEs are mapped to BE PHB. For the applied WFQ transport scheduler, the weight of BE PHB is 1 whereas the weight of AF PHB is set to 10. Fig. 4 shows the average FTP transfer time to download a 5Mbyte file for different S1 link utilizations per user priority. The left figure shows the mean transfer delays of premium UEs and the right one shows the delays of the basic UEs. Fig. 4 also demonstrates that the proposed analytical model can provide a suitable estimation for the average application delays for each user priority for the elastic traffic.

0.4 0.45 0.5 0.55 0.6 0.65 0.70

5

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ave

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Figure 4. Average FTP application delay over S1 utilization (2 priorities) Secondly, we validate the proposed dimensioning model for

real time traffic. In the following examples, we investigate the scenario with only VoIP traffic. The applied voice traffic model uses the G.729A codec (8kbps coding rate) and has a call duration of 90s. The configured S1 bandwidth is 5Mbps. Fig. 5 presents the mean S1 delay over S1 link utilization for two cases: 10 VoIP UEs per cell (i.e. 30 UEs per eNB) and 20 VoIP UEs per cell (i.e. 60 UEs per eNB). In these two cases, all VoIP users are transmitted with the same priority. It shows that the M/D/1 model can give proper evaluation for the average S1 delay (in ms) compared to the simulations in both cases. Furthermore, Fig. 6 presents the VoIP only scenario with 10 UEs per cell where there are 50% premium users (mapped to AF PHB) and 50% basic users (mapped to BE PHB). We apply the approach described in section IV, using the M/D/1 model per priority class. The results in Fig. 6 verify the applicability of the M/D/1 model for dimensioning for RT traffic with multiple priorities.

0 0.2 0.4 0.6 0.80.2

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Figure 6. Average S1 delay over S1 utilization (VoIP) 2 priorities

VII. CONCLUSION In this paper, we present two different analytical models to

dimension the S1 bandwidths for elastic traffic and real time traffic in the LTE access transport network. The analytical models are validated by comparing with simulation results for various traffic scenarios. The presented analytical results match properly with the simulation results. It demonstrates that the proposed analytical models can appropriately estimate the application performances of different traffic and priorities and thus can be used to dimension the LTE S1 interface.

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2. A. Demers, S. Keshav, and S. Shenker. Analysis and simulation of a fair Queueing algorithm. J. Internetworking Res. Experience, pp. 326, Oct. 1990; also in Proc. ACM SIGCOMM89, pp. 312.

3. K. Lindberger. Balancing Quality of Service, Pricing and Utilisation in Multiservice Networks with Stream and Elastic Traffic. In Proc. of the International Teletraffic Congress (ITC 16), Edinburgh, Scotland, 1999.

4. A. Riedl, T. Bauschert, M. Perske, A. Probst. Investigation of the M/G/R Processor Sharing Model for Dimensioning of IP Access Networks with Elastic Traffic. First Polish-German Teletraffic Symposium PGTS 2000.

5. X. Li, R. Schelb, C. Grg And A. Timm-Giel. Dimensioning of UTRAN Iub Links for Elastic Internet Traffic. In Proc. of the 19th International Teletraffic Congress, Beijing, Sep. 2005.

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