On the Potentials of Traffic Steering Techniques between HSDPA and LTE.pdf

8/14/2019 On the Potentials of Traffic Steering Techniques between HSDPA and LTE.pdf

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Abstract In this paper traffic steering between a High-

Speed Downlink Packet Access (HSDPA) network and a

3GPP Long Term Evolution (LTE) network with

different carrier frequencies is investigated. First, two

traffic steering algorithms, relying on static network

information, are assessed from a traffic theoretical point

of view and numerically. Furthermore, numerical

analysis of two traffic steering algorithms, relying on

dynamic information such as user SINR and cell load, is

also performed. It is shown that the dynamic trafficsteering algorithms outperform the static methods in

terms of end user performance. Finally, it is investigated

how the LTE terminal penetration affects the

performance of the proposed traffic steering algorithms.

For low LTE terminal penetration all LTE capable

terminals should be pushed to the LTE network, and for

high LTE terminal penetration a more dynamic traffic

steering scheme should be used.

I. INTRODUCTION

Future generation wireless networks are envisioned to

deploy overlay networks whose coverage areas may partiallyoverlap. Those emerging networks may integrate new radio

access technologies such as 3GPP Long Term Evolution and

LTE-Advanced [1] over the existing legacy systems such as

Global System for Mobile Communications (GSM) [1],

3GPP Universal Mobile Telecommunications System / High

Speed Packet Access (UMTS/HSPA) [1], and Wireless

Fidelity (WiFi) [2]. This is for instance due to partial

availability of multimode mobile terminals. Additionally, the

different networks may operate at multiple frequencies and

may introduce different sized cells. Cell sizes may range

from macro layers with large coverage areas to pico or femtocells, which can be used to provide additional capacity, but

in small coverage areas. Figure 1 depicts an example ofoverlaying multi RAT networks.

The interworking of such heterogeneous networks poses

several new challenges. The most critical issues to be tackled

are in the following areas: terminal mobility support forproviding optimised and seamless service across the

networks; interference management to control and reduce the

interference level in case of co-channel deployment; range

extension of small cells; and traffic steering to steer users

towards the network capable of providing the optimal

performance from the user and network point of view. In

general, traffic steering is about assigning the traffic towards

a certain network layer, where a network layer is one

frequency carrier of a certain RAT, which then delivers the

requested service to the user. Such assignment can be

performed keeping several objectives in mind such as to

increase the overall resource utilisation; to optimise user

satisfaction while considering subscription and application

differentiation, and to minimise signalling overhead and

handset power consumption. The exact definition of the

objective priorities will depend on the operator goals and

policies for traffic steering. The topic of traffic steering isdiscussed in several papers. For instance, the mobility

parameters to enforce load balancing such as handover

settings, are explored in [3],[4],[5] in the context of LTE.

Potential traffic steering architectures are presented in [6].

This paper discusses traffic steering techniques between

HSPA and LTE macro cells in the downlink direction and

focuses on optimising the end user performance, measured in

user throughput. A theoretical model of traffic steering

between two layers is presented, and the optimal loadbalancing ratio between the layers is evaluated. Numerical

results are provided, and their match with the theoretical

model is discussed. The impact of the terminal capability is

also investigated.The rest of the paper is organised as follows. Section II is

a brief of the principles of traffic steering and introduces the

proposed traffic steering techniques. Section III describes a

theoretical model of traffic steering between two layers. In

Section IV the system model is presented, and the numericalresults are discussed in Section V. Finally, Section VI

concludes the paper.

Figure 1 - Illustrative example of wireless overlaying network structure.

Niels Terp Kjeldgaard Jrgensen#1

, Daniela Laselva#2

, and Jeroen Wigard#2

#1

Aalborg University, Aalborg, Denmark#2

Nokia Siemens Networks, Aalborg, Denmark#[email protected]

#2{name.surname}@nsn.com

On the Potentials of Traffic Steering Techniques

between HSDPA and LTE

HSPA/LTE

UMTS/HSPA/LTE

Macro / rural

Micro

Pico

Femto / indoor solutions /

WiFi

GSM/UMTS/HSPA

978-1-4244-8329-7/11/$26.00 2011 IEEE


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II. TRAFFIC STEERING FUNCTIONALITY

In the following the proposed traffic steering techniques

are presented. Those range from the random strategy where

the assignment of users is done purely randomly to the

scheme where the instantaneous user load and user SINR are

used as input for the steering.

It is assumed in this paper that the steering of users is

performed only during the connection setup, and later eachuser will stay active and receive the service in the layer

assigned during the setup phase until it finishes its call. The

capacity of a layer is defined as the average cell throughput

and the load is defined as the number of users in a layer.

A. Random Algorithm (RA)TheRAmethod steers the users randomly towards a layer.

On average 50 % of the users are steered to each layer, but

instantaneously the user distribution per layer may vary. No

a priori information is required when theRAis used.

B. Push-to-Best Layer Algorithm (PBLA)ThePBLAmethod is a static traffic steering algorithm that

exploits knowledge of the layer capacities. A predefinedamount of the users is steered to each of the layers. In this

paper the ratio of users steered to each layer is chosen such

that the user throughput is maximised. This will lead to a

load balancing ratio between the layers which is determined

by the layer capacities.

The particular users to be steered to each of the layers arechosen randomly, according to the predefined ratio, i.e. the

instantaneous load of the layers and the SINR of the users

are not considered.

C. User Load based Algorithm (ULA)In contrast to the previous two algorithms, which steer the

traffic based on static information, the ULA method steersthe traffic dynamically based on the load per layer. The users

are steered to the layer with the lowest normalised load. The

normalised load is defined as the number of users in each

layer divided by the layer capacity:

l

l

Ll C

nUEminarg ,

(1)

where lis the layer index, nUElis the number of users in

layer l, Clis the capacity of layer l andLcomprises the set of

available overlay layers.

D. User Throughput based Algorithm (UTA)The UTA method first estimates the average throughput

which could be achieved in each layer by a user connecting

to the network. The throughput estimate is based on the user

SINR as determined by a stochastic model, the number ofactive users present in each layer, and a SINR-to-user-

throughput mapping curve per layer. The latter could for

instance be based on Shannon bound formulation or derived

from system level studies. User u is steered to the layer lwhich offers the highest average achievable user throughput

TPu.l:

( )1

2 ,,

+

=

l

lu

lunUE

SINRTPSINRTP ,

(2)

( )luLl

TP,minarg

, (3)

where SINRu,l is the SINR of user u in layer l,SINR2TP isthe assumed function that maps the user SINR to the user

throughput for a single user scenario, and TPu,l is theestimate of the average throughput for user uin layer l.

III. ANALYTICAL MODELLING

In this section an analytical model of traffic steering for atwo layer model is presented. The two layers are

characterised by capacity C1 and C2, respectively, and the

traffic steering is modelled by a Markov process. The two-

dimensional process is illustrated in Figure 2. The states in

the model are denotedPx,y, wherexandyare the number ofusers in layer 1 and layer 2, respectively. 1 and 2 are the

arrival probabilities of new calls in layer 1 and layer 2. It is

assumed that the arrival probability of new calls is

independent of the number of users in a layer. 1and 2arethe probabilities of a call finishing in respectively layer 1 and

layer 2. It is also assumed that each user carries the same

amount of data and that the available cell capacity in each

layer is equally shared by the users in that layer. Themaximum number of users in layer 1 and layer 2 are denoted

N1and N2.

Figure 2 - Markov process illustrating the traffic steering mechanisms

between two layers.

The above is a Poisson arrival process, which implies equal

probabilities as time average rates of arrivals and departures

(PASTA property). The probability of a call finishing itssession while connected to one of the layers in a state with n

users can then be expressed as:

,callsize

C

ncallsize

Cn =

= (4)

where C is the capacity in bps of the cell the user is

connected to and callsizeis the size of the call in number ofbits. It is assumed that callsizeis independent of the system,

but Ccan be different for the different layers.


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Assuming a M/M/1/N queue for both layers we calculate the

state probabilityPx,yas [7]:

( ) ( )1

2

22

1

1

11

1

1

1

1,

++

==

LTEHSPA N

y

N

x

PyPxyPx

,(5)

where z equals z/z. The average user throughput is

calculated as the average throughput per state weighted with

the state probability. The average user throughput in forexample layer 1 is expressed as follows:

= =

=

1

1

2

0

1,1 ,

N

x

N

y

yxx

CPTP

(6)

where C1is the capacity of layer 1.

In order to get the Cumulative Distribution Function

(CDF) of the user throughput we need to take into accountthe different bit rates that users experience in a cell, i.e. a cell

centre user experiences a much larger throughput than a user

at the cell edge. Therefore we define the probability of a user

getting throughput k asP(k)=f(k), where we assume that the

user is the only user in the layer utilising all resources. Thisfunction statistically represents the different bit rates a user

experiences due to the location in the cell. The probability of

getting bit rate min layer 1 can be approximated as:

= =

==

1

1

2

0

,1, )()(N

x

N

y

yxu xmfPmTPP.

(7)

The analytical model can be used to determine the

performance of the RA scheme by setting 1 = 2. The

performance of the PBLA scheme can be analysed by

sweeping through different values.

IV. SIMULATION SETUP

This section describes the simulation methodology, the

used assumptions and settings adopted in the numerical

analysis. In the simulations user arrivals are generated

according to a Poisson distribution. The user SINR values

are drawn from a SINR distribution, which depends on the

simulation scenario. The considered scenario is the 3GPPmacro 3 scenario with an inter site distance (ISD) of 1732 m.

User SINRs are kept constant throughout the data sessions

since users are assumed stationary, and neither fast fading

nor shadowing is considered. The analysis is carried out in

the downlink direction. Two layers are present, an HSDPA

layer with carrier frequency at 800 MHz and an LTE layer at2.1 GHz. The SINR CDFs differ less than 1 dB for the two

frequencies. Radio system dependent SINR to userthroughput mapping curves are used to compute the possible

user throughput within HSDPA and LTE. Those curves

reflect the system specific spectral efficiency and are derived

from system level studies. It is also assumed that the cellresources are shared equally among the active users. All

users are HSDPA capable so the LTE penetration level

equals the percentage of dual-mode users.

TABLE I-SYSTEM SIMULATION PARAMETERS

PARAMETER VALUE

Propagation scenario Macro 3 (ISD=1732m) [8]

System bandwidth HSDPA: 5 MHz or otherwise specifiedLTE: 5 MHz

Antenna Configuration 1x2 antennas

HSDPA User Class Up to 15 codes and up to 64 QAM

Traffic model Finite buffer @600 kB

LTE penetration level 100 % or otherwise specified

Traffic steeringalgorithms

{RA, PBLA, ULA, UTA}

Offered load {1 Mbps, 2 Mbps,,8 M ps}

User generation Poisson arrival

Packet scheduler Round robinNo admission control is assumed, since only best effort

traffic is considered. This will result in rather low user

throughputs at high user load. It is also assumed that the load

information is ideally known e.g. without delays. Table I

shows the default simulation parameters.

V. SIMULATION RESULTS

In this section the simulation results are discussed. Part A

compares the results from the analytical approach with the

numerical results of the static traffic steering schemes (RA

andPBLA). In Part B thePBLAmethod with an optimal load

balancing ratio is compared with the dynamic traffic steering

algorithms (ULA and UTA) in terms of average user

throughput and 5 % user throughput. Finally it isinvestigated how the LTE terminal penetration and layer

capabilities affect the traffic steering performance.

A. Comparison Analytical Model and SimulationsIn this part the performance of the RA and the PBLA

schemes is assessed. Figure 3 illustrates the 5 % user

throughput performance of the RA and the PBLA as a

function of the offered load in the overall network. The

curves named HSDPA and LTE show the 5% user

throughput performance for the HSDPA and LTE layerseparately when using the RAmethod. A significant gain is

observed when using thePBLAover theRA.RAperformance

is limited because the HSDPA layer becomes congested as

50 % of the users are steered to each layer. Furthermore the

figure shows that the analytical results and the simulation

results match rather well.

1 1.5 2 2.5 3 3.5 40

0.5

1

1.5

2

2.5

3

Offered Load - [Mbps]

5%UEThro

ughput-[Mbps]

RA - HSDPA - Analytical

RA - LTE - Analytical

RA - Analytical

PBLA - Analytical

RA - HSDPA - Sim

RA - LTE - Sim

RA - Sim

PBLA - Sim

Figure 3 - 5% user throughput results as a function of offered load for

theRAand thePBLAmethods via analytical and simulation studies.

Similarly, in Figure 4 the average user throughput is

shown versus the offered load for the same cases. As


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expected, the average of the RAperformance of the HSDPA

and LTE layers equals the overall RA performance. Thetrends are the same as for the 5 % user throughput. The

PBLA shows a significant gain over the RA. The analytical

results match nicely the results from the simulations. Note

that the analytical model is only valid for stable systems. Inthis investigation the system becomes unstable when the

system load exceeds 4 Mbps. For higher loads the analytical

model cannot be applied any longer.

1 1.5 2 2.5 3 3.5 40

2

4

6

8

10

12


AvgUEThroughput-[Mbps]

RA - HSDPA - Analytical

RA - LTE - Analytical

RA - Analytical

PBLA - Analytical

RA - HSDPA - Sim

RA - LTE - Sim

RA - Sim

PBLA - Sim

Figure 4 - Average user throughout comparison for analytic results and

simulation results.

The results show that the numerical results match the

expected outcome from a theoretical point of view. It was

also shown that the PBLAperformed best, and the PBLA is

used as a reference to the adaptive schemes in the next part.

B. Performance of ULA and UTA over PBLAThe PBLAis in this part compared to the ULA, and UTA.

In Figure 5the 5 % and average user throughput is plotted as

a function of the offered load. The PBLA shows the worstperformance and is used as reference in the rest of this

section. It is seen from the 5 % user throughput that at an

offered load of 6 Mbps the network becomes congested. For

the reference algorithm starvation of the users at the cell

edge happens, while the ULA and UTA are still able to

provide a throughput significantly above zero. When a

minimum 5 % user throughput of 0.5 Mbps is considered,the gain of the ULA and UTA is about 20 % and 40 %

respectively in terms of the extra offered load they could

accommodate.

Also the average throughputs show large gains for the

ULA and UTA over the reference case. For loads up to 4

Mbps the ULA andUTA perform similar, so it is concludedthat no gain is achieved by taking the SINR of the user into

account at low loads. For loads over 4 Mbps the UTA shows

best performance. At a load of 6 Mbps the average user

throughput for the reference, ULA and UTAis 1.3 Mbps, 2.7

Mbps and 3.4 Mbps, respectively. This is equal to gains of

112 % and 162 %.These large gains are possible because the reference

algorithm does not use any instantaneous information and

only relies on the layer capabilities. On the other hand the

two more advanced algorithms steer the users based on

1 2 3 4 5 6 7 80

1

2

3

4

5

6

7

8


Throughput-[Mbps]

PBLA - 5%-tile TP

ULA - 5%-tile TP

UTA - 5%-tile TP

PBLA - Avg UE TP

ULA - Avg UE TP

UTA - Avg UE TP

Figure 5 - Average and 5% user throughput performance for the

PBLA, ULAand UTAmethods.

instantaneous information, e.g. the instantaneous load per

layer, and is thus able to adjust to the dynamics of the

system. The additional gain achieved by the UTA derives

from additionally making use of the instantaneous user SINR

information. The user SINR is used to estimate the expecteduser throughput in each layer at the current load. Figure 6

illustrates the LTE throughput curve divided by the HSDPAthroughput curve vs. the SINR. Note that Figure 6 is not a

result even though it is presented in the result section. At low

SINR the LTE throughput is up to 8 times higher than the

HSDPA-throughput and at high SINR the LTE throughput is

only ~1.5 times higher than the HSDPA-throughput. This

applies for the HSDPA bandwidth of 5 MHz. The

consequence of this is that the UTA steers the low SINR

users to the LTE layer and the high SINR user to the HSDPA

layer in order to maximise the average user throughputs,

equation (2) and (3).

-5 0 5 10 15 200

1

2

3

4

5

6

7

8

SINR - [dB]

TPLTE/TPHSDPA

HSDPA - 5 MHz, LTE - 5 MHz

HSDPA - 10 MHz, LTE - 5 MHz

Figure 6 - LTE throughput mapping gain over the HSDPA throughput

mapping.

The potential gain from utilising the UTAdepends on the

ratio between the LTE and HSDPA throughput. Such ratio

depends on system spectral efficiencies, usage of advanced

network features such as Multiple Input Multiple Output

(MIMO), adopted user receiver type, etc. The closer the ratiois to 1 the lower gain can be achieved with the UTAover the

ULA.


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C. Impact of LTE Penetration and Layer CapabilitiesIn this section it is shown how the LTE penetration level

affects the performance of the PBLA and the UTA. Two

cases with a HSDPA and LTE bandwidth of 5 MHz and load

3 Mbps and 5 Mbps are considered. The third case is with a

10 MHz bandwidth HSDPA layer (Dual-Carrier HSDPA)and a 5 MHz bandwidth LTE layer with load 5 Mbps. Figure

7 shows the average throughput gain of using the UTA over

thePBLA for the three cases. The gain is plotted for different

LTE penetration levels, ranging from 50 % capable LTEusers to 100 % capable LTE users.

50 55 60 65 70 75 80 85 90 95 1000

5

10

15

20

25

30

35

40

45

50

LTE penetration - [%]

Throughputgain-[%]

Avg UE TP, HSPA5LTE5, OL = 3 Mbps



Figure 7 - Average user throughput gains versus LTE penetration of

the UTAover the referencePBLA.

At 100 % LTE penetration the gain of using the UTA is

larger for higher offered load, as was already visible in

Figure 5. The reason for this is that at low loads thelikelihood of just having one or no user per layer is high, and

for that case there is no difference between the algorithms.

For the Dual-Carrier HSDPA (DC-HSDPA) case the gain

at any LTE penetration is larger than in the first two cases.The reason for this is that in case of DC-HSDPA the

throughput of HSDPA is doubled, making HSDPA the

preferred layer for good users, as can be seen in Figure 6,

while for the worst users LTE is still the preferred layer. Thismakes the gain of UTA larger since the algorithm is able to

exploit this information in contrast to PBLA, which selects

users randomly. In case of a 5 MHz system, all users sent to

LTE experience a gain, even though the gain of sending cell

edge users to LTE is largest, but they have least impact on

the average cell throughput, so therefore less gain from UTA.

A general trend for all three cases is that the gain of using

the UTA is decreasing when the LTE penetration isdecreasing. This is explained by the fact that when not all

users are LTE capable, some non LTE capable users are

steered to the non-optimal HSDPA layer. At some point the

best the UTAcan do is to steer all LTE capable users to theLTE layer, just as the PBLA, and no gain is obtained using

the UTA over thePBLA.

The LTE penetration level investigation shows that when

the penetration level is 50 % or below, then the simplePBLAis as good as the more advanced traffic steering algorithms.

When the LTE penetration levels are 75 % or higher then it

is more beneficial to use a more adaptive traffic steering

algorithm. The breaking point where it is not sufficient toutilise a static algorithm anymore depends on the actual

scenario. E.g. if the LTE layer capacity is increased, then the

simple algorithm is sufficient at higher LTE penetration

levels, and if the HSDPA layer capacity is increased, thenthe advanced algorithm is beneficial at lower LTE

penetration levels. Another important aspect that must also

be considered is the traffic volume. In all the simulations thetraffic generated by a user is the same regardless of terminal

capability. It may be expected that LTE users will generate

more traffic than HSDPA users making dynamic traffic

steering algorithms beneficial at lower penetration levels.

VI. CONCLUSIONS

This paper investigates the potentials of traffic steeringbetween HSDPA and LTE. Four traffic steering algorithms

were introduced. Two static approaches were assessed from

an analytical point of view. The comparison of the analytical

results against simulation results shows a fairly good match.

Numerical results showed that the most adaptive UTAscheme significantly outperforms the other methods underhigh load conditions and at 100 % LTE penetration level.

It was also shown that the dynamic schemes, ULA and

UTA, started showing gain for an LTE penetration level

above 75 %. Below that penetration level the static PBLA

performs just as good as the ULAand the UTA. Notice that

the breaking point, when more adaptive strategies are

required, depends on e.g. the layer capabilities, the traffic

volume generated per RAT.

Based on the findings, the following recommendations are

given. When the LTE penetration is low or medium, the best

steering strategy is to push all LTE capable terminals to the

LTE layer. When the LTE penetration level increases, itbecomes beneficial to make use of instantaneous load

information, and a dynamic traffic steering algorithm similar

to the UTAmay be considered.

References

[1] The 3rdGeneration Partnership Project. http://www.3gpp.org

[2] Wi-Fi Alliance. http://www.wi-fi.org

[3] H. Son et al., Soft Load Balancing Over Heterogeneous WirelessNetworks, in Proc. of IEEE Veh. Tech. Transactions, July 2008.

[4] A. Lobinger et al, Load Balancing in Downlink LTE Self-OptimizingNetworks, in Proc. of the 71st IEEE Vehicular Technology

Conference (VTC), May 2010.

[5] R. Nasri et al, Handover Adaptation for Dynamic Load Balancing in

3GPP Long Term Evolution Systems, in Proc. of the 5th Intern.

Conf. on Advances in Mobile Computing and Multimedia, Dec. 2007[6] J. Ha et. al, Dynamic load balancing architecture in heterogeneous

wireless network environment, in Proc. Of Comm. and Inform. Tech.,

9th Internat. Symposium,Sept. 2009

[7] M. Schwartz, Telecommunication Networks: Protocols, Modeling andAnalysis, Addison-Wesley, Reading, Massachusetts, 1988

[8] 3GPP: TR 25.814, Physical layer aspects for evolved UTRA (release7), Sept. 2006, Version 7.1.0.

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On the Potentials of Traffic Steering Techniques between HSDPA and LTE.pdf