06139780

Performance of LTE SON Uplink Load Balancing in

Non-Regular Networks

Jussi Turkka

Department of Communications Engineering

Tampere University of Technology

Tampere, Finland

[email protected]

Timo Nihtil

Magister Solutions Ltd

Helsinki, Finland

[email protected]

Ingo Viering

Nomor Research GmbH

Munich, Germany

[email protected]

AbstractThis paper presents a performance evaluation of an

uplink load balancing algorithm for Long-Term Evolution (LTE)

Self-Organizing Networks (SON) in a non-regular network

layout and shows how cell sizes affect the performance of the

algorithm. The proposed algorithm solves local overload

situations by handing over users to neighboring cells and

adjusting power control settings. However, the non-regular

network layout and the varying cell size can limit the expected

gains of the algorithm in some situations due to the chosen uplink

load balancing strategies and the limitations of the LTE uplink

radio access technique. The performance evaluation is done by

using a fully dynamic LTE system simulator which is capable to

model user and network characteristics, mobility and radio

resource management (RRM) algorithms accurately.

Keywords - Radio Network Optimization; Non-regular Network

Layout; Self-Organizing Networks; Uplink Load Balancing;

I. INTRODUCTION

A rapid evolution of cellular networks and an increased

capacity demand has led to a situation where network

operators need to maintain large multi-vendor radio access

networks. The burden of operating and maintaining a complex

network infrastructure has caused a need to develop automated

solutions for network deployment, operation and optimization

which would reduce the operational expenditures and at the

same time improve the perceived end-user quality-of-service

(QoS). Self-organizing networks and Minimization of Drive

Tests solutions are currently researched by the network

vendors in the 3rd Generation Partnership Project (3GPP)

making possible the solutions for the network deployment

automation.

A target of the SON work item in the 3GPP is to define the

necessary measurements, procedures and interfaces to support

the self-configuration, self-optimization and self-healing use

cases which can dynamically affect the network operation, and

therefore, improve the network performance and reduce the

manual operation efforts [1]. The SON use cases in [1] target

to a coverage and capacity optimization, energy savings

optimization, interference reduction, automatic configuration

of physical cell identity, mobility robustness optimization,

mobility load balancing optimization, random access channel

optimization, automatic neighbor relations configuration, and

inter-cell interference coordination. However, the actual

solutions are not discussed in the 3GPP and the algorithms are

usually left to be vendor specific solutions.

A mathematical framework of one possible SON load

balancing (LB) algorithm for a downlink direction was

presented in [2] and the performance was evaluated later in

[3]. The downlink load balancing algorithm resulted in a better

network performance and an improved user happiness which

means that more users were able to achieve the given service

requirement e.g., the average bitrate requirement. Moreover,

the uplink load (UL) balancing algorithm was presented in [4].

The uplink enhancements were related to the limitations of the

maximum number of simultaneously scheduled users and

power limitation of the uplink direction. The solutions in [4]

were related to a better understanding of the overload and the

better control of initiating LB handovers (HO) together with

UL specific load adaptive power control (LAPC) which was

introduced in [5].

In this article we study the uplink load balancing

performance together with the proposed enhancements in a

non-regular cellular layout which was not analyzed in [4].

Moreover, the gain in non-regular layout is compared with the

regular hexagonal network layout with inter-site distance of

500 and 1732 meters. This paper is organized as follow.

Section II describes the uplink load balancing algorithm. In

Section III, the simulation parameters and assumptions are

described. Finally, section IV concludes the article.

II. ALGORITHM

A. Algorithm Description

The basic principle of the load balancing algorithm is to

handover a selected user (UE) from a stronger but overloaded

cell to a weaker but underloaded cell as described in [2-4]. By

doing so, the UE QoS is improved in both cells. This can

happen if the underloaded cell with a weaker signal strength

can allocate more physical resource blocks (PRB) to the

handovered UE than what was allocated in the overloaded

cell, and if the scheduler of the overloaded cell can allocate

the released resources to make some of the existing unhappy

UEs happier.

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications978-1-4577-1348-4/11/$26.00 2011 IEEE 162

The algorithm for the downlink load balancing is described

in [4]:

1. Collect measurements during a measurement period. 2. Detect an overload situation. 3. Find the best LB HO candidate. 4. Execute a LB HO. 5. Optimize a HO offset towards the LB HO candidate

cell.

In uplink, the functionality is similar but there are some

differences for detecting the overload and finding the best

neighboring cell for the load balancing handover as shown in

[4]. In addition to the handover based load balancing, the

uplink loading can be adjusted by tuning the power control

parameters [5], and therefore, the algorithm performance

depends on how much emphasis is put on these two different

load balancing strategies.

A virtual load c in a cell c is the sum of served UE component loads during the measurement period as described

in [4]:

,

1

,cN

u cuc

u u c

ARBGBR

BR SRB

(1)

where the component load of UE u inside the sum is a product

of two terms. The first term is a ratio of the guaranteed bitrate

GBRu and the realized bitrate BRu. The latter term is a ratio of

the allocated resources ARBu,c and total amount of schedulable

resource SRBc. The variable Nc is the total number of served

UEs during the measurement period. The cell c is overloaded

in case pc > 1 as explained in [2]. The definition of the

overload works well in downlink since all the PRBs can be

allocated always and therefore the users are unhappy mainly

due to the lack of resources. However, in uplink direction,

there are other reasons for UE unhappiness which cannot be

detected by using (1) as a sole overload indicator as explained

in [4]. The other reasons for the UE unhappiness are a UE

power limitation and a network control channel limitation.

B. Uplink limitations

If the UE is in a power limitation, then the available

transmission power, which is limited by the UE capabilities,

and the power control mechanism defines the maximum

available transmission bandwidth e.g., the number of ARBu,c

per transmission time interval (TTI). The assumption in [4] is

that the LB handover cannot improve the QoS of the power

limited UE if it is connected to the strongest cell unless the

weaker cell can schedule the UE more often. This can limit the

number of UEs which can be LB handovered if the UE QoS is

to be guaranteed to remain at least the same.

In case of the maximum scheduled users (MSU) limitation

as described in [4], the available Physical Downlink Control

Channel (PDCCH) resources limit the maximum number of

simultaneously scheduled user. This limitation together with

the power limitation can result in a situation where the cell is

underloaded in terms of PRB usage and yet there are many

UEs which cannot achieve the service requirement target such

as GBRu. In this study these UEs are called unhappy UEs.

This means that the sum in the latter part of the product in

(1) is never 1 even though the resources are always shared

with the maximum number of UEs in every TTI. If some of

the UEs are power limited and unable to allocate all the

available bandwidth to be happy, then there are unhappy users

at the same time with unused PRB resources. Therefore, the

first part of the product in (1) is larger than one but at the same

time the latter part of the product is smaller than 1. This can

results in an unhappiness even if the virtual load is smaller

than 1. Hence, the scheduler should never schedule the

resources to several power limited UEs at the same time since

this results easily in a large amount of unused PRBs.

C. Load Adaptive Uplink Power Control

The idea of the load adaptive uplink power controlling is to

adjust the available transmission power per PRB according to

the interference which tends to increase along the traffic

loading [5]. The algorithm adjusts dynamically the static

power offset P0 depending on the network loading by allowing

users to transmit data with the lower power per PRB in the

underloaded cells. This allows wider bandwidth allocations

which results in fewer problems due to MSU limitation e.g.

fewer resources are wasted if many power limited users are

scheduled simultaneously. However, there is a danger that in

large cells, the P0 is reduced too much which can drown cell

edge UEs to noise e.g. the power per PRB in transmission is

so small that the received PRBs power at eNB falls below the

thermal noise thresholds. This can limit the usage of LAPC in

load balancing and was the main reason why the uplink load

balancing and LAPC performance was analyzed in non-

regular layout consisting of small and large cells.

The LTE power control algorithm is specified in [6]. In this

study, a simplified version of the algorithm was used

excluding the transport format and power correction offsets as

shown in (2)

0 10min , 10log ,MAXP P P M PL (2)

Where the P is the power per TTI defining the maximum

bandwidth allocation the UE is capable to transmit. The PMAX

is the maximum available transmission power. The M is the

bandwidth allocation expressed in number of PRBs. The P0 is

the power offset and is a variable defining the degree of the fractional path loss compensation. The variable PL is the

estimation of the downlink path loss.

In this study, the dynamic LAPC adjustment of the P0 was

made based on the following equation (3)

0, 0, 1010log ,initial

c c cP P (3)

where the variable 0,

initial

cP is the initial P0 of the cell c and it is

adjusted based on the virtual loading of the cell. In case the 163

virtual loading is small, the P0,c is reduced resulting smaller

power allocation per PRB. How should the initial P0 be

configured in the first place? In regular hexagonal layouts with

inter-site distance (ISD) of 500 and 1732 meters the

assumption for a reasonable initial P0,c is -52 dBm. In the non-

regular layout, the initial P0 is based on the cell size and the

estimation of the path loss and a 5 percentile power limitation

rule which assumes that only the 5% of the users allocate all

the available power to a single PRB as shown in (4)

0, ,95% ,initial

c MAX cP P PL (4)

where the variable PLc,95% is the estimation of the cell pathloss

distribution 95 percentile point e.g., the path loss at the cell

edge.

III. SIMULATION AND MODELLING ASPECTS

A. Simulation Tool

The results in this paper are derived by using a fully

dynamic system simulation tool modeling E-UTRAN LTE

release 8 in downlink and uplink and it has been used in

several other publications as in [4,7]. The simulator maps link

level SINR to system level following the methodology in [8].

Both the downlink and the uplink can be modeled in an

OFDM symbol resolution with several radio resource

management, scheduling, mobility, handover and traffic

modeling functionalities. Simulation parameters are based on

the 3GPP specifications defining the used bandwidth, center

frequency, network topology, and radio environment [9].

B. Simulation Scenario

Three different network layouts are used in the simulations.

The performance of two regular hexagonal layouts with 500

and 1732 meters constant ISD were compared with the non-

regular Springwald layout which is described [10]. The

simulations consisted of a calibration simulation and a load

balancing simulation with a moving traffic hotspot. The

calibration simulations were done with and without the load

adaptive power controlling but the HO based load balancing

and the traffic hotspots were excluded. In the load balancing

simulation, the route of the moving traffic hotspot was

planned to go near the estimated cell edges without

overlapping the cell borders directly as depicted in Fig.1. The

overlapping route would balance the loading automatically

between the neighboring cells. The largest cells were located

in the beginning and the end of the route. The smallest cells

were in the middle of the route. In the regular and the non-

regular case, there was an additional tier of interfering cells

causing background interference to the outer tier cells. This

results in a similar kind of interference conditions to all

simulated cells.

In this paper only the uplink direction is analyzed. The

maximum number of the users in the simulation area was set

according to the offered background load target which

depends on the GBRu,c. The UE traffic profile was set to a

constant bit rate (CBR) and was 64, 128 or 256 kbps. The call

length was random and varied being approximately 23

seconds in average. Other simulation parameters are shown in

the Table I and the Table II.

IV. RESULTS

A. Calibration Simulation Results

Table III summarizes the calibration simulation results. The

offered background load was adjusted based on the sum of

users per cell with the selected CBR traffic model. As seen in

the Table III, all the users are happy in the dense ISD 500

network with the chosen bitrates and the LAPC can

Fig. 1. Springwald scenario with traffic hotspot route.

TABLE I

SCENARIO SPECIFIC SIMULATION PARAMETERS

Parameter Value

Regular layout 19 sites, each with 3 sectors

Non-regular layout Springwald

Antenna Type 65 deg, 14 dB gain

ISD 500 m / 1732m / varying

Pathloss model PL = 128.1 + 37.6*LOG10(Rkm)

Penetration loss 20 dB

Center frequency 2 GHz

Shadowing std 8 dB

Channel model 3GPP Typical Urban

Mobility model 3 km/h pedestrian

eNodeB max TX power 46 dBm

UE max TX power 23 dBm

Bandwidth 10 Mhz

Frequency reuse factor 1

TABLE II SIMULATION SPECIFIC PARAMETERS

Parameter Value

System LTE-FDD Rel.8

Simulation Time 71.5 seconds (1,000,000 symbols)

Hybrid ARQ yes

ARQ no

Link Adaptation Both inner and outer loop

BLER target 0.2

Channel sounding yes

Packet Scheduler TD-PF/FD-ATB

Handovers Sliding window size: 200ms

Handover margin: 3 dB

Traffic Model CBR 64/128/256 kbps

Average number of users

per sector

Depends on CBR

164

compensate the unhappiness in case of the higher bitrates by

adjusting the P0 according to the load. The initial P0 settings

were introduced in section II. In sparse ISD 1732 network,

there are unhappy UEs in 64 and 256 kbps CBR services.

LAPC compensation reduces the median P0 to -58.5 dBm for

the low bitrates reducing the unhappiness from 9.6% to 3.1%.

However, in case of 256 kbps bit rate the median P0 is -59.2

dBm but the LAPC compensation cannot reduce the

unhappiness from the 25.3%. In the Springwald, the

performance is a mixture of the dense and the sparse network

layouts due to the varying cell sizes as described in [10].

Table IV shows an example of the estimation of the

pathloss threshold with different P0 settings for three CBR

traffic classes. The PRB requirement M per TTI is calculated

based on robust QPSK modulation with 1/3 coding rate

assuming 0.3 BLER as shown in (5).

1000,

12 14 1

kbps

TTI

GBRM

B CR BLER N

(5)

where the variable B is a bits per symbol and CR is a code rate

of the chosen modulation and coding scheme (MCS). The

term (1-BLER)*NTTI indicates how many TTIs per second can

carry the actual user data at maximum. If the UE is scheduled

always then the NTTI equals to 1000 TTIs per second. It is

worth of noting that the last term is affected by a factor which

depends on the number of the UEs exceeding the MSU

threshold e.g. the probability that the UE can be scheduled

always decreases if there are more active UEs in the cell than

what is the MSU limitation. However, the behavior of this

depends on the scheduler as well.

The pathloss requirement is calculated based on (2)

assuming that the variable is 0.6 and the UEs are using the maximum power to allocate M PRBs always, and therefore, it

estimates the pathloss threshold for the UE power limitation.

The cell edge pathloss based on the 95% criteria in ISD 500,

ISD 1732 and Springwald is 120.5 dB, 133.5 dB and 137.5 dB

as described in [10]. As seen in (5) and Table IV, there are at

least two ways to improve the path loss region threshold. One

way is to use a better MCS which results in a smaller

bandwidth allocation M. Another way is to reduce the P0 if

possible. However, if the P0 is reduced too much, then power

allocation per PRB and signal to interference ratio (SINR)

decreases. This results in a more robust MCS selection and a

wider bandwidth allocation requirement M for the guaranteed

bit rate.

Table IV indicates that the ISD 500 cell edge threshold of

120.5 dB can be compensated with the LAPC if the GBR is

256 kbps just by adjusting the P0. By reducing the P0, the

pathloss requirement for the service is set to a smaller value

that the cell edge threshold of 120.5 dB. In the ISD 1732 case,

the 64 kbps service is already power limited since the pathloss

requirement with initial P0 is larger than the cell edge

threshold of 133.5 dB. To guarantee the 256 kbps service with

4 PRBs bandwidth allocation at the cell edge of a large cell

requires the P0 be smaller than -65.5 dBm according to the cell

edge pathloss and (2). However, the P0 cannot be reduced too

much or otherwise the UEs would drown to the noise because

the SINR gets too small for the chosen MCS [11]. Therefore,

the usage of the LAPC is limited in the large cells resulting in

smaller gains of UE happiness.

B. Traffic Hotspot Simulation Results

Table V shows the overall simulation results for the uplink

load balancing performance in the Springwald. The

simulations were run with three optimization strategies:

a reference case (no LB nor LAPC)

LAPC without HO LB optimization

LAPC and HO LB joint optimization. The route of the traffic hotspot (HS) is depicted in Fig.1. The

UE traffic profiles were 64, 128 and 256 kbps. The overall

results indicate that LAPC and load balancing can provide

gain for the UE happiness. For 256 kbps case, the gain is 3.6%

decreasing the unhappiness from 12% to 8.4%. In other cases,

the gain was approximately 1% but the unhappiness was rather

low as well. However, even if the overall gain in Springwald

is small, there are regions where the LAPC and UL HO load

balancing provided significant improvements as depicted in

Fig.2.

In Fig.2, the blue regions are areas where the UL LB and

LAPC cannot improve the UE happiness and the red regions

indicates areas where UEs are unhappy only in reference case

but not anymore after taking UL LB and LAPC into use. The

leftmost illustration presents the 128 kbps results. In that case,

nearly all unhappy users benefit the usage of the load

balancing in the region of small cells. However, in the region

of the large cells, the load balancing is not able to help the

unhappy users. This is obvious, since the path loss conditions

in the large cells are quite demanding as described in [10].

The rightmost illustration in Fig.2 presents the 64 kbps

results. In that case, there are less unhappy users and the

TABLE III

CALIBRATION SIMULATION RESULTS

CBR 64 kbps 2MB offered load, 32 UEs per Cell

Scenario ISD 500 ISD 1732 Springwald

P0 Scheme Ref LAPC Ref LAPC Ref LAPC

Total no of HOs 966 964 909 890 2609 2573

No of started calls 3844 3844 3752 3823 7797 7805

Unhappy users, UL % 0.0% 0.0% 9.6% 3.1% 1.7% 1.2%

Power limited UEs % 0.0% 0.0% 1.9% 0.9% 0.5% 0.3%

Times MSU limited 1 1 851 413 63 21

CBR 256 kbps 2MB offered load, 8 UEs per Cell

Scenario ISD 500 ISD 1732 Springwald

P0 Scheme Ref LAPC Ref LAPC Ref LAPC

Total no of HOs 440 434 477 516 1444 1393

No of started calls 1843 1846 1676 1682 3653 3676

Unhappy users, UL % 1.2% 0.0% 25.3% 24.4% 7.1% 5.9%

Power limited UEs % 0.6% 0.0% 17.5% 17.0% 3.6% 3.0%

Times MSU limited 0 0 15 9 0 0

TABLE IV

PATHLOSS THRESHOLD FOR POWER LIMITATION

P0=-52 P0=-57 P0=-62

GBRkbps PRB Req. Pathloss Req. Pathloss Req. Pathloss Req.

64 kbps 1 125 dB 133 dB 142 dB

128 kbps 2 120 dB 128 dB 137 dB

256 kbps 4 115 dB 123 dB 132 dB

165

remaining ones were concentrated only in the beginning of the

route which was surrounded by the large cells 5, 23, 25 and 27

and the cell edge path loss was approximately 140 dB. If that

situation is compared with the large ISD regular scenario with

cell edge path loss of 137.5 dB in Table IV, then it can be

concluded that the handover based UL load balancing and the

LAPC cannot improve the network performance.

C. Deployment considerations in Radio Networks

Based on these results and [4], one can conclude that UL

load balancing provides gain for UE happiness in dense

networks for a wide range of traffic profiles. Moreover, it was

shown that the cell edge UEs at large cells cannot benefit of

using the handover based load balancing especially in case

there is a traffic hotspot near the cell borders. In practical

network deployments, the UE mix is likely to be consisting of

indoor and outdoor users. Therefore, in large cells the indoor

users with stationary or smaller velocities cannot be

handovered because they suffer the building penetration loss

and the power and bandwidth limitation due to the larger

signal attenuation. On the other hand, the outdoor users with

smaller path loss and variety of velocities can be handovered

to tackle the overloading problem which makes the load

balancing useful in dense as well in sparse networks.

However, the effects of the higher UE mobility were not taken

into account in this study.

V. CONCLUSION

This paper shows a performance evaluation of the LTE

uplink load balancing algorithm in the non-regular radio

network consisting of small and large cells. Two load

balancing strategies were compared. The LAPC strategy

without the handover based load balancing. Secondly, the

effect of LAPC strategy together with the handover based load

balancing. Based on the results, it can be concluded that in

large cells, the users are power limited and only a little gain

can be achieved either by using the LAPC or the handover

based LB. Therefore, the overall gain in non-regular layout is

smaller compared with the gains in certain spatial regions

where the load balancing improves the performance quite

much. However, this spatial gain is difficult to see, if one is

only observing the global statistics of the network.

VI. ACKNOWLEDGEMENTS

The author would like to thank colleagues from Nokia,

Nokia Siemens Networks, Magister Solutions Ltd, Jyvskyl

University and the Radio Network Group at Tampere

University of Technology for their constructive criticism,

comments and support with the work.

REFERENCES

[1] 3GPP TR 36.902, Self-configuring and Self-optimization (SON) network use cases and solutions, ver. 9.2.0, June 2010.

[2] I. Viering, M. Dttling and A. Lobinger, A mathematical perspective of self-optimizing wireless networks, in proceedings of the IEEE International Conference on Communications, Dresden, Germany, June

2009.

[3] A. Lobinger et al., Load Balancing in Downlink LTE self-optimizing networks, in proceedings of 71th IEEE Vehicular Technology Conference (VTC 2010-Spring), Taipei, Taiwan, 2010.

[4] T. Nihtil & J. Turkka, Performance of LTE Self-Optimizing Networks Uplink Load Balancing, accepted to 73th IEEE Vehicular Technology Conference (VTC 2011-Spring), Budapest, Hungary, 2011.

[5] R. Mller et al., Enhancing uplink performance in UTRAN LTE networks by load adaptive power control, European Transactions on Telecommunications, 2010, DOI:10.1002/ett.1426.

[6] 3GPP TS 36.213, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer Procedures, ver. 9.2.0, June 2010.

[7] P. Kela et al., Dynamic Packet Scheduling Performance in UTRA Long Term Evolution in Downlink, in conference proceeding of ISWPC2008, 2008.

[8] K. Brueninghaus et al., Link performance models for system level simulations of broadband radio access systems, in proceedings of the Personal, Indoor and Mobile Radio Communications (PIMRC05), vol. 4, September 2005.

[9] 3GPP TR 25.814, Physical Layer Aspects for Evolved UTRA, version 7.1.0, September 2006.

[10] J. Turkka & A. Lobinger, Non-regular Layout for Cellular Network System Simulations, in proceeding of PIMRC 2010, Istanbul, September 2010.

[11] J. Turkka and J. Puttonen, Using LTE Power Headroom Report for Coverage Optimization, in proceedings of 74th IEEE Vehicular Technology Conference (VTC 2011-Fall), San Francisco, USA,

September 2011.

TABLE V LOAD BALANCING RESULTS IN SPRINGWALD

CBR 256 kbps Ref. LAPC LAPC + LB

Offered load ( BG ) kbps 2048 MB (8 MT x 256 kbps)

Offered load ( HS ) kbps 12800 MB (50 MT x 256 kbps)

Number of Handovers 1777 1811 2461

Number of LB Handovers 0 0 559 (23%)

No of started calls 4197 4255 4244

Unhappy users, UL 12.0% 9.9% 8.4%

Power limited UEs 3.2% 2.0% 2.4%


Offered load ( BG ) kbps 1536 MB ( 12 MT x 128 kbps)

Offered load ( HS ) kbps 7680 MB ( 60 MT x 128 kbps)







Offered load ( BG ) kbps 1536 MB ( 24 MT x 64 kbps)

Offered load ( HS ) kbps 5120 MB ( 80 MT x 64 kbps)






a) b) Fig. 2. Springwald with a traffic hotspot. a) 128 kbps CBR. b) 64 kbps CBR. 166

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