Managing and Altering Mobile Radio Networks by Using

SON Function Performance Models

Sören Hahn, Thomas Kürner

Technische Universität Braunschweig

Institut für Nachrichtentechnik

Braunschweig, Germany

{hahn, kuerner}@ifn.ing.tu-bs.de

Abstract— Self-Organising Network (SON) functions change

parameters in the (mobile) network based on input and SON con-figuration parameters. However, it is still unclear how the behaviour of the SON function itself and the impact on the network changes by modifying the SON configuration parameters. The herein proposed SON function performance model (SFPM) shows how the impact on KPIs changes by varying SON function configuration parameters. Moreover it opens up the possibility to steer the network perform-ance towards a certain direction by applying appropriate SON configuration parameters that have been derived by the SFPM.

Keywords—self-organising network; SON; mobility load bal-

ancing; MLB; mobility robustness optimisation; MRO; network

management; son management; operational management; realistic

scenario;

I. INTRODUCTION

Network operators want to run their network according to pre-defined target values so that performance is optimised towards their specific technology objectives. These target val-ues are often defined by Key Performance Indicators (KPIs). With the more and more emerging Self-organising Network (SON) paradigm the improvement of KPIs can be done by various SON function that are running (in a stand-alone manner or in parallel with other SON functions) in the network [1]. With SON functions it is possible to optimise, configure or to heal the network by monitoring the network status and chang-ing network configuration parameters accordingly. Furthermore, SON functions themselves can be configured by SON function Configuration Parameters (SCPs). By changing the SON Configuration parameter Value sets (SCV) the behav-iour of the SON function itself might changes. This allows the operator to use SON as an instrument to manage their network and adapt the target value not only once during the design time, but accordingly based on context specific information (e.g. daytime, cell load, area types, etc.) during run time since these information may change.

However, this opens up a manual gap between the defined target values and the selection and adjustment of such SCV sets. In order to close this gap a novel approach has been pro-posed in [2]. The proposed SON Objective Manager (SOM) is supposed to transform (KPI) target values into SCV sets for different SON functions by creating a policy system that maps (technical) objectives (which may vary over time) with ade-quate SCV sets. To do this a SON Function Performance Model (SFPM) is essential and needs to be provided, e.g. by

the vendor of such SON functions. A SON function model provides information about SCV sets and their impact on KPIs. In this paper potential generation rules and a mechanism to provide such SFPM based on simulations merely will be pre-sented and described in detailed.

This paper is structured as followed: In Section II the con-secutive steps of generating a SON function performance model will be described in depth. Section III focuses on simu-lation results, whereas in Section IV the SFPM is applied and first simulation results will be presented. Finally, Section V provides a conclusion and an outlook.

II. GENERATION OF A SON FUNCTION PERFORMANCE

MODEL

The generation of SFPMs is explained in this section. At first the assumptions to generate such models will be clarified and an overview of the simulation environment is given. In a second step the used SON functions will be briefly described. At last the different consecutive steps of the generation will be explained thoroughly.

A. Modelling assumptions

One approach would be to do the simulation, generation and evaluation network wide. Meaning the network is running with SON functions with only one particular SCV set. In the end the result would be one single SFPM for a specific SON function that is valid for all cells within the network as it was done in previous work. In [3] the Mobility Load Balancing (MLB) SON function comprised the testing of the MLB with different SCV sets which were valid for each cell in the net-work, respectively. Results have shown that this approach might not be the suitable way for this SON function because it was hard to pick a SCV set that showed a remarkable im-provement in terms of network KPIs. A second possibility would be to cluster cells in classes based on attributes (e.g. cell size, traffic demand, user mobility patterns, etc.) and to do the analysis only on selected cells from those cell classes. The SFPM then is valid only for the respective cell class, but the simulation effort would decrease drastically. Other cells that fall in a certain cell class would then have all the same SFPM. The last approach is to do the generation cell specific. Mean-ing, the simulation and the evaluation is done on each and every cell in the considered network, respectively. This ap-proach will lead to SFPMs that are only valid for the given cell.

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DOI: 10.1109/ISWCS.2014.6933349

In the first place the second approach will be neglected and thus the third approach is chosen in order to get as much in-sights on the creation of such SFPMs as possible.

B. Simulation Scenario Enviroment

The analysis was done in a realistic scenario setup. The data represent an area of 3 by 5 km² in the middle of Hannover, Germany and is depicted in Figure 3. Key parameters are given in Table 1 and are described in much more detail in [4].

Network Urban Hannover Scenario

Layer LTE 1800 Macro

Cells (total/inner) 195 / 65

Bandwidth 10 MHz

TX power 46 dBm

Inter-site distance Not available (not regular)

Antenna down tilt 4° (mechanical)

Pathloss model 3D Ray-optical

Antenna model Kathrein 742212 (real)

Table 1: Realistic urban Hannover scenario network

C. Considered SON Functions

For this work two SON functions have been considered, namely: Mobility Load Balancing (MLB) and Mobility Ro-bustness Optimisation (MRO). Both are an outcome of the FP7 SOCRATES project [5].

1) Mobility Load Balancing The SON function MLB tries to improve the network per-

formance by, e.g. decreasing the number of unsatisfied users [6].

MLB

Max_Load

CIO_MaxMax_Load@SeNB

Max_Load@TeNB CIO_Step

Cell Load

UE Measurments

CIO Value

Handover Command

Control Parameter

Input Parameter Output Parameter

CIO: Cell Individual Offset

Figure 1: MLB input, output and control parameters

This is done by reducing highly loaded cells. One possibil-ity to do this is to distribute the traffic among less loaded neighbouring cells in the network and to alter the (virtual) cell boarders by increasing the size of less loaded and decreasing the size of highly loaded cells. The input, output and control parameters are shown in Figure 1.

2) Mobility Robustness Optimisation The objective of MRO is to improve the handover perform-

ance by reducing negative effects like call drops or handover failures [7]. This can be done by changing cell configuration parameters like handover hysteresis or handover time-to-trigger values, resting upon the current network performance. The network performance is calculated based on handover reports coming from the user equipment. The MRO input-, output- and control parameters are shown in Figure 2.

MRO

MRO_Method

Operator_PolicyEvaluation Window

Event_Number Step_Size

(Network)

Handover

Performance

Handover Hysteresis

Handover TTT

Control Parameter

Input Parameter Output Parameter

Figure 2: MRO input, output and control parameters

D. Model Generation

The whole process of generating a SON function perform-ance model is mainly subdivided into four steps: First of all, a respective cell is selected. Second, based on the location and orientation (azimuth angle) of the cell user trajectories are placed around that cell. Third, all (necessary) SON function configuration parameter sets will be generated. At last, based on the trajectories and SCV sets, simulations have to be con-ducted and results will be evaluated.

1) Cell Selection As explained before the generation of the SFPM is single

cell orientated. In a first step one specific cell will be selected. The cell is characterised by the location, the orientation (mean-ing the azimuth angle) of the antenna at the respective base station and the surrounding neighbouring cells. Since a realistic network topology is considered, and not a regular pattern like in a hexagon network, every cell will have a unique size and pattern as shown in Figure 3. Thus, it is required to simulate not only one, but all cells in the network.

Figure 3: Realistic urban Hannover scenario network 3 x 5 km² on the left and a zoomed in area for a specific Site on the right

2) User Test Trajectories After the cell selection pre-defined user trajectories will be

inserted around that cell. Users travel back and forth on one of these trajectories (blue lines and circles in Figure 4). The tra-jectory pattern has been chosen to cover enough of the respective cell. Users moving along those trajectories and pro-duce handover related events by entering and leaving the cell, e.g. successful handover, ping-pong handover or Radio-Link-Failures (RLFs). To cover as much of possible user classes the

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users have varying velocity profiles that range from slow users with 5 km/h (indoor/pedestrian), to faster users with 15 km/h (bicycler, slow traffic), up to fast users with 50 km/h (urban traffic). A final set of user test trajectories for a given cell are shown in Figure 4. In addition to the traffic demands of the individual users it is assumed that the surrounding cells have background traffic to account for a more realistic load and interference situation.

Figure 4: User trajectories for one selected cell

3) SCV Set generation SON functions, or to be more precise the underlying algo-

rithms, are usually working with SON configuration parameters (SCPs), e.g. ranges, step sizes or methods. These SON function configuration parameter values (SCVs) can be varied. The assumption is that the SON function reacts differ-ently based on the chosen SCV set and modifies parameter in a different way. With that the actual outcome of a SON function can be steered towards a certain direction. Having that in mind as much as possible SCV sets should be simulated and evalu-ated in order to get the best understanding of how the SON function affects KPIs. On the other hand not every SCV com-bination can or should be taken into account since the link-level microscopic simulation with the needed degree of com-plexity are often time consuming and not every SCV set contribute as much as other SCV sets.

The herein selected SCV sets are enough to be able to de-rive a first reasonable SFPM. For the MRO function finally 120 SCV sets, as shown in Table 2, have been analysed for each cell in the network. For the MLB function there have been 162 SCV sets in total.

SCV Set ID

Parameter 1 MRO Method

Parameter 2 Window Size

… Parameter 5 Hysteresis Step Size

1 Method I 60 sec 0,5 dB

2 Method II 60 sec 0,5 dB

3 Method I 120 sec 0,5 dB

4 Method II 120 sec 0,5 dB

… … … … …

120 Method II 300 sec … 2 dB

Table 2: Abstract of the considered MRO SCV sets

Please note: It is not the intention that the here derived SFPMs are the final models. We think of it as more as a start-ing point to adjust the network in the first place. It is foreseen to adapt and improve the SFPMs during operation time, e.g. based on learning algorithms.

4) Simulation and Evaluation During the simulation of the different SCV sets counter

values like successful handovers or RLFs produced by the users travelling on the test trajectories are logged. Furthermore additional (network) information is collected. All monitored key performance indicators (KPIs) are listed in Table 3.

KPI Description

1 (Network) Load

2 (Network) Throughput

3 Number of Connected Users

4 Number of Successful Handovers (HO)

5 Number of Ping-Pong HO

6 Number of HO Failures

7 Number of Successful Handbacks after HO Failures

8 Number of RLFs

Table 3: Monitored KPIs during SFPM simulations

Besides the SCV sets a baseline scenario is simulated with no SON function active. This simulation is supposed to show the behaviour of the network without the involvement of SON and to normalise the results that have been obtained with dif-ferent SCV sets. The normalisation is done to directly compare the results (that will be presented in Section III) and to see directly the increased or decreased KPI values. If the value is one the baseline scenario and the SON-enabled scenario show the same performance. If the value differs the SON-enabled scenario shows a degradation or improvement of the perform-ance, depending on the considered KPI.

III. SIMULATION RESULTS

In this section selected simulation results, obtained during the SFPM generation will be presented, for the MRO and the MLB function respectively.

A. Mobility Robustness Optimisation

The total simulated time for the MRO evaluation was set to 20 minutes to see reasonable effects in the network perform-ance caused by the MRO. Having a time granularity of 100ms, it sums up to 12000 simulation steps in total for each SCV set and each cell. Figure 6 and Figure 7 show exemplary results. In Figure 6 a spatial view regarding the handover failure perform-ance for the different SCV sets and the different cells is given. One can observe that, depending on the SCV set and on the respective cell as well, the performance can change drastically. Compared to the baseline scenario the MRO function is able to improve the handover failure performance. On the other hand some SCV sets rather decrease the performance than maintain is as it can be seen in Figure 6.

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Figure 5: Handover failure performance for different MRO SCV sets for every cell in the considered scenario (spatial view on changes due to SCV sets on

cell level)

The performance is also highly dependent on the actual cell, meaning: No real trend in terms of KPI performance for the considered SCV sets is observable. This is also a notewor-thy outcome when it comes towards the operation and management of SON functions in general.

Figure 6: Handover failure performance for different MRO parameter sets for every cell in the considered scenario

Figure 7: (Network) Throughput performance for different MRO parameter sets for every cell in the considered scenario

Figure 7 shows similar effects on a different KPI, namely the (network) throughput performance. There are SCV sets that are more suitable than others. Also many SCV sets have a negative effect on the throughput, but as the MRO is designed to improve HO related KPIs and not throughput this was some-how foreseeable.

B. Mobility Load Balancing

Considering the MLB function the simulated time was set to 15 minutes in order to capture enough effects caused by the SON function. With the same time granularity of 100ms, it sums up to 9000 simulation steps. Exemplary results are shown in Figure 8. It is again noticeable that, in terms of (network) throughput, the direction and severity of the chosen SCV set highly dependent on the considered cell.

Figure 8: (Network) Load performance for different MLB parameter sets for every cell in the considered scenario

IV. APPLICATION OF SFPMS IN THE CONTEXT OF A

REALISTIC NETWORK SCENARIO

In order to investigate the impact on network KPIs the SFPM have been evaluated for one specific (operator) goal. Two examples are given here: First, the goal was to minimise the handover (HO) failures in the network, second the goal was set to maximise the (network) throughput. This was done by selecting the appropriate SCV sets which have been derived in the previous step for each SON function with the best perform-ance value for each and every cell in the network. In total four scenarios have been simulated. A) The network with no SON enabled, B) the network with the MLB function, C) the MRO function (both in a stand-alone version) and D) the network with MLB and MRO running in parallel (without any coordina-tion) with SCV sets for the respective SON function. Results regarding the first goal are shown in Figure 9. One can see that the SCV set combinations, which aim for minimising the HO failures specifically (reddish), achieve better results (especially “HO Failure – MRO”).

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Figure 9: HO Failure performance for the given (operator) goals and SCV sets

Results concerning the second goal are presented in Figure 10. The combination “Throughput – MLB” leads to the highest average network throughput. It can also be observed that A) MRO is not suitable to improve the throughput (since it is designed to improve handover performance and not capacity) and B) the combination of MLB and MRO improves the per-formance, but C) cannot compensate the bad performance of MRO. In fact the combination “HO Failure – MRO + MLB” even has a better performance in terms of network throughput than “Throughput – MRO + MLB”. This is due to the fact that first of all the SON function SCV sets have been derived for each SON function respectively and second, the SON functions run without any coordination which might lead to undesired or faulty network behaviour.

Figure 10: (Network) Throughput performance for the given (operator) goals

and SCV sets

V. CONCLUSION AND FUTURE WORK

In this paper potential design rules for the creation of a SON function performance model have been presented. Be-sides this SFPMs have been derived and evaluated for a given network. Additionally, simulation results for two different (operator) goals have been presented. As the results show it is possible to steer the network (behaviour) towards a certain

direction by applying the right SCV sets for every cell that was derived by the SFPMs and choosing the appropriate SON func-tion that should improve a KPI. Apart from this the results show that the combinations of SON functions may require a coordination function to ensure the right network behaviour. Other approaches to derive such SFPMs may be to learn the behaviour of SON functions and the impact on KPIs over time.

Future work will consider other SON functions, e.g. func-tions that also run on a macroscopic time scale, and a more realistic model generation approach, i.e. realistic user trajecto-ries and not a single cell but a cell class approach in order to tackle the huge simulation effort. As the results also may show only little gain the proposed approach will be studied in differ-ent scenarios to check whether the impact can be enlarged. Besides that the verification of the given SFPM will be studied to have sophisticated means to judge the quality of a given SFPM. Finally the results will be used in cooperation with the proposed SON Objective Manager from [2].

ACKNOWLEDGMENT

The work leading to these results was carried out in the FP7 SEMAFOUR project [8], which is partially funded by the Commission of the European Union.

References

[1] S. Hämäläinen, H. Sanneck and C. Sartori, LTE Self-Organising Networks (SON), Wiley, 2012.

[2] C. Frenzel, S. Lohmüller and L. Schmelz, ““Dynamic, Context-Specific SON Management Driven by Operator Objectives,” IEEE/IFIP Network Operations and Management Symposium (NOMS), Poland, Krakow, May 5th-9th, 2014.

[3] S. Hahn, L. Schmelz, A. Eisenblätter and T. Kürner, “SON Management,” IEEE/IFIP Network Operations and Management Symposium (NOMS), Poland, Krakow, May 5th-9th, 2014.

[4] D. M. Rose, T. Jansen, U. Türke, T. Werthmann and T. Kürner, “The IC 1004 Urban Hannover Scenario – 3D Pathloss Predictions and Realistic Traffic and Mobility Patterns,” European Cooperation in the Field of Scientific and Technical Research, COST IC1004 TD(13)08054, Ghent, Belgium, 2013.

[5] SOCRATES, “The SOCRATES Project,” [Online]. Available: http://www.fp7-socrates.org/. [Accessed 13 March 2014].

[6] A. Lobinger, S. Stefanski, T. Jansen and I. Balan, “Load Balancing in Downlink LTE Self-Optimizing Networks,” 2010 IEEE 71th Vehicular Technology Conference (VTC2010-Spring), Taiwan, Taipei, May 16th-19th, 2010.

[7] T. Jansen, I. Balan, J. Turk, I. Moerman and T. Kürner, “Handover parameter optimization in LTE self-organizing networks,” 2010 IEEE 72nd Vehicular Technology Conference (VTC2010-Fall), Canada, Ottawa, September 6th-9th, 2010.

[8] SEMAFOUR, “The SEMAFOUR Project,” [Online]. Available: http://www.fp7-semafour.eu. [Accessed 13 March 2014].

0.072

0.073

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Throughput - MRO

Throughput - MRO + MLB

Baseline (No SON)

33.038

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33.042

33.044

33.046

33.048

33.05

KPI II: (Network) Throughput

SCV Set Combination

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HO Failure - MLB

HO Failure - MRO

HO Failure - MRO + MLB

Throughput - MLB

Throughput - MRO

Throughput - MRO + MLB

Baseline (No SON)