SON Laboratory:A Multi-Technology Radio Network Simulation

Environment for the SON AnalysisDemonstration

Johannes Baumgarten†, Andreas Eisenblatter∗, Thomas Jansen†, Thomas Kurner†, Dennis M. Rose†, Ulrich Turke∗∗atesio GmbH, Berlin, Germany

Email: {eisenblaetter,tuerke}@atesio.de†Institut fur Nachrichtentechnik, Technische Universitat Braunschweig, Braunschweig, Germany

Email: {baumgarten,jansen,kuerner,rose}@ifn.ing.tu-bs.de

Abstract—The SON laboratory addresses the need for toolsthat analyze the behavior and likely gains from SON solutionsunder realistic conditions. At the core of the SON laboratory arehigh-performance simulation engines that offer extensive system-level multi-RAT network performances analyzes for medium-to large-scale scenarios using high-resolution signal predictions,realistic mobility models, and real-world or realistic traffic data.The simulations within the SON laboratory can be augmented(using XML-RPC) either from MATLAB or via a custom API.Moreover, distributed computing is supported.

Two MATLAB simulations that build on the SON laboratoryare demonstrated: First, an LTE handover optimization from theSocrates project [1], [2]; Second, an analyzes on the time-variantenergy consumption in mixed GSM and UMTS networks andoptions to save on energy from the FP7 project GreenNets.

I. INTRODUCTION

Mobile network operators await SON. Yet, many oper-ators have a pragmatic approach towards introducing self-organization into their networks: The networks are fairlycomplex systems, and they want to see clear benefits from SONat no or little risk. An accurate evaluation of the performanceof SON functions is therefore a strong requirement prior totheir implementation into the live network [3], [4].

Analyzing the behavior of SON functions is often intricate.One reason is the complexity of the environment, whichobstructs predicting the effects of the SON functions’ controlactions as well as the interactions among them [3]–[6].

The need for some “SON laboratory” is thus evident. It hasto support analyzes of SON solutions by means of computersimulations with network configurations, traffic and mobilitypatterns, etc., modeled as realisticly as reasonably achievable.

We outline principle capabilities of our first version of sucha SON laboratory in Sec. II. We then introduce in Sec. III twodistinct simulation experiments, each of which showcases fea-tures of the SON laboratory. Sec. IV draws some conclusionsfrom the experiences so far and sketches planned extensionsof the SON laboratory.

II. THE SON LABORATORY

The complexity of typical simulation scenarios is growingfor two reasons: First, the complexity of the networks is

increasing, since more radio access technologies (multi-RAT,multi-band) need to be taken into account and the networklayers of different cell types becomes more important. Second,for many SON applications realistic scenarios need to besimulated because the behavior of SON functions will be ascell-specific as the local conditions are.

Our SON laboratory addresses the need for designing andanalyzing SON solutions under realistic conditions, with thepossibility to model the conditions according to an operators’network deployment and typical demand conditions for thatnetwork. That entails realistic network layouts, cell parametersettings, base station antennas, path-loss predictions, traffic in-tensities varying according to location and time of the day, anduser mobility. The network configuration may, for example,be extracted from the configuration management. The trafficand mobility may be modeled after what is observable fromPM data and other measurements. Further data sources suchas mobile measurements, scanner measurements, and SON X-maps [7] can also be taken into account.

The SON laboratory builds on a platform for system-levelradio network analysis, planning, and optimization. It provideshighly efficient and fully automated functionalities for (mixed)GSM/EDGE, UMTS/HSPA, LTE radio networks and is capableof handling hundreds of sites spread over large areas.

One key capability is the effective handling of path-losslayers, which may be defined for outdoor, indoor at differentfloors, streets, tracks, subways, etc. Each layer may havemultiple pixel sizes (down to 1m resolution). Other keycapabilities concern the efficient handling of basic analyzes.Cell loads are derived using static system models (similar to[8] for CDMA and to [9], [10] for OFDMA). Based on thecell load levels, a variety of cell, user, and pixel specificmetrics can be derived (e. g., SINR, interference level, blockingprobability, throughput).

What makes the SON laboratory special according to ourunderstanding lies in the way the above capabilities can beused. The implementation is tuned for high performance aswell as great flexibility, which are often antagonistic features.The design supports parallel processing, cloud-based simula-

tions, as well as distributed simulations connected over theInternet.

There are MATLAB, Python, and C/C++ interfaces (APIs)in order to build extensions, see Fig. 1. One example is tohave separate dedicated user mobility simulators, a demandmanagement and separate SON functions handled by differentextensions. All extensions influence the same simulation andhence the metrics handled by the SON laboratory.

Fig. 1. SON laboratory architecture overview with examples for add-ons.

Furthermore, the simulations of concurrent SON functionsneed not “know” about each other. They simply link to andinteract with the same network simulation.

III. DEMONSTRATIONS

This section presents two simulation studies building onSON laboratory. The LTE SON demo for the automatic gener-ation of initial radio parameters (AGP) from the FP7 projectSOCRATES [11] used an early version of the laboratory.

A. LTE SON Function: Handover Performance Optimization

For this demo, the LTE handover optimization from theSOCRATES project [1], [2] is set-up in the SON laboratoryusing the MATLAB interface. The original implementation wasdone entirely in MATLAB. The ported version still handles themanagement of the user mobility and the handovers withinMATLAB, but all information related to cell signal strengthsat the user positions are retrieved from the SON laboratoryover the Internet. Changing the network configuration, forexample, can now be done much more efficiently. Moreoverthe impact of handover optimization on other SON functionscan be analyzed without integrating other SON functions intothe same simulation environment. Fig. 2 contains a snapshotfrom the MATLAB visualization of a simulation run. The userpositions are updated every 100ms simulation time. In our testenvironment, retrieving all signal strength data for a new setof positions from the platform over a VDLS connection takesabout 200ms. This is no noticeable performance degradationin comparison to the original implementation, where this datawas retrieved locally from precomputed “trace files.”

B. Multi-RAT Network: Time-Varying Energy Consumption1

This demo illustrates the power consumption of cells/sitesin a mixed GSM/UMTS scenario. The study is performedwithin the FP7 project GREENNETS. A SON-light function

1The research leading to these results has received funding from the Euro-pean Union’s Seventh Framework Programme managed by REA - ResearchExecutive Agency (FP7/2007-2013) under grant agreement no 286822.

is simulated that is capable of changing the load-balancingregime depending on the time of the day, see Fig. 3. If theload in a second carrier cell falls below some threshold, thecell is powered down.

IV. CONCLUSION

The SON laboratory as presented here is work in progress.A central objective for the SON laboratory is is to handlerealistic settings in terms of size and complexity of networkexcerpts and environment modeling (including path-loss anduser behavior). Localized, distributed, and cloud-based com-putation models are all supported. The basic feasibility of thisapproach is demonstrated by the two distinct examples.

Eventually, the SON laboratory shall provide means to easilydevelop, simulate, analyze, and tune SON functions and theircoordination in a fully controlled, yet complex environment.The environment facilitates repeatable computational studiesand in-depth analysis of particular phenomena. The SONlaboratory itself does not need to know about the detailsof SON implementations if they are linked in remotely. It’ssufficient to agree on the SON function’s API.

In its most advanced form, the SON laboratory shall allowfor mimicking a live network’s behavior and to “replay” whathas been observed live. The goal is to reproduce the behaviorof the network in simulations in order to understand whythings happened the way they did. This will effectively providemeans to analyze and to correct SON misbehavior, that is, to“debug” SON functions.

REFERENCES

[1] T. Jansen, I. Balan, J. Turk, I. Moerman, and T. Kurner, “Handoverparameter optimization in LTE self-organizing networks,” in VTC Fall2010. IEEE 72nd, Ottawa, Canada, September 2010.

[2] T. Jansen, I. Balan, S. Stefanski, I. Moerman, and T. Kurner, “Weightedperformance based handover parameter optimization in LTE,” in VTCSpring 2011. IEEE 73rd, Budapest, Hungary, May 2011.

[3] S. Hamalainen, H. Sanneck, and C. Sartori, Eds., LTE Self-organizingNetworks (SON): Network Management Automation for OperationalEfficiency. John Wiley & Sons, 2012.

[4] J. Ramiro and K. Hamied, Eds., Self-Organizing Networks: Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE.John Wiley & Sons, 2011.

[5] M. Gruber, S. Borst, and E. Kuhn, “Stable interaction of self-optimization processes in wireless networks,” in Proceedings of theWorkshop on Planning and Optimization of Wireless CommunicationNetworks at IEEE ICC 2011, Kyoto, Japan, 2011.

[6] X. Gelabert, B. Sayrac, and S. Ben Jemaa, “A performance evaluationframework for control loop interaction in self organizing networks,”in Proceedings of the 22nd annual IEEE International Symposium onPersonal, Indoor and Mobile Radio Communications (PIMRC 2011),Toronto, Canada, 2011.

[7] M. Neuland, T. Kurner, and M. Amirijoo, “Influence of different factorson X-map estimation in LTE,” in VTC Spring 2011. IEEE 73rd, 2011.

[8] H.-F. Geerdes, UMTS Radio Network Planning: Mastering Cell Cou-pling for Capacity Optimization, ser. Advanced Studies Mobile ResearchCenter Bremen. Vieweg+Teubner, 2008.

[9] K. Majewski, U. Turke, H. Xuemin, and B. Bonk, “Analytical cellload assessment in OFDM radio networks,” in Processings of PIMRC,Athens, Greece, 2007.

[10] K. Majewski and M. Koonert, “Conservative cell load approximationfor radio networks with Shannon channels and its application to LTEnetwork planning,” in Proceedings of AICT 2010, Barcelona, 2010.

[11] A. Eisenblatter, U. Turke, and C. Schmelz, “Self-configuration in LTEradio networks: Automatic generation of eNodeB parameters,” in VTCSpring 2011—IWSON, Budapest, Hungary, 2011.

Fig. 2. Snapshot of the simulation: Control panels are on the upper left, some statistical outputs on the lower left. The right-hand side shows an excerpt ofthe scenario with six sectors and several outdoor users (purple circles).

Fig. 3. Load-distribution across the UMTS cells (with a heat color map from blue to red): At the point in time shown, load is moved from the first (left-handside) to the second carrier (right-hand side) only in case of high load at the first carrier. Empty second carrier cells are powered down (gray cell areas).