Comparing Online and Offline SON Solutions forConcurrent Capacity and Coverage Optimization

Sascha Berger, Albrecht Fehske, Paolo Zanier, Ingo Viering, and Gerhard FettweisVodafone Chair Mobile Communications Systems, Dresden, Germany

{Sascha.Berger, Albrecht.Fehske, Fettweis}@tu-dresden.deNokia Solutions and Networks GmbH, Munich, Germany

{Paolo.Zanier}@nsn.comNomor Research GmbH, Munich, Germany

{Viering}@nomor.comAbstractSelf-organizing networks (SONs) can carry out their

optimization procedures in an on- or off-line manner. On onehand, an online SON solution optimizes network parametersduring operation. On the other hand, an offline SON solutionemploys a simulation environment of the network to be optimizedin order to perform an offline parameter optimization beforeapplying changes to the network. Thus far, researchers havenot yet compared the characteristics of on- and offline SONsolutions and they typically do not comment their SON solutionsoperational mode. However, specifying the SONs operationalmode is crucial because it determines the type and numberof measurements to be performed, and it decides whether it isrequired to accurately model the network to be optimized ornot. In this work, we compare the general properties of on-and off-line SON solutions qualitatively and compare an on-and an off-line algorithm for coverage and capacity optimizationquantitatively, using a realistic LTE simulation scenario. Based onthe results obtained, we can conclude that offline SON solutionsshould be preferred as long as the required inputs are available.However, online SON solutions provide an adequate alternative tooffline SON solutions if some of the inputs required are missing.

Index TermsSelf-Organizing Networks, Online Optimization,Offline Optimization, Coverage and Capacity Optimization.

I. INTRODUCTION

Self-organizing networks (SONs) are dedicated to adjust-ing the networks control parameters, such as antenna tilts,transmit powers, and cell individual offsets, automatically inorder to maximize the quality of service (QoS) provided to thecustomer, while minimizing capital and operational expenses.For the sake of maximizing the networks QoS, researcherstypically employ sophisticated optimization methods (see e.g.,[1], [2]), rule-based algorithms (see e.g., [3], [4]), or learningapproaches (see e.g., [5], [6]). An aspect common to all theseapproaches is that, they either have to operate on- or off-line.In online SON, the QoS optimization procedure is supposedto operate during network operation, i.e., all parameter mod-ifications are applied directly to the network, the networksresponses to the algorithms actions are measured and aretaken into account for future parameter adjustments. SONalgorithms operating off-line follow a different concept: first,the QoS optimization is carried out in a simulation using anaccurate modelling environment imitating the network to beoptimized. Thereafter, the parameter setting, which turns out

Optimization Method

Network

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tting

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KPIs

UserLocation &ReceivePower Info.

Fig. 1. The principle of on- and off-line SON solutions. Red colour is usedto describe an online solution, adding the black coloured drawings leads tothe principle of off-line SON solutions.

to be optimal in the simulation, is applied to the network.While researchers have already examined the characteris-tics of various SON architectures (distributed, centralized,or hybrid; see e.g., [7]), they have not yet addressed thecharacteristics of on- and off-line operation and have notcompared these operation modes. As a result a majority ofSON solutions proposed do not provide statements about theoperational mode (on- or off-line) that is considered. However,the mode of operation is crucial since it (i) determines the typeand number of measurements to be performed and (ii) decideswhether it is required to model the network to be optimizedaccurately or not. In this work, we clearly characterize andcompare the general properties of on- and off-line SON solu-tions in a qualitative manner (Sections II and III). Moreover,we carry out a quantitative comparison of both approachesby comparing an on- and an off-line SON algorithm in ajoint capacity and coverage optimization (CCO) use case(Section IV). The scenario under investigation is a dense urbanEuropean city with real LTE site locations and ray-tracingpath loss data. To solve the CCO problem and compare both

978-1-4799-4449-1/14/$31.00 2014 IEEE

approaches, we apply the online algorithm proposed in [8]and develop an off-line algorithm which applies a popular andpowerful optimization technique simulated annealing.

II. ONLINE SON SOLUTIONThe principle of an online SON solution is depicted in Fig.

1 using black colour and concurs with the SON feedback loopintroduced in [7]. At first, a SON operating online determinesthe current values of the key performance indicators (KPIs)of interest by either measuring them directly, or by estimatingthem via measurements on other metrics. In the next steps,the SON functionality collects the KPI values of interest,computes new parameter settings, and applies them to thenetwork. The decision to select a new parameter setting isbased on the present and past KPI values, and is typicallydone by means of (i) rule-based procedures (see e.g., [3],[4]), (ii) learning techniques, such as reinforcement learning(see e.g., [5], [6]), or (iii) direct search methods, such as acoordinate descent search (see e.g., [9]). After applying thenew parameter setting to the network, the procedure startsagain with measuring or estimating new KPI values.The main advantage of an online operation is that, no sim-ulation models of the network to be optimized are required,since all parameter modifications are applied to the networkand their impact is measured and not simulated. Thus, nomodelling error reduces the algorithms performance, whichis also advantageous. Another advantage is that, online SONsolutions can, in principle, operate without the knowledge ofuser locations and received signal strengths. Of course, onlineSON algorithms can also employ such knowledge, e.g., in anrule-based approach considering the user locations. However,since an online SON solution does not require an accuratesimulation environment, the SON can in principle optimize thenetwork without this information. For example, the learningapproaches [5], [6], and the algorithm from [8] which we willemploy in this work refrain from using such advanced systemknowledge.The main disadvantage of an online SON solution is that theoptimization procedure can only search around the currentparameter setting locally. This restriction is derived from theassumption that the amount of change in the networks QoS(and in the KPI values) is proportional to the magnitudeof change in the parameter setting, i.e., we expect largeparameter modifications to cause a large increase or decreaseof the networks QoS and vice versa. Since the operatorwants to avoid very unfavourable parameter settings, onlineSON solutions must avoid large parameter changes sincethey might lead to a very bad QoS. The need to avoidvery bad parameter settings coupled with a restriction tosmall parameter modifications limits the ability to avoid localoptima. Another disadvantage of an online SON solution isthat the optimization procedure takes a rather long time, sinceevery guess for a better parameter setting is applied to thenetwork. Before an online SON algorithm can propose thenext parameter setting, measurements of the relevant KPIsare required. However, depending on the type of KPI, such

measurements can range from hours to days. For example,the network coverage may be estimated based on statisticson failed connection set-ups and call drops. Obtaining areliable statistic for these metrics requires many measurementinstances, and therefore, a longer period of time. One moredisadvantage of an online SON operation is that, the networksQoS may decrease for certain time periods during the opti-mization if unfavourable parameter settings are proposed bythe search algorithm. Depending on the optimization procedureemployed, an online SON algorithm may be forced to carry outsmall but random parameter modifications which, of course,can be disadvantageous for the networks QoS.

III. OFFLINE SON SOLUTION

The structure of an offline SON solution is depicted usingblack and red colours in Fig. 1. The offline SON also measuresor estimates the KPIs to be optimized in order to monitor thenetwork. However, an offline SON solution additionally usesan accurate simulation environment modelling the network andits KPIs to be optimized. When the KPIs measured indicatethe need for parameter optimization, the SON triggers a searchfor a better parameter setting using its inherent simulationenvironment. Often, sophisticated optimization techniques likesimulated annealing are employed to compute new parametersettings which are not applied to the network, but whoseimpact is simulated using the simulation environment. Wecall this simulated optimization an offline optimization. Theparameter setting which is found to be optimal in the offlineoptimization is then applied to the (real) network. Thereafter,the KPIs are measured or estimated once again. If the newparameter setting worsens the KPIs to be optimized, thesimulation environment requires troubleshooting. Examplesfor offline SON solutions are [2], [10].The most important advantage of offline SON solutions is thatsophisticated optimization tools can be applied. For example,methods like simulated annealing or genetic algorithms maybe applied. Main advantage of such algorithms is that, theytest an enormous number of parameter settings and search thecomplete optimization space and not just locally around thecurrent parameter setting. Such complex methods can onlybe applied in an offline SON solution, since the optimizationsteps are just simulated and do not take place in the networkitself. Due to a high inherent randomness in the optimizationmethods used, such an offline approach is capable of escapingfrom local optima.A drawback of this approach is that, an accurate simulationenvironment typically requires detailed knowledge of userlocations and their received signal strength for all sectors con-sidered, and for all possible tilt values (in case of tilt optimiza-tion). Both traffic demand and received signal strength can, inprinciple, either be measured or modelled. While modelling isusually not accurate enough, measuring these metrics is oftennot easy since it requires specific monitoring tools either onthe network or terminal side. Moreover, the general propertiesof the network such as site locations, heights, etc. must be

On-line Off-lineAdvantages

very low requirements for operation, because sophisticated search methods can be employed:- no simulation environment required - many different parameter settings can be tested- no knowledge on user locations & received signal strengths required - high capability to overcome local optima

Disadvantages limited capability to overcome local optima an accurate simulation environment requires knowledge long optimization duration - on users received signal strengths sectors and tilts

- on user locations, site locations, etc.Unfavourable parameter settings may be applied due to

inherent random decisions of the optimization method applied a huge mismatch between simulation and realityTABLE I

SUMMARY OF GENERAL ADVANTAGES AND DISADVANTAGES OF ON- AND OFF-LINE SON SOLUTIONS.

0 500 1000 1500 20000

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y[m]

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150 100 50

Fig. 2. Map of best received signal strength for the simulation scenario understudy. The points indicate site locations and the arrows denote the radiationdirection of the sites sectors. Sites denoted by a white point are consideredfor the optimization. The remaining sites serve as interferers. The area shownis the target area TA.

known. While having this general information available is notan issue in macro networks which change slowly, it can bevery challenging in rapidly changing small cell deployments.It should also be pointed out that, the performance of offlinesolutions depend on the accuracy of all the inputs mentionedabove. Inaccuracies will lead to a mismatch between realityand the model, which we call model mismatch. We expectthat a high model mismatch will decrease the algorithmsperformance.Please note that, the operational mode (on- or off-line op-eration) does not restrict the SON solution to a particulararchitecture (distributed, hybrid or centralized). The main ad-vantages and disadvantages of on- and off-line SON solutionsare summarized in Table I.

IV. CASE STUDY: TILT-BASED CONCURRENT CAPACITYAND COVERAGE OPTIMIZATION

After elaborating on qualitative advantages and disadvan-tages of on- and off-line SON solutions, we perform a casestudy in order to compare an on- and an off-line SONalgorithm quantitatively by examining a realistic CCO usecase.

A. Scenario and MetricsWe aim to modify the networks antenna tilts in order to

jointly optimize the downlink capacity and coverage in aEuropean city, which is modelled using real 26GHz LTEsite locations and ray-tracing path loss data. The simulationscenario is depicted in Fig. 2. Each sector considered foroptimization can adjust its antenna tilt from 4 to 14 with 1 step size. We consider an indoor penetration lossof 10 dB + 06 m. Since the performance of the SONalgorithms depends strongly on the traffic demand distribution,we investigate 64 different traffic demand scenarios. Eachartificially created traffic demand scenario is characterizedby uniform traffic demand of 60 userskm2 adding one or twotraffic demand hot spots (HSs) of maximum size 00165 km2with an user density 130 times larger than the surroundingarea. The traffic demand HSs are located at feasible locationssuch as plazas at city centres and their corresponding area isdenoted as HS. We assume each traffic demand scenario tobe equiprobable. We only adjust the antenna tilts of the sitesdepicted by a white dot in Fig. 2 in order to avoid edge effects.The initial tilt settings are the same as in [8], i.e., they areobtained using a simple reference algorithm which considersonly coverage and interference. The remaining simulationparameters are presented in Table II. Please note that we usethe same system model as in [9].We tackle the capacity optimization by focusing on

low-end users, i.e., the algorithms aim to optimize the5th percentile of the user throughput in the target area(TA) TA, denoted as 5TA . The TA TA is shownin Fig. 2. The coverage in the TA TA is denoted asTA . We also use the area covered by a certain cell = { TA| = argmaxrx() rx rx,min} where denotes the cell index, rx() and rx,min denote thereceived signal strength from sector at location and

the minimum received signal strength required, respectively.Additional details can be found in [9].

B. Online Algorithm

In this work, we employ the online CCO algorithm whichhas been proposed in [9] and further improved in [8], becausethe algorithm has been shown to be very effective. The majorfeatures of the online CCO algorithm from [8] are capturedby the pseudo-code presented in Algorithm 1. Here is how thepseudo-code in Algorithm 1 can be understood.1) Line 1: The throughput statistic measured by each cell

is a probability density function (PDF) of all user data ratesobserved. The coverage statistics measured are estimates ofcovered and uncovered users. The coverage information refersto the users rather than to an area because the algorithmassumes that location information is unavailable.2) Line 2: The main idea of the online CCO algorithm is

to merge the concurrent optimization of coverage and capacityinto a single optimization by developing cell-wise cost func-tions for both capacity and coverage. The cost functions unit-less values indicate by how much the operators optimizationgoal, for a specific KPI, is overshot. Each optimization goalis represented by a threshold which needs to be overcome.The cost function monotonically increases when the KPI fallsbelow the desired threshold and the cost function is zerofor all KPI values above the threshold. In this work, weuse the same cost functions as in [9], i.e., the thresholdsfor coverage and throughput percentile are set to 99% and400 kbps, respectively. Following this line of thought, thealgorithm can obtain a single cell-wise cost value accountingfor both coverage and capacity issues using

= () + (5) (1)where and represent the capacity and coverage costfunction, respectively. , , and 5 represent the cost,the coverage, and the throughput percentile pertinent to cell ,respectively.3) Line 4: By summing up coverage and throughput statis-

tics from all cells in the cluster C, the algorithm can obtain thecoverage C and throughput percentile 5C for the clusterarea. Replacing the cell-wise metrics in Eq. 1 with the cluster-wise metrics defines the cluster cost C .For more details, please refer to [8] and [9].

Simulation ParametersCarrier Frequency 26GHzBandwidth 10MHzBS Transmit Power 29 dBm

PRBBS Antenna Gain 14 dBiUE Min. Receive Power 120 dBm

PRBUE Height 15mPath Loss Ray TracingThermal Noise 121 dBm

PRBSpatial Resolution 5 5m

TABLE IICONSIDERED SIMULATION PARAMETERS

FSA ParameterInitial Temperature 40 (80)Re-annealing Interval 80 (100)Temperature Update 095iterationTime Limit per Scenario 36 h

TABLE IIIFSA SIMULATION PARAMETERS.

C. Offline Algorithm

The offline SON solution considered in this work makesuse of the same cost functions as the online SON solutionand is summarized by the pseudo-code in Algorithm 2. Thepseudo-code can be understood as follows.1) Line 3: Assuming that the inputs listed are available,

the simulation environment is capable to compute the datathroughput and coverage for each user. Hence, we can obtaincoverage and throughput information for the TA TA to beoptimized.2) Line 4: In this work, we use fast simulated annealing

(FSA) to minimize the cost function TA [11]. FSA is an ex-tension of the well known simulated annealing (SA) technique,enabling global optimization by lower computational effortthan the conventional SA approach. SA is a random-searchtechnique mimicking the way in which a metal cools andfreezes to a minimum energy structure (the annealing process)and is already widely used in the field of SONs (see e.g., [10],[12]). At each step of the SA algorithm, the current solution iscompared with a random neighbour solution. The new solutionis accepted for sure if it is better than the old one. However,the new solution is also accepted with a certain probability, ifthe new solution is worse than the old solution. The probabilityof acceptance decreases with the difference between new andold solution, depicted as , and a global parameter calledtemperature according to . Enabling steps towardsworse solutions empowers SA to overcome local optima, andtherefore, SA is suited for global optimization. Also, SA isversatile since it does not rely on any restrictive propertiesof the objective function. However, SA is not suitable foronline optimization since parameter settings which cause anunacceptably low QoS may be tested.First, we obtained proper parameters for the FSA by sweeping

Algorithm 1 The online SON algorithm proposed in [8].Input: Neighbour relationships, cost functions.1: sectors considered: measure throughput and coveragestatistics and communicate them to all other sectors con-sidered.

2: sectors considered: evaluate local cost for allsectors considered.

3: sector having the highest cost : Set up optimizationcluster C consisting of its own and neighbouring cells.

4: C: Find the set of tilt values minimizing the clustercost C using a coordinate descent method.

5: jump to line 1.

through the parameters coarsely and comparing the algorithmsperformance. Thereafter, we applied the FSA procedure withtwo different parameter configurations, which are presented inTable III, in Algorithm 2 for all 64 different traffic demandscenarios.

D. ResultsIn order to capture the results for all 64 traffic demand

scenarios graphically, we use box plots. The red lines in thefollowing box plots denote the median over all simulationsand the blue box shows the area where the mid 50% of theresults are located. The antennas (whiskers) extend to the mostextreme data value which is not considered an outlier. Outliersare depicted by red crosses.In Fig. 3 and 4, we report the relative change of the throughputpercentile in the TA TA and HS area HS, respectively.We obtain a relative measure by normalizing the throughputpercentiles after optimization by the value before optimization(at the reference tilt setting). We denote the normalized valuesas Q5TA and Q5HS for the throughput percentile in the TA TAand HS area HS, respectively. It is clearly visible that, theoffline algorithm outperforms the online algorithm. The medialgains in the TA are 60% and 101% for the online and offlinealgorithm, respectively. The median of the TA throughputpercentile before optimization is 59 kbps. In the median, theonline algorithm can increase the throughput percentile in theHS area by 80% while the offline algorithm can generate 90%gains based on a medial HS area throughput percentile of49 kbps before optimization. The absolute change in the TAcoverage before optimization (TA) and after optimization(TA) is presented in Fig. 5. The online algorithm canincrease the coverage in the median by about 01% while theoffline algorithm could generate a medial increase of approx.1%. The initial medial coverage is approx. 98%.

E. DiscussionThe performance benefit of the offline solution compared

to the online solution is due to the superiority of the FSA

Algorithm 2 Offline SON algorithm for tilt-based CCO.Input: map of received signal strengths for all cells and tilts,

user locations, simulation environment, cost functions.1: In real network: Monitor local cost of all sectors tobe optimized.

2: if, for at least one : 0 then3: In simulation environment: compute TA and 5TA .

Compute the TA cost TA by replacing the cell-wisemetrics in Eq. 1 with the TA metrics.

4: In simulation environment: use fast simulated annealingoptimization to obtain a tilt setting minimizing the TAcost TA .

5: else if then6: Jump to line 1.7: end if8: In real network: Apply tilt setting obtained in line 4.

1

2

3

On-line O-line

Q5 RTA

Fig. 3. Box plot presenting the on- and off-line algorithms normalized gainin the TA throughput percentile.

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Q5 RHS

Fig. 4. Box plot presenting the on- and off-line algorithms normalized gainin the HS area throughput percentile.

procedure over the coordinate descent search. The coordinatedescent search employed in this work has a very limited abilityto overcome local optima, which causes the lower relativegains compared to the FSA approach. However, the offlinesolution requires knowledge of user locations, signal strengths,and an adequate simulation environment which is not requiredfor the online solution. We also did not consider a modelmismatch between reality and simulation while investigatingthe offline algorithms performance. We expect that a modelmismatch will, on average, decrease the performance dueto faulty computation of the networks KPIs. Furthermore,we would like to point out that the FSA algorithm entailshigh computational complexity compared to the coordinatedescent approach: on average the FSA algorithm tested approx.3200 different tilt settings, while the coordinate descent searchreached its final tilt setting on average after 50 trials. However,these 50 trials, for better tilt settings, are applied in the livenetwork. Assuming a temporal granularity of about one day

0

On-line O-line

Copt

RTAC

ini

RTA[%

]

Fig. 5. Box plot presenting the on- and off-line algorithms absolute gain inthe TA coverage.

for the online algorithm, the online tilt optimization would (onaverage) take 50 days, while the offline computation, in ourcase, did not take longer than 36 hours.

V. CONCLUSIONIn this work, we compared qualitative properties of on- and

off-line SON solutions and examined a real-world CCO usecase in order to quantitatively compare an on- and an off-lineSON solution. Based on both the qualitative and quantitativeresults at hand, we recommend to use an offline SON solutionwhenever the required input knowledge is available. Neverthe-less, we can also conclude that online SON solutions providean adequate alternative if accurate data on user locations andsignal strengths is missing, e.g., due to technical or financialreasons.

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[5] M. Naseer ul Islam and A. Mitschele-Thiel, Cooperative fuzzy q-learning for self-organized coverage and capacity optimization, in 2012IEEE 23rd International Symposium on Personal Indoor and MobileRadio Communications (PIMRC), Sept 2012, pp. 14061411.

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[8] S. Berger, A. Fehske, P. Zanier et al., Online Antenna Tilt-BasedCapacity and Coverage Optimization, accepted for publication inWireless Communications Letters, 2014.

[9] S. Berger, M. Soszka, A. Fehske, P. Zanier, I. Viering, and G. Fettweis,Joint throughput and coverage optimization under sparse system knowl-edge in lte-a networks, in International Conference on ICT Convergence(ICTC), Oct 2013, pp. 105111.

[10] G. Koudouridis, H. Gao, and P. Legg, A centralised approach topower on-off optimisation for heterogeneous networks, in VehicularTechnology Conference (VTC Fall), Sept 2012, pp. 15.

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[12] D. Karvounas, P. Vlacheas, A. Georgakopoulos, K. Tsagkaris,V. Stavroulaki, and P. Demestichas, An opportunistic approach forcoverage and capacity optimization in self-organizing networks, inFuture Network and Mobile Summit (FutureNetworkSummit), July 2013,pp. 110.

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