Self-Optimization Algorithm for Inter-RATCon guration Parameters

Ahmad Awada and Bernhard WegmannNokia Siemens Networks

Munich, GermanyEmails: {ahmad.awada.ext; bernhard.wegmann}

@nsn.com

Ingo VieringNomor Research GmbH

Munich, GermanyEmail: viering@nomor.de

Anja KleinTechnische Universitat Darmstadt

Communications Engineering LabDarmstadt, Germany

Email: a.klein@nt.tu-darmstadt.de

Abstract The increase in demand for wireless data commu-nication has incited mobile operators to deploy new radio accesstechnologies (RATs) overlaying with legacy ones. Flexible usage ofmultiple RATs and trouble-free inter-RAT operation require theoptimization of the con guration parameters of different RATs.One approach to optimize these parameters is to try differentdefault network-wide settings for all cells and apply the best interms of network performance. Another approach is to manuallyoptimize the parameters of the cells having problems with theaid of drive tests and expert knowledge. The major problem withthese two approaches is that they require human interventionand increase operational expenditures. In this paper, a generaland decentralized self-optimizing algorithm for the inter-RATcon guration parameters is proposed. The algorithm optimizesthe con guration parameters on a cell-pair basis and runs only inthe base stations of the newly installed RAT. As a testing use case,the proposed algorithm is applied to optimize the con gurationparameters related to inter-RAT mobility handovers. Simulationresults have shown that the proposed optimization algorithmoutperforms the default network-wide settings and converges toa stable operation point that signi cantly improves the networkperformance.

Index Terms Self-optimizing network, inter-RAT optimizationalgorithm, mobility robustness optimization, inter-RAT handover.

I. INTRODUCTION

The continuing increase in the demand for high speed com-munication services requires mobile operators to deploy newradio access technologies (RATs) overlaying with legacy ones.The coexistence of multiple RATs offers mobile operatorsa powerful means to match network resources to differentapplication requirements and meet users demands [1]. Toexploit this variety of RATs and provide users with the bestquality of service (QoS), the con guration parameters of basestations belonging to different RATs have to be mutuallyoptimized.

A typical approach to con gure the inter-RAT parameters isto determine by experiments the best default network-wide pa-rameter setting in all cells. This approach is simple, however, itdoes not yield the best network performance as most of the oc-curring problems require cell-speci c adaptation [2]. Anotherapproach is to only optimize the con guration parameters ofthe cells where problems are detected. This manual optimiza-tion is expensive as it needs permanent human intervention and

performing drive tests which increase the operational expenses(OPEX) [3]. To reduce OPEX and achieve a better networkperformance, a self-optimizing algorithm for the inter-RATcon guration parameters is needed.

Legacy RATs such as the second generation (2G) or thirdgeneration (3G) are becoming mature, and therefore, any newoptimization functionality is expected to run in the base sta-tions of the newest RAT such as Long Term Evolution (LTE).From that perspective, we propose that the cells of the newestRAT do not only optimize their con guration parameters butalso the parameters of the neighboring cells of other RATs.Keeping the optimization locally restricted is usually suf cientas problems are often concentrated in certain locations. In thiswork, we present a network-wide optimization algorithm thatrenders a simple cell-pair based optimization scalable suchthat cell-pair optimizations run autonomously in a distributedand parallel manner in the whole network. As a testing usecase, we apply the proposed algorithm to optimize mobilityparameters related to inter-RAT handover which refers to the(vertical) handover between LTE and other legacy technologiessuch as 2G or 3G mobile communication system.

The paper is organized as follows. In section II, the inter-RAT optimization problem is described. The proposed opti-mization algorithm is explained in section III and is appliedto inter-RAT mobility robustness optimization (MRO) problemin section IV. In section V, simulation results are shown tocompare the performance of the optimization algorithm withrespect to different network-wide parameters. The paper isthen concluded in section VI.

II. INTER-RAT OPTIMIZATION PROBLEMIn this section, the inter-RAT optimization problem is for-

mulated after stating some de nitions and assumptions.

A. General De nitions and Assumptions1) There exists a cell-pair optimization algorithm that opti-

mizes the inter-RAT con guration parameters of two cellsbelonging to different RATs.

2) A master cell c = 1, . . . , N , where N is the total numberof master cells in the network, is a cell which belongsto the newest RAT and runs the cell-pair optimizationalgorithm in a bi-directional manner. In addition, a master

2011 8th International Symposium on Wireless Communication Systems, Aachen

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Fig. 1. Example of cell-pair relationships between master and slave cells.

cell c knows its intra- and inter-RAT neighboring cellsfrom automatic neighbor relation (ANR).

3) A slave cell i is one of the Ic neighboring cells of a mastercell c which belongs to a legacy RAT. The con gurationparameters of a slave cell i are optimized by a mastercell c.

4) A cell-pair Pi is a 2-tuple that contains the physical cellID (PCI) of a master cell c denoted by MIDc and the PCIof a slave cell i denoted by SIDi, i.e., Pi = (MIDc, SIDi).

5) The inter-RAT performance of a cell-pair Pi is capturedby the key performance indicators (KPIs) collected fromboth master and slave cells which are assumed to beknown at the master cell. The value of each KPI is thetotal number of a speci c network error event experiencedby the user equipments (UEs) during a de ned period oftime. Based on KPIs, the master cell c calculates a user-de ned cost function, i.e.,

Ci = f(KPI1, . . . ,KPIn) (1)with respect to each slave cell i. The de nition of thecost function depends on the investigated optimizationproblem and the priorities of the mobile operator.

The cost function Ci is calculated only if the values of cell-pair KPIs are high enough, e.g., the value of a KPI exceedsa certain acceptance threshold. This is necessary to avoid thereaction on outliers. A master cell can optimize only its cell-pairs having high values of KPIs and which are referred toin the sequel as feasible cell-pairs. A cell-pair which wasoptimized before by a master cell is considered unfeasibleunless its optimized cost function value increases by more thanp%. If the optimized cost function of an unfeasible cell-pairhas increased by more than p%, the master cell reconsiders thelatter cell-pair as feasible and is allowed to optimize it againin order to adapt to the new network changes.

For clarity, Fig. 1 illustrates cell-pair relationships, depictedby a bi-directional arrow, between master and slave cells. Forinstance, a master cell 20 has cell-pair relationships with slavecells 55 and 56, i.e., P1 = (20, 55) and P2 = (20, 56). In thisexample, the cost functions C1 and C2 are 6 and 10 for cell-pairs P1 and P2, respectively. Note that master cell 20 does nothave any cell-pair relationship with the slave cell 57 becausethe values of their cell-pair KPIs are not high enough, i.e.,unfeasible cell-pair.

B. Optimization ProblemThe optimization problem is to minimize Ci for each pair

Pi using an inter-RAT cell-pair based optimization algorithm.Optimizing all cell-pairs of a master cell c jointly is dif cult asit requires complex coordination scheme among master cells.Alternatively, we propose that the master cell optimizes onlyone cell-pair at a time. For instance, master cell 20 can select

rst either P1 or P2 to optimize but not both at the sametime. We refer to this con ict by con ict type I. Anothercon ict occurs if two master cells optimize simultaneouslythe parameters of the same slave cell. For example, mastercells 20 and 21 have cell-pair relationships with the sameslave cell 56 and cannot optimize its con guration parameterssimultaneously. The second con ict is referred to as con icttype II. Other cells in the network may not have any typeof con icts such as master cell 22 in the example. To runindependent cell-pair based optimizations in parallel, the twoaforementioned con icts need to be resolved by the mastercells.

III. INTER-RAT OPTIMIZATION ALGORITHMIn this section, we give rst a network-wide view of the

operation of the optimization algorithm, then we explain itstwo main blocks in more detail.

A. Network-Wide View of the Optimization AlgorithmThe cell-pair based optimization algorithm is switched on

for the rst time in all master cells of the network. For simplic-ity, it is assumed that the various phases of the optimizationalgorithm are running synchronous by the master cells. Eachmaster cell c starts collecting inter-RAT KPI statistics withrespect to each slave cell i within a de ned time intervalTmonitor. Once Tmonitor expires, each master cell c checks thevalues of the collected KPIs for each cell-pair Pi and in casethey are high enough, a cost function Ci is calculated. Beforerunning any cell-pair optimization, each master cell resolvescon ict type I and II. To resolve con ict type I, each mastercell c selects the cell-pair having the highest cost function.This con ict resolution rule gives higher priority for the cell-pair having more problems. In the example of Fig. 1, mastercell 20 selects rst the slave cell 56. Solving con ict type I isrelatively easy as the master cell can decide independently ofother master cells.

However, solving con ict type II is not possible withoutcoordination among master cells and exchange of information.This is because the master cell does not know if there isanother master cell that wants to operate on the same slavecell. Con ict type II is resolved by exchanging informationsuch that each master cell knows the cost function of othercells. Based on this information, the master cell having thehighest cost function runs the cell-pair based optimization.As an example, master cells 20 and 21 would know after asequence of message exchanges that master cell 20 has a costfunction 10 with respect to slave cell 56 which is higher thanthat of cell 21. In this case, master cells 20 and 22 run thecell-pair optimizations and cell 21 monitors again the KPIs.

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Fig. 2. Routine followed by a master cell to resolve con ict type I.

Note that once the con guration parameters of a master cellare optimized with respect to the cell-pair having the highestcost function, they are kept xed when optimizing later theparameters of other slave cells. The time intervals required toresolve con ict type I and II are T1 and T2, respectively. Aftereach T2 period, a new time interval Tmonitor is applied wheresome master cells run cell-pair optimizations and the rest onlymonitor their KPIs.

B. Con ict Type I Resolution RoutineThe routine followed by a master cell to resolve con ict

type I is shown in Fig. 2. After collecting KPIs duringTmonitor, the master cell determines the feasible cell-pairs andcalculates their corresponding cost functions provided that nooptimization is running. If the master cell is already optimizinga cell-pair, it does not have any con ict to resolve and willresume later its cell-pair based optimization. If the mastercell has multiple feasible cell pairs, it selects slave cell icorresponding to SIDi that has the highest cost function Ci.Having resolved con ict type I, the master cells start toexchange cell-pair information for time interval T2 in orderto resolve con ict type II.

C. Con ict Type II Resolution RoutineCon ict type II is resolved using the information exchanged

among the master cells.1) Message Forwarding Routine: The master cell that has

selected a cell-pair having the highest cost function Ci sendsa multicast message (MM), denoted by X, to its neighboringmaster cells to inform them about its selection. The formatof an MM is (MIDc, SIDi, Ci, h, L) where h indicates howmany times the message has been forwarded and L is the listof PCIs of the neighboring master cells to which the messageis sent to. The counter h starts with 1 and is incremented witheach further hop, i.e., with each forwarding of the message tothe neighboring master cells. Thus, h is increased with each

message forwarding until a maximum number Nhop of hopsis reached. The sending of a message to neighboring mastercells lasts a certain time interval Thop.

Each master cell checks constantly its receive queue forany received MM. Once an MM is received, the master cellincrements the number of hops h, appends to L the PCIs ofthe master cells to which the message will be sent to andforwards the MM to its neighboring master cells. An MMis forwarded as long as Nhop is not reached, otherwise it isdiscarded. To avoid unnecessary message repetitions or loopsback to the original sender, two rules are followed duringmessage forwarding: a) each master cell forwards an MM onlyto neighboring cells that are not included in the list L, i.e.,the MM is always forwarded to new neighboring master cells,and b) each master cell checks if there is an MM in its receivequeue that contains the same information as the received one,i.e., the rst three elements (MIDc, SIDi, Ci) of MM are thesame. If there is no such message, it forwards the receivedone according to rule a), otherwise it does not forward.

It may happen that a master cell is running the cell-pairoptimization algorithm and receives an MM having SIDi equalto the one which is being optimized. In this case, the mastercell creates a new MM having Ci = +, h = 1 anddistributes it to the neighboring master cells. Once received byother master cells, they will recognize that there is a mastercell which is operating on the slave cell of interest.

Moreover, a master cell may had already optimized a cell-pair relationship before and receives an MM having SIDi equalto the one which had already been optimized. To prevent anyother master cell from optimizing again the parameters of theslave cell, the master cell creates a new MM with Ci = ,h = 1 and distributes it to the neighboring master cells. Oncereceived by other master cells, they will recognize that theparameters of the slave cell had been optimized before andshould not be changed anymore. In this case, the master cellcan only optimize its con guration parameters assuming theparameters of the slave cell xed. The message forwardingends once timer T2 expires.

2) Resolve Con ict Type II: Once timer T2 expires and themessage forwarding ends, the master cell uses its receivedinformation from other master cells to resolve con ict type IIas shown in Fig. 3. If the master cell didnt send any messageX and is optimizing a cell-pair Pi, it resumes its optimization.If the master cell is not optimizing any cell-pair, it continuesto monitor its KPIs provided that it didnt send any messageX. On the other hand, a master cell that had computed Ci andhad sent a message X has to resolve con ict type II beforerunning any cell-pair optimization. To this end, the master cellchecks if there is a set of MMs in the receive queue denotedby M having the same con icting SIDi as that of X. If M isempty, the master cell starts to apply the cell-pair optimizationalgorithm as there is no con ict. Otherwise, the master cellcompares the cost function Ci of X with those in M. If Ci isthe highest, the master cell applies the cell-pair optimizationalgorithm, otherwise the master cell continues to monitor theKPIs. Note also that the master cell is not allowed to change

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Fig. 3. Routine followed by a master cell to resolve con ict type II.

the parameters of the slave cell if there is an MM in M havinga cost function equal to .

D. Parameterization of T1 and T2The processing time needed to resolve con ict type I is

negligible as the master cell has to only select the slave cellhaving the highest cost function, and therefore, T1 0. Theparameter Nhop should be set high enough to allow an MMto reach master cells which are far away from the sender. Forinstance, Nhop = 5 allows a master cell to inform other cellswhich are 5 hops away about its cell-pair selection. Hence,Nhop determines the sounding range of a master cell whichdiscovers locally its neighborhood. As some master cells haveto send back an MM with Ci = , the timer T2 should belarge enough to allow such messages to arrive to the mastercell of interest. Therefore, T2 2 Nhop Thop. Note that thetime interval Tmonitor necessary to collect the values of the KPIsis much higher than T2, i.e., Tmonitor T2.

IV. APPLICATION OF THE OPTIMIZATION ALGORITHM TOINTER-RAT MRO

In this section, we apply the optimization algorithm to inter-RAT MRO problem as a testing use case.

A. Con guration Parameters of Master and Slave cellsA UE is handed over from a master cell in LTE to another

slave cell in 3G, if its LTE measurement event B2 is trig-gered [4]. The entering condition of event B2 is ful lled whenthe reference signal received power (RSRP) of the master cellis below B2thr1 threshold and the received signal code power(RSCP) of the slave cell is higher than B2thr2 threshold. Viceversa, a UE is handed over from a slave cell in 3G to a mastercell in LTE if its measurement event 3A is triggered [5]. Theentering condition of event 3A is ful lled if the RSCP of theslave cell is below 3Athr1 threshold and the RSRP of the master

cell is higher than 3Athr2 threshold. In this work, a simpletraf c steering approach is used based on LTE availability ina fully covered 3G serving area. This approach increases thenumber of UEs receiving LTE service. To this end, B2thr2 and3Athr1 are set to and + respectively.

In addition to these measurement thresholds, each masteror slave cell has a time-to-trigger (TTT) parameter de nedas a period of time in which a speci c condition for an eventneeds to be met in order to trigger the measurement report [4].The UE sends a measurement report only if the condition ispermanently ful lled for the TTT period.

B. Inter-RAT Related KPIsIn accordance to the KPIs de ned for the intra-LTE case [6],

[7], two categories of KPIs are speci ed in inter-RAT scenario:The rst captures radio link failures (RLFs) and the other theunwanted handovers such as ping-pongs (PPs) which refer toevents where a UE is immediately handed over to anothercell after a successful inter-RAT handover. The KPIs used toevaluate the performance in inter-RAT scenario are describedin what follows.

1) Ping-Pong to Same Cell (PPSC): Immediately after asuccessful inter-RAT handover, the UE is handed overback to the source cell of the former RAT.

2) Ping-Pong to Different Cell (PPDC): Immediately aftera successful inter-RAT handover, the UE is handed overback to the former RAT, however, to a different cell.

3) Short Stay (SS): Immediately after a successful inter-RAThandover, the UE is handed over to another cell in thenew RAT.

4) Too Late Inter-RAT Handover (TLH): An RLF occursin the source cell before the handover is initiated orconcluded and the UE reconnects to a new cell ofdifferent RAT.

5) Too Early Inter-RAT Handover (TEH): An RLF occursshort time after a handover to a new cell of a differentRAT has been completed and the UE reconnects to a cellof the former RAT, i.e., either source or other cell.

6) Handover to Wrong Cell of New RAT (HWC): An RLFoccurs short time after a UE has been successfully handedover to a new cell of a different RAT and the UEreconnects to another cell within the new RAT.

In addition to these KPIs, the coverage of an LTE cellshould be also considered in the optimization. In principle,the coverage of an LTE cell should be extended as far as thecell does not experience any mobility problems, i.e., PPs orRLFs. The coverage of the LTE cell is mainly controlled bythe threshold B2thr1. The lower the threshold B2thr1, the largerthe LTE coverage and vice versa.

C. Cell-Pair Based Optimization AlgorithmThe cost function Ci calculated by a master cell c with

respect to a slave cell i is a user-de ned function of theaforementioned KPIs collected from both master and slavecells. A master cell c calculates a cost function Ci for acell-pair Pi only if the values of its corresponding KPIs are

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Fig. 4. Locations where the inter-RAT mobility problems occur in the network before and after optimization.

high enough. To this end, let THc,i be the total numberof handover attempts, i.e., a handover attempt is either asuccessful handover or a missed TLH, between the master cellc and slave cell i. Moreover, let us denote the total number ofPPs, i.e., sum of PPSC, PPDC and SS, and RLFs, i.e., sumof TLH, TEH and HWC, collected from both master cell cand slave cell i by NPPc,i and NRLFc,i , respectively. We de nethe percentage of cell-pair ping-pongs P PPc,i and RLFs P RLFc,ibetween a master cell c and slave cell i as

P PPc,i =NPPc,i

THc,i 100%, and (2)

P RLFc,i =NRLFc,i

THc,i 100%. (3)

In this work, a cell-pair Pi is considered feasible if eitherP PPc,i > 5% or P

RLFc,i > 2% or coverage of the LTE master cell

cell is not fully exploited. The cell-pair optimization algorithmused by a master cell to minimize Ci is based on a multi-dimensional descent gradient method where each con gurationparameter is adjusted in the direction of improvement until thealgorithm converges to a local minimum.

V. SIMULATION RESULTSIn this section, the optimization algorithm is compared with

respect to different default network-wide settings.

A. Network Layout and Simulation ParametersA typical irregular network layout for partly overlaying

inter-RAT deployment is used, see Fig. 4(a), namely an urbanarea with an adjoining suburban area. The complete area(urban and suburban areas) is served by 3G technology (lightgray), while LTE covers only the urban area (dark gray). Thetotal number of cells is 72 among which 45 are slave cellsand N = 27 master cells.

The simulation parameters are the same as those used in [7].The total number of UEs in the network is assumed to be 720.Each UE is connected to a single RAT and has a constant datarate requirement equal to 512 kbps [8]. Half of the UEs movewith a speed of 70 km/h on a street located at the end of LTEcoverage border, i.e., black solid line in Fig. 4(a), and the rest

moves in the network randomly at a speed of 30 km/h. TheUEs located in the street are wrapped to the beginning of thestreet when they reach its end. For the optimization algorithm,the percentage p, Tmonitor, Thop, Nhop and T2 are set to 10%,250 s, 20 ms, 8 and 320 ms, respectively.

B. Performance EvaluationThe percentage of each mobility error event described in

subsection IV-B is de ned as the ratio between the sum ofthe values of the corresponding KPIs collected by all mastercells and the sum of all handover attempts in the network.The percentages of the six different mobility error events areshown in Fig. 5 for four different default (B2thr1, 3Athr2) valuesassuming TTT = 0.2 s. According to the gure, it can be seenthat the lower the B2thr1 threshold, the more the percentage ofRLF events. In contrast, a high value of B2thr1 decreases thepercentage of RLF events, however, it shrinks the coverageof LTE cells. Among the default con gurations, the setting(B2thr1, 3Athr2) = (119,116) dBm resolves completely theRLFs at the expense of an increase in the percentage of PPSCand reduction in LTE coverage. On the other hand, the setting(B2thr1, 3Athr2) = (130,130) dBm yields the largest LTEcoverage at the expense of an increase in the percentage ofRLF events.

The inter-RAT con guration parameters are optimized start-ing from the two aforementioned network-wide settings forall cells: 1) (B2thr1, 3Athr2) = (130,130) dBm and2) (B2thr1, 3Athr2) = (119,116) dBm. The performanceof these two network-wide settings is compared with theiroptimized counterparts in Fig. 6. The gains achieved by theoptimization algorithm are shown in Fig. 6(a). For the rstsetting, the percentage of PPSC, PPDC, TLH, TEH and HWCevaluated prior the optimization, i.e., shown in red, is reducedby 53%, 30%, 98%, 95% and 98%, respectively after runningthe MRO algorithm, i.e., shown in blue. For the second setting,the percentage of PPSC, shown in magenta, is drasticallydecreased from 25% to 2% when compared to its optimizedcounter-part shown in green.

To check the coverage of LTE cells, the subset of B2thr1values that are changed during optimization is shown in

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(a) The percentages of mobility error events in the network before andafter optimization: The parameters are optimized starting from two differentnetwork-wide settings.

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MRO, Initial: (B2thr1, 3Athr2) = ( 119, 116) dBm

(b) B2thr1 values that are changed during optimization.

Fig. 6. Network performance before and after optimization.

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Fig. 5. The percentages of mobility error events in the network for differentnetwork-wide (B2thr1, 3Athr2) values assuming TTT = 0.2 s.

Fig. 6(b). For the rst setting (B2thr1, 3Athr2) = (130,130)dBm, it can be noticed that the reduction in the percentages ofmobility error events cannot be achieved without sacri cingLTE coverage. According to Fig. 6(b), 6 master cells haveincreased their B2thr1 thresholds during the optimization, i.e.,difference between red and blue values, and consequently theircoverage is reduced. This decrease in LTE coverage is justi edbecause resolving the mobility problems captured by the KPIshas relatively a higher priority than LTE coverage as it directlyaffects the user experience. In contrast, for the second setting,master cells 14, 17, 26 and 27 have decreased their B2thr1thresholds, i.e., difference between magenta and green values,and consequently, their coverage is increased.

To visualize the gains achieved by the optimization algo-rithm on a network-wide level, the locations of the inter-RAT mobility problems are shown in Fig. 4 before and afterthe optimization assuming (B2thr1, 3Athr2) = (130,130)dBm and TTT = 0.2 s as initial setting. Before using theoptimization algorithm, a high number of ping-pongs occursat the border of LTE coverage areas whereas most of RLFsare concentrated around the upper part of the street. Afteroptimization, it can be noticed that the majority of RLFslocated at the street is resolved as well as the number of ping-pongs in the network is signi cantly decreased.

VI. CONCLUSIONIn this paper, we have proposed a self-optimizing algorithm

for inter-RAT con guration parameters. The algorithm is im-plemented in the base stations of the newly installed RAT only.The main advantage of the proposed distributed algorithm isthat simple cell-pair based optimizations are applied simulta-neously for decoupled cell-pairs without additional effort inthe legacy network. The proposed optimization algorithm cancon gure the inter-RAT parameters of a large number of cellswithout complexity increase as each master cell discovers onlyits local neighborhood. As a testing use case, the optimizationalgorithm has been applied to optimize the inter-RAT relatedmobility parameters. Results have shown that the proposedoptimization algorithm has succeeded in reducing the valuesof KPIs, reaching a stable optimized operation point with cell-speci c parameter settings and improving signi cantly the UEmobility performance.

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