Event-based Performance Monitoring for Inter-System ... - IEEE Xplore

Event-based Performance Monitoring for Inter-System Cell Reselection: A SON enabler Icaro da Silva and Yu Wang

Faris Mismar and William Su

Ericsson Research, Wireless Access Network Stockholm, Sweden Email: {icaro.leonardo.da.silva, yu.a.wang}@ericsson.com

Ericsson Inc, Business Unit Global Services Texas, USA Email: {faris.mismar, william.xx.su}@ericsson.com

mainly counters, collected at the operation and support system (OSS) to compute key performance indicators (KPI) [3]. OSS counters consider all users, taking into account their real experience in terms of radio conditions, indoor losses and traffic distribution. However, counters aggregate the statistics such a way that the 3G/2G camping problem is not easily identified. Another challenge inherent from this problem is the need of a combination of KPIs from 3G and 2G networks [4]. In this paper we propose a methodology for post processing performance monitoring (PM) events in order to identify the 3G/2G camping problem. The aim is to support selfoptimization algorithms using network observables, thereby eliminating the need for drive testing [5]. PM events are recordings of the detailed message flow for a selected number of protocols in different interfaces (e.g. Iub, Iu, etc). They can be collected by means of hardware probes or embedded software [5], [6], or via the OSS of some network vendors. PM events have been collected from a 3G/2G network of an European operator to validate the methodology and define event-based KPIs to analyze the 3G/2G camping problem. In addition to the analysis, we propose a self-optimization algorithm which uses the post processed information as input, showing that the proposed PM framework can be used as a SON enabler [5]. The paper is organized as follows. In Section II we present the IS-CR procedure and the available observability via counters and PM events. In Section III we show how we process PM events. In Section IV we define event-based KPIs. Therein, a large set of results from live networks are provided. In Section V we show the proposed self-optimization algorithm relying on event-based KPIs.

Abstract—It has been observed that in overlaid 3G/2G networks certain user equipments (UE) may stay camped on 2G for long periods despite the fact there is available 3G coverage. In this paper we present a framework to enable network operators to identify these occurences, not easily recognized using counterbased observability. The proposed methodology relies on the post processing of performance monitoring (PM) events from the radio access and core networks, feature provided by most major network vendors. Based on that, new key performance indicators (KPI) are defined e.g. the time UEs camp on 2G. Then, we show how these event-based KPIs may be used as inputs to self-optimization algorithms, functioning as a self-organizing networks (SON) enabler. Results are shown using data from live 3G/2G networks. Keywords: performance monitoring; inter-system cell reselection; PM events; self-organizing networks; self-optimization.

I. I NTRODUCTION In 3G, a user equipment (UE) in idle mode (i.e., with no radio connection with the network) is said to be camped on a cell when it is ready to establish a radio connection through that cell within a reasonable time. While the UEs are camped, they shall search (as dictated by the radio conditions) for a better cell to camp on via cell reselection. In order to have access even when the UE is out of 3G coverage, intersystem cell reselection (IS-CR) is also enabled by including 2G cell information in the 3G system information blocks (SIB) broadcasted to the UEs. The UE shall perform IS-CR based on cell level radio parameters read from the 3G SIB or 2G system information (SI) when it is camped on either systems [1]. One possible strategy for operators with 3G/2G overlaid networks is keeping users in 3G as long as possible to enjoy higher data rates and using 2G only when 3G coverage fades out. Cell level parameters are usually tuned to fulfill this strategy however, in certain circumstances, it may happen that users still complain about being served by the 2G in areas where 3G coverage should be available. We call this the 3G/2G camping problem. Previous works in the literature addressed IS-CR optimization but none of them have considered this problem. In [2] the authors propose to optimize the tradeoff between the 3G coverage in idle mode vs the service accessability. Measurements were obtained from drive testing, which is a costly solution to be scalable to a whole live network. A cost effective alternative to drive testing is the use of statistics,

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II. I NTER -S YSTEM C ELL R ESELECTION UEs camped in a 3G or 2G cell may reselect to an intrafrequency, inter-frequency, or inter-system cell; and, during this procedure there is no signaling with the network nodes. However, when the new cell, which the UE is camping on, belongs to a different routing area compared to the previous cell the UE must update its location to the SGSN via a routing area update (RAU) [1]. Operators usually group 2G and 3G cells in different routing areas. In this case, a RAU message is always sent to the 2G-SGSN when a IS-CR is performed from 3G to 2G and,

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similarly, a RAU message is sent to the 3G-SGSN when IS-CR is performed from 2G to 3G.

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A. Counter-based observability

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Counters may be triggered by signaling messages received or sent by the nodes. In order to send RAU messages to the SGSN, radio resource control (RRC) connections must be established. Counters triggered on both RATs by RRC Connection Requests with cause values IRAT Cell Reselection are typically used to measure the number of IS-CR occurences. Despite this possibility, a KPI based on these counters is not enough to characterize the IS-CR performance since it is still not possible to identify i) IS-CR ping-pongs i.e., a user performing IS-CR from 3G to 2G and imediately coming back to 3G and ii) the 3G/2G camping problem described earlier.

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As defined earlier, PM events contain information from the different network interfaces (Iub, Iu, etc) and internal procedures at the radio network controller (RNC) or at the radio base station (RBS) [5]. These recordings provide better granularity compared to counters such as the time stamp to indicate when the procedure occured, UE identities, UE location in different levels (radio access and core networks) and other specific information associated to each procedure. Due to the larger amount of data generated, a performance management system relying on PM events would need larger servers to operate in large scale [6].

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III. P OST PROCESSING OF PM EVENTS In order to analyze the 3G/2G camping problem we have used the PM event associated to RAU messages. These events contain the following parameters: ∙ Time stamp; ∙ Mobile subscriber identities; ∙ Radio access technology: 3G or 2G; ∙ Triggering reason: intra/inter-frequency or IS-CR; ∙ Old and new routing area identities; RAU events are stored in a performance monitoring database (PM-DB) for each time a RAU is sent to the 3G-SGSN or the 2G-SGSN as depicted in Fig. 1. In our framework, RAU events are post processed by correlating two consecutive events from the same UEs (using the mobile subscribers identities) triggered by IS-CR with respectively 2G and 3G as radio access technologies. RAUs are also triggered either in 3G or 2G by handovers (HO) and cell changes (CC) when the UE is in connected mode. Then, the RAU events associated to IS-CR are filtered by analyzing the existence of HO/CC preparation and execution events before the RAU. In the absence of them, RAU is considered to be associated to IS-CR. A similar approach is used in the other direction but using 2G-SGSN events instead. The result of this correlation is what we call a correlated IS-CR event, depicted in Fig. 2. The key measurement provided by this event is the 2G camping time after leaving the 3G coverage denoted by 𝑇𝑢,2𝑔,3𝑔

Correlated IS-CR event

The correlated IS-CR events also contain location parameters such as the 2G incoming cell. However, the 3G cell the user was camping on before an IS-CR to 2G is unknown. An alternative to get this information may be to find out the 3G cell where the user had its last activity by tracking its closest RRC message. The accuracy is limited, since intrafrequency cell reselections within the same routing area could have been performed. Based on the new correlated events, ISCR statistics can also be aggregated per UE model by mapping the subscriber identities to the international mobile equipment identity (IMEI) number [7]. IV. E VENT- BASED KPI S In this Section we define new KPIs relying on the parameter 𝑇𝑢,2𝑔,3𝑔 from the correlated IS-CR event. A small 𝑇𝑢,2𝑔,3𝑔 is a strong indicator of unnecessary IS-CR or ping-pongs. On the other hand, large 𝑇𝑢,2𝑔,3𝑔 can be a result of either lack of 3G coverage or non-optimal parameter settings. Based on the parameter 𝑇𝑢,2𝑔,3𝑔 , a number of KPIs are defined to analyze the 2G/3G camping problem and to assist parameter tuning. Results from a case study are shown as examples of the proposed KPIs. The case study data is from a live 2G/3G overlay network in a European city with urban and sub-urban environments. Three days data was collected and analyzed. In order to collect the PM events, we have used general

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performance event handler (GPEH) and SGSN event-based monitoring (EBM) system which are respectively embebed recording tools in the Ericsson RNC and SGSN. The events were collected at the OSS and parsed into a PM-DB in a server at the performance management system. In this Section, W2G stands for a transition from 3G to 2G and G2W stands for a transition from 2G to 3G. KPIs are defined in different aggregation levels: overall network, UE level and cell levels.

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) ( 1) Total 2G Camping Time per UE per day 𝑇𝑢𝑑𝑎𝑦 : is calculated by summing up the 2G camping time of each UE. It is a direct measure to address the 2G/3G camping problem. UEs with a large value have high probability to start a service in 2G or hesitate to start a data service in 2G which means the KPI is a good indicator of end-user experience. 𝑇𝑢𝑑𝑎𝑦 is mainly affected by 3G radio conditions, i.e. received signal code power (RSCP) and the (energy per chip divided by the power density in the band) E𝑐 /N0 , 2G coverage and IS-CR parameter setting.

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1) Distribution of inter-system transitions: considers all possible system transitions in both directions. These transitions may be HOs, CCs or CRs as shown in Fig. 3. As expected, major part of them are IS-CR from/to 3G accounting for 63% of the cases. One can also see that HOs from 2G to 3G are not enabled i.e., once a W2G HO is performed the call must be ended before the UE can come back via IS-CR or CC.

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( 𝑊 2𝐺−𝑥 ) 2) Total 2G Camping Time per transition pair 𝑇𝐺2𝑊 −𝑥 : is defined as the average time the UE spend in 2G per type of transition W2G and G2W where 𝑥 can be a HO, CC or CR. Fig. 4 shows the cumulative distribution function (CDF) for each transition pair. Notice that about half of the UEs spent less than 2 minutes in 2G between two consecutive system 𝑊 2𝐺−𝐻𝑂 changes. The longer durations for 𝑇𝐺2𝑊 −𝐶𝑅 can be explained by the fact that G2W-HO is not enabled so that the calls have to be finished in 2G before a G2W-CR as mentioned earlier.

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The UE type (e.g. dongles and smartphones) may significantly influences user behavior and mobility. Furthermore, UE vendors may apply customized features to optimize their idle mode performance. Therefore, calculating KPIs for each UE type and model separately is important to enable a deeper analysis. UE types are determined by examining the IMSI and TAC information. We define then the following KPIs.

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Fig. 5 shows the distribution of 𝑇𝑢𝑑𝑎𝑦 . One can see in our case study network, 80% of the UEs stayed camping in 2G for less than two hours per day. Provided good 2G coverage, 𝑇𝑢𝑑𝑎𝑦 should be roughly equal to the time during which the 3G radio conditions are worse than the 2G measurement trigger thresholds set by the IS-CR parameters. Therefore, given the

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knowledge of the network radio conditions, one can estimate expected 2G camping time. The expected 2G camping time in the case study network is approximately 1 hour per day, which is consistent with the (measured result. ) 2) Ping-pong IS-CR rate 𝑅𝑢𝑝𝑖𝑛𝑔−𝑝𝑜𝑛𝑔 : is defined as the rate of ping-pongs out of all IS-CR samples. Ping-pongs are defined as any IS-CR taking place back and forth between two technologies in a short time. From Table I one can notice that three models accounted for almost half of the UEs and the difference between dongles and smartphones is significant. Due to low mobility, most dongles had never camped in 2G during the measured period. On the contrary, almost all of the smartphones had camped in 2G and have much higher 𝑅𝑢𝑝𝑖𝑛𝑔−𝑝𝑜𝑛𝑔 than the two dongles brands A and B. UE Type Overall

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the 2G camping time and avoid unnecessary IS-CR in these areas.

TABLE I 2G CAMPING TIME AND PING - PONG IS-CR RATE PER UE MODEL

C. Cell Level Aggregation Uniform parameter setting is usually a sub-optimal solution for all the regions due to different 2G/3G radio conditions, UE mobility, etc. Therefore, its critical to define cell level KPIs to enable local optimization. To analyze the performance of the 3G/2G camping problem using our approach, we initially classify the areas in: ∙ Type I: UEs stay camping in 2G for long time; ∙ Type II: most of the IS-CR are unnecessary. To analyze the cell level performance the following KPIs are defined. 1) Mean and percentile ( values of 2G) camping time before 𝑛−𝑡ℎ 𝑚𝑒𝑎𝑛 : are used to identify , 𝑇3𝑔𝑐𝑒𝑙𝑙 reselecting to a 3G cell 𝑇3𝑔𝑐𝑒𝑙𝑙 the two types of areas. In the urban and suburban environments IS-CR are mainly due to the weak 3G coverage in small spots, e.g. in-building and deep shadowing regions. Therefore its 𝑛−𝑡ℎ 𝑚𝑒𝑎𝑛 , 𝑇3𝑔𝑐𝑒𝑙𝑙 to optimize parameters in reasonable to use 𝑇3𝑔𝑐𝑒𝑙𝑙 the neighborhood of the serving area of the 3G cell. 𝑛−𝑡ℎ 𝑚𝑒𝑎𝑛 and 𝑇3𝑔𝑐𝑒𝑙𝑙 in the Fig. 6 shows the distribution of 𝑇3𝑔𝑐𝑒𝑙𝑙 case study network. Based on the 75-𝑡ℎ percentile CDF curve, about 17% of the cells have at least 25% of the 2G camping time larger than 15 minutes. In about 5% of the cells have at least 75% of the samples with less than 1 minute. The serving areas of these cells are respectively identified as Type I and Type II areas. Fig. 7 illustrates the location of these cells. The map of another city is used to hide the location of the case study network. Two areas are highlighted in the map as detected Type I and Type II areas. Tuning IS-CR parameters may reduce

Fig. 7.

Areas identified as of Type I and Type II

) ( 𝑑𝑎𝑦 : gives the total 2) Number of IS-CR per day 𝑁𝐼𝑆−𝐶𝑅 number of IS-CR samples averaged per day. Fig. 8 shows 𝑑𝑎𝑦 the distribution of 𝑁𝐼𝑆−𝐶𝑅 . One can notice more than 200 inter-system changes in 20% of 3G cells every day which may indicate unnecessary transitions and, consequently, target optimization areas to reduce this number. Traffic, radio condition, UE mobility and parameter settings should be taken into account.

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In both cases, the 3G IS-CR trigger levels are lowered by 1 or 2 dB to delay or avoid the 3G to 2G IS-CR. For the Type I area the 2G IS-CR trigger level is lowered by 1 or 2 dB as well for the UEs to come back to 3G earlier. On the other hand, for the Type II area the 2G parameter is not tuned which is helpful to avoid ping-pong. In the case of an autonomous algorithm, after applying the parameter changes to the network, accessibility, e.g. paging success rate, must be kept monitored to control negative effects of the tuning. Additional tuning range limitation can be applied to the parameters to avoid extreme values.

V. I NTER - SYSTEM C ELL R ESELECTION SON When a UE is camping on 3G, measurements on 2G cells should be performed when the measured E𝑐 /N0 is lower than qQualMin + sRatSearch, which are both 3G parameters defined per cell. qQualMin is defined as the minimum E𝑐 /N0 measured by the UE that can be served by this cell. Similarly, sRatSearch is an extra threshold specific for the UE to start searching for other systems for reselection. We consider this as a triggering point to perform IS-CR from 3G to 2G. When the UE is camped on 2G, a common strategy is to keep measuring 3G to increase the probability the UEs come back to 3G. So, IS-CR from 2G to 3G is triggered when the 3G E𝑐 /N0 is higher than the 2G parameter FDDQMIN.

VI. C ONCLUSION This paper presented a framework to identify the 3G/2G camping problem. The proposal relies on the post processing of PM events from the radio access and core networks. We validated the approach using data from a 3G/2G network. We also have shown how the produced outcome may be used as input for self-optimization algorithms. The authors are currently performing tests concerning the optimization algorithms and results might be addressed in further publications.

A. Self-Optimization Relying on the event-based KPIs, a rule-based selfoptimization algorithm is proposed to illustrate the applicability of our approach as a SON enabler. The target of the algorithm is to reduce camping time in 2G and avoid unnecessary IS-CR. The rules can be either autonomous (iterative in closed-loop) or automated (automated and open-loop) [5]. When tuning mobility parameters, core or center of coverage and border cells or edge of coverage are usually differentiated by operator by some method or simply by observing the cell location in a map and coverage related statistics such as drop rate, inter-system changes and so on. We only assume core cells in this example. The first rule is shown in Fig. 9 and applies for Type I areas, 𝑑𝑎𝑦 75−𝑡ℎ > 𝑛0 and 𝑇3𝑔𝑐𝑒𝑙𝑙 > 𝑑𝑙𝑜𝑛𝑔 where characterized by 𝑁𝐼𝑆−𝐶𝑅 0 a 𝑛0 is assumed to be a high number of IS-CRs and 𝑑𝑙𝑜𝑛𝑔 0 long time camped in 2G both respectively averaged per day and per transition. In our case study network we considered = 600 seconds and 𝑛0 = 100 transitions. 𝑑𝑙𝑜𝑛𝑔 0 The second rule is shown in Fig. 10 and applies for cells of 𝑑𝑎𝑦 75−𝑡ℎ Type II areas, characterized by 𝑁𝐼𝑆−𝐶𝑅 > 𝑛0 and 𝑇3𝑔𝑐𝑒𝑙𝑙 < 𝑠ℎ𝑜𝑟𝑡 𝑠ℎ𝑜𝑟𝑡 𝑑0 where 𝑑0 is assumed as a too short time in 2G. In = 120 seconds. our case study we assumed 𝑑𝑠ℎ𝑜𝑟𝑡 0

R EFERENCES [1] 3GPP TS 25.304 v10.4.0 Release 10, User Equipment (UE) procedures in idle mode and procedures for cells reselection in connected mode, http://www.3gpp.org/ftp/Specs/html-info/25304.htm, mar. 2012. [2] A. Garavaglia, C. Brunner, D. Flore, Ming Yang, and F. Pica, “Intersystem cell reselection parameter optimization in UMTS,” in Personal, Indoor and Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on, sept. 2005, vol. 3, pp. 1636 –1640 Vol. 3. [3] J. Laiho, Wacker A., and Novosad T., Radio Network Planning and Optimisation for UMTS, John Wiley and Sons, West Sussex, United Kingdom, 2nd edition, 2006. [4] A. Mohammed, H. Kamal, and S. AbdelWahab, “2G/3G inter-RAT handover performance analysis,” in Antennas and Propagation, 2007. EuCAP 2007. The Second European Conference on, nov. 2007, pp. 1 –8. [5] J. Ramiro and K. Hamied, Self-Organizing Networks: Self-Planning, SelfOptimization and Self-Healing for GSM, UMTS and LTE, John Wiley and Sons, West Sussex, United Kingdom, 1st edition, 2012. [6] Fabio Ricciato, “Traffic monitoring and analysis for the optimization of a 3G network,” Wireless Communications, IEEE, vol. 13, no. 6, pp. 42 –49, dec. 2006. [7] Mapping between IMEITAC and terminal models, “http://www.nobbi.com/tacquery.php,” .

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