effectiveness of cell outage compensation in lte networks

Effectiveness of Cell Outage Compensation in LTE Networks

M. Amirijoo1, L. Jorguseski2, R. Litjens2 and R. Nascimento2 1 Ericsson, Linköping, Sweden, 2 TNO ICT, Delft, The Netherlands

[email protected], {ljupco.jorguseski,remco.litjens,renato.nascimento}@tno.nl

Abstract — Cell outage management is a self-healing

functionality in future mobile cellular networks, aiming to automatically detect cell or site level outages (cell outage detection) as well as to mitigate as much as possible the caused degradation of coverage, capacity and/or service quality (cell outage compensation). Cell outage compensation has a variety of control parameters (and combination thereof) at its disposal in surrounding cells/sites, including the reference signal power PRS,antenna tilt, scheduling parameters and the uplink target received power level P0. By appropriately tuning these control parameters, the outage-induced performance effects can be minimised, in terms of some operator-specified balance of relevant performance metrics. This paper analyses the effectiveness of selected control parameters in mitigating the effects of cell/site outages, learning that the antenna tilt and P0are most effective in restoring coverage, while P0 is most effective in restoring user throughput performance.

I. INTRODUCTION Cell outage management (COM) is an integral part of the

‘self-organising network’ concept in E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) [1]-[5], with the objective to enhance the network robustness and resilience, and to minimise the outage-induced decrease in operator revenue and/or the customer satisfaction. COM comprises cell outage detection (COD) and cell outage compensation (COC). The aim of COD is to automatically identify the occurrence and scope of an outage, while COC aims at automatic mitigation of the performance degradation by an appropriate adjustment of suitable radio parameters (e.g. antenna tilt, power settings) in surrounding cells. Such compensation is governed by the operator policy which specifies the desired performance tradeoffs in the outage area.

Figure 1 depicts the different elements and workflow of COM in future cellular networks. The depicted example is characterised by a site outage, whose pre-outage service area is indicated in red. A variety of measurements, such as alarms, counters or key performance indicators, are gathered by the user terminals, the base stations and/or the operations and maintenance (O&M) center, and fed to the cell outage management algorithms. Fed with these measurements, the cell outage detection function then determines whether, where and what type of outage has occurred, and triggers both the cell outage compensation function as well as the operator’s maintenance department for possible manual repair. The celloutage compensation function translates its measurement input to compensation measures in terms of an adaptation of one or more control parameters in surrounding cells, in line

with the operator-formulated policy regarding the trade-off of local and regional performance effects. Cell outage compensation is likely to be characterised by an iterative process of radio parameter adjustment and evaluation of the performance impact until convergence is reached. Important feedback is provided by the so-called X-map estimation function, which processes measurements including location information in order to generate e.g. coverage or performance maps. Refer to [16] for a more elaborate discussion of cell outage management.

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CompensationOperator�policy

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Figure 1: Overview of cell outage management.

The issue of optimising the coverage and capacity in wireless cellular networks has already been addressed in the literature via e.g. off-line optimisation approaches [6][7][8][9][10]. Coverage and capacity are optimised by appropriately tuning the pilot power, antenna tilt and azimuth. Although such off-line optimisation methods may provide useful suggestions, for COC it is important to develop methods that adjust involved parameters on-line and in real-time in order to timely respond to the outage. Further, some approaches consider single-objective optimisation (for instance capacity), whereas we believe that multiple objectives (like some combination of coverage and quality) need to be considered. Recent studies [11] also address the real-time automatic reconfiguration of the base stations neighbouring the outage area via rule-based and genetic algorithms. This downlink-only study assumes that base stations do not fully utilize the available transmission power in nominal operational mode and have a kind of ‘power budget’ available to compensate for the outage situation. In practical deployments such a power budget is unlikely, as base stations generally utilise full power in order to best provide

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coverage and quality. In this paper we present a quantitative analysis of the compensation potential of different control parameters in mitigating outage-induced performance degradations in LTE (Long Term Evolution) networks, considering both the up- and downlink. Such a controllability study is a logical first step before developing COC algorithms.

The outline of the paper is as follows. Section II discusses control parameters that are potentially effective for COC purposes. In Section III we describe a number of relevant scenarios for the development and assessment of cell outage compensation solutions. The assessment approach is outlined in Section IV, followed by the numerical results in Section V. Section VI ends this paper with some concluding remarks.

II. CONTROL PARAMETERS In this section we describe a list of radio parameters that are

potentially effective in achieving the compensation objectives. An extensive quantitative assessment is needed to assess the relative effectiveness of different control parameters, and thereby lay the foundations for algorithm development. In principle all radio parameters that somehow affect coverage and the spatial aspects of capacity and service quality, are potentially relevant from a cell outage compensation perspective. These control parameters include the following. � Physical channel transmit power (downlink) – The

transmit power allocated to the downlink physical channels determines the cell size. By increasing the physical channel power in cells surrounding an outage, the service area of those cells can potentially be extended to cover all or part of the outage area. Alternatively, by lowering the physical channel power in some surrounding cells, the interference footprint of those cells in the outage area may be reduced, possibly enabling other surrounding cells to serve the outage area. In the typical case a cell’s total transmit power is already at its maximum, the split between (primarily) the reference signal (RS) and the physical downlink shared channel (PDSCH) can be adjusted [12], for instance raising the RS power PRS to enhance coverage, at the cost of a reduced PDSCH power and hence a reduce traffic handling capacity.

� PUSCH (physical uplink shared channel) target received power level (uplink) – Uplink transmit powers are typically derived from a (possibly user-specific) target received power density P0, in combination with a path loss compensation factor �, both of which are broadcast by the eNodeB [13][14]. Control parameter P0 can be used to enhance the coverage probability in case of an outage: reducing P0 lowers inter-cell interference levels and allows more remote terminals to connect to a given base station. This coverage enhancement leads to a higher number of served users per cell and hence comes at the cost of a lower per-user data throughput. A further negative effect on the user throughput is due to the lower achievable modulation and coding scheme (MCS) per resource block. Effectively, the cell then trades service quality for enhanced coverage. Alternatively, it can be

regarded as a trade-off between cell throughput and cell-edge user throughput.

� Antenna parameters – Modern antenna design allows electrical (non-mechanical) adaptation of both the orientation of the main antenna lobe and the antenna pattern, e.g. via remote electrical tilt or beam forming techniques. Extensive studies for WCDMA-based systems reveal that antenna tilt is a highly responsive lever when it comes to shaping the cell footprint and the interference coupling with other cells [15]. These capabi-lities are therefore of great potential for COC purposes .

� Packet scheduling parameters – Modern mobile networking technologies generally feature a shared channel operation, with channel access managed by a channel-adaptive packet scheduler. Such channel-adaptive packet schedulers typically come in different operator-tuneable flavours, that primarily differ in their trade-off between resource efficiency and spatial fairness. Typically, an associated scheduling parameter exists which provides an opportunity to effectively shift resources towards remote users and hence a potentially effective means for cell outage compensation.

Besides the above-mentioned primary control parameters, other control parameters exist that may require an update as consequence of outage-induced parameter adaptations. For instance, changes in PRS and/or antenna tilts may influence neighbour relations and mobility parameters and hence require these to be updated. The analysis presented in this paper will concentrate on PRS, P0 and the antenna tilt.

III. SCENARIOS In this section a few scenarios are identified that are

deemed relevant for the pursued controllability studies. The relevance of the scenarios lies therein that they capture a diversity of case studies representing a variety of network, traffic and propagation conditions, where higher or lower compensation gains are anticipated. A selection of key scenarios is briefly described below: � Impact of eNodeB density and traffic load – In a sparse,

coverage-driven network layout, little potential is likely to exist for compensating outage-induced performance loss. In a dense, capacity-driven network layout, however, this potential is significantly higher. This is particularly true when traffic loads are low, considering the relatively high available capacity and low interference levels.

� Impact of service type/mix – The distinct quality of service requirements of different services affect the compensation potential. For instance, in case of only low bandwidth services, it is much more likely that adjacent cells can provide coverage to the relative remote users in the outage area. The service mix determines the relative traffic load associated with such low bandwidth services and hence also the compensation potential.

� Impact of spatial traffic distribution – If traffic is located mainly near sites, the compensation potential is limited since the UEs are relatively far away from neighbouring

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and compensating sites. Alternatively, if traffic is concentrated ‘in between’ sites, the potential is larger.

Refer to [16] for a more elaborate discussion of these and other scenarios, for instance related to the impact of mobility, the location of an outage area or the propagation environment.

The analysis presented in this paper will address scenarios that differ in the eNodeB density (inter-site distance) and the traffic load. Scenario specifications are given in Section V.

IV. ASSESSMENT APPROACH In this section we outline the assessment approach of the

controllability study, consisting of methods to deal with the inherent dimensionality of the problem and considerations regarding the assessment of the relative effectiveness of the different control parameters. The dimension of the considered problem is determined by the number of considered cells involved in the outage compensation, the number of control parameters and the number of possible settings of these parameters. A further distinction is made between a site and a cell outage, which doubles the number of relevant cases.

Figure 2 illustrates (part of) the assumed hexagonal layout surrounding a site (left) or cell (right) outage. The outage cells are marked with a light red shade. Surrounding cells whose radio parameters are adapted for the purpose of cell outage compensation are marked with an arrow pointing in the direction of the associated main antenna lobe. With the colours of these arrows the assumed cell grouping is indicated, which is based on symmetry arguments: cells whose arrows have the same colour have the same orientation towards the outage area and will therefore be treated identically when varying control parameter settings. As illustrated by the figure, in case of a site outage, there are two such cell groups (blue and green cells), while in case of a cell outage there are three such cell groups (blue, green and red cells). For reasons of readability and space limitation, we only cover the site outage case in the remainder of the paper.

SITE OUTAGE CELL OUTAGESITE OUTAGE CELL OUTAGE

Figure 2: Site/cell outage scenarios and cell grouping.

Considering e.g. antenna tilt as a control parameter, whose impact of the local performance is to be investigated in a post-outage scenario, we are evaluating the performance effects of different pairs (��A,��B), where ��i denotes the change in antenna tilt applied to all cells in group i � {A,B} and A and B refer to the ‘green’ and ‘blue’ cell groups as marked in Figure 2 (left), respectively.

The performance achieved by a given choice of (��A,��B) is expressed in terms of the fraction of satisfied users, where a

user is deemed satisfied if there is coverage at its location and it experiences an up- and downlink throughput at least equal to � times the requested up- and downlink throughput. Herein, a location is called ‘covered’ if three conditions are satisfied: (i) the RS SINR exceeds -6 dB; (ii) the RS received power (RSRP) exceeds -127 dBm; and (iii) the experienced SINR on the DL and UL is high enough to support the lowest MCS, i.e. SINR � -6.5 dB. Further, the choice of operator policy parameter � indicates the relative importance of coverage and throughput in the performance assessment. Note that in the event of an outage, a different operator policy may apply than during nominal operation, e.g. placing greater emphasis on providing coverage. The metric is assessed over the first tier of sectors surrounding outage, i.e. those capturing the traffic previously served by the outage site and, thus, compensating for the outage effects. For a given choice of �, the optimal choice of e.g. (��A,��B) can be obtained for each scenario.

This exercise is repeated for all scenarios and for all three investigated control parameters, i.e. the reference signal power PRS, the uplink target received power level P0 and the antenna tilt, keeping the untouched control parameters at their default (pre-outage) setting. A comparison of the potential of such an optimised control parameter setting in mitigating the outage-induced performance degradations, will give an indication of the extent to which the outage effects can indeed be mitigated by means of cell outage compensation and which control parameter(s) is/are most effective in doing so.

V. NUMERICAL RESULTS The sensitivity analysis is performed using a Monte Carlo-based LTE network simulator. We consider a hexagonal layout of 19×3 cells, as recognisable in Figure 3. Key system parameters are listed in Table 1 (largely based on [17]).

Table 1: Key system parameters.

Capacity-driven layout Coverage-driven layout

Inter-site distance 500 m 2200 m

Antenna downtilt 15o 5o

System bandwidth 10 MHz

PMAX,BS, PRS, PMAX,UE 46 dBm, 33 dBm, 25 dBm

Path loss 128.1 + 37.6 log10 r, with r in km

Shadowing � = 8 dB, inter-site correlation of ½, decorrel. distance = inter-site distance / 15

Antenna model 3GPP 3D model

Noise level -199 dBW/Hz in DL, -195 dBW/Hz in UL

Service Generic elastic data service with a requested throughput of 1 Mb/s (DL) & 250 kb/s (UL)

The considered scenarios are specified by the inter-site distance and the traffic load. The coverage-driven network layout is characterised by an inter-site distance of 2200m and a default (pre-outage) antenna downtilt of 5o. For this layout we consider only a low traffic load of (on average) 1 UE per cell (with a traffic model as given in Table 1). The capacity-driven network layout is characterised by an inter-site distance

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of 500m, a default antenna downtilt of 15o, and low, medium or high traffic loads of (on average) 1, 23 and 47 UEs per cell. In the simulator, the users’ throughput requirements are in principle satisfied with a location- and interference-dependent amount of resources, while in case of cell congestion, the performance of all served users is fairly degraded.

In search for the optimal control parameter value, a limited range of practical settings is considered. The reference signal power PRS is varied from 27 to 39 dBm, with a default setting of 33 dBm (on average about 5% of the total maximum transmit power, assuming a uniform power distribution over all control and data symbols). The uplink target received power level P0 is varied from -114 dBm to -78 dBm (-132 dBm to -96 dBm), with a pre-optimised default setting of -96 dBm (-114 dBm) in the capacity-driven (coverage-driven) network layout. Finally, the antenna tilt is varied between 0o to 20o, with a default setting of 5o in the coverage-oriented network layout and 15o in the capacity-oriented layout.

For the high load scenario with the capacity-oriented network layout, Figure 3 shows spatial plots of the coverage (showing ‘holes’ with a coverage probability < 95%), average up- and downlink throughputs (columns). The rows are showing the performance for the pre-outage situation, the post-outage situation without compensation and the post-outage situation with a P0, antenna downtilt or PRS, that are optimised under an operator policy specified by � = 0.05, i.e. with a principal interest in providing coverage. The optimised settings of P0 are -108 dBm for cell group A directed towards the outage area, and a P0 of -105 dBm for the cell group B that are directed sideways (see Figure 2). The plots illustrate the outage-induced performance effects (coverage and throughput degradation), as well as the degree of compensation that is achieved by optimising P0, downtilt or PRS in the sectors adjacent to the outage area. Observe that, in line with the chosen operator policy, the coverage is restored to nearly 100% in the outage area, which however comes at the cost of a further throughput degradation in the outage area.

The optimised antenna downtilt settings are 10o and 12o for cells in groups A and B, respectively, i.e. a significant reduction of the pre-outage tilt settings applied in order to direct the antennas more towards the outage area. Compared to the optimised P0 settings, these antenna adjustments yield a slightly worse coverage but a somewhat better uplink throughput. Note that when P0 is used to enhance coverage, this also affects the uplink throughput: a lower P0 leads to lower applied MCSs per resource block and (hence) also an increased contention level for uplink resources. The optimised PRS setting are 39 dBm for all compensating cells, i.e. an increase of 6 dB compared to the default setting. Since the uplink is the bottleneck direction in the considered scenario, optimisation of PRS is less effective in enhancing coverage. Rather, the significant increase of PRS required to achieve the slight coverage improvement, comes at a significant reduction of downlink throughput. This is due to the decreased transmit power availability for the PDSCH. The latter effect also explains the observation that the uplink throughput remains relatively high, compared to the cases with optimised P0 or tilt.

Figure 4 shows for all four scenarios the fraction of satisfied users, which considers both coverage and quality aspects, and is evaluated over the sectors surrounding the outage area. The post-outage optimised performance, in terms of satisfied user ratio, is shown for all three control parameters.

Consider the bottom-right scenario (capacity-driven network layout with a high traffic load) and assume a primarily coverage-driven operator policy specified by � = 0.05 (left-most bar for each case). Observe that the fraction of satisfied users (which for this choice of � pretty much equals the coverage probability) equals 100% in the pre-outage situation. This then drops to about 91% due to the site outage, recalling that this is measured over the first tier of sectors surrounding the outage area; hence the coverage loss in the outage area itself is significantly more dramatic. As shown, this coverage loss can be fully restored to 100% by appropriate tuning of the antenna tilt or P0. As discussed above (and illustrated in Figure 3), restoring the coverage probability to such a level may come at a throughput sacrifice. Note that optimisation of PRS cannot fully restore the coverage loss, since for several users coverage condition (iii) (see Section IV) remains unsatisfied. Similar insights apply for the cases of � = 0.10 and � = 0.20, although for the latter choice, even with optimised compensation the resulting performance falls significantly short of the pre-outage situation. Still, there is a clear improvement compared to the post-outage situation without any compensation actions, thereby clearly indicating the benefits of cell outage compensation.

For higher choices of �, the focus on user throughput becomes rather large, and optimisation of e.g. P0 may achieve a fraction of satisfied users that even exceeds the pre-outage level. Although this possibility may at first seem counterintuitive, it is noted that the pre-outage network is planned with a healthy mix of coverage and service quality targets, in particular requiring a coverage probability of 98%. For these higher �’s, however, the optimisation of compensation parameters tends to sacrifice coverage for enhanced throughput. For instance, an increase in P0 reduces the coverage of a cell and therefore captures less traffic. Both the higher P0 (via a higher MCS) and the reduced contention level lead to a throughput enhancement. Since for high �’s throughput weighs relatively strong compared to coverage, this may indeed lead to a higher fraction of satisfied users than the default P0 applied in the pre-outage scenario.

Comparing the different scenarios, note that the outage-induced performance degradations and also the (potential) compensation gains are larger for more heavily loaded scenarios, as long as service quality is not of insignificant relevance in the operator policy (� not too small). This is due to the fact that for lower traffic loads, cells surrounding an outage area have more resources available to serve additional traffic. Furthermore, observe that the tilt and P0 are the most effective control parameters when the policy is primarily coverage-oriented, while optimisation of P0 is most effective when the policy is predominantly quality-oriented (which implicitly states that the uplink is typically the bottleneck).

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Figure 3: For the scenario with a capacity-driven layout and a high traffic load, these spatial plots show the coverage holes and the up-/downlink user throughputs (in Mb/s) for the pre-outage situation, the post-outage situation without

compensation and the post-outage situation with optimised P0, antenna downtilt and PRS.

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Figure 4: For four distinct scenarios, the fraction of satisfied users is shown for the pre-outage situation, the post-outage situation without compensation and the post-outage situation with optimised control parameters. For each case, six bars are shown for different settings of the operator policy parameter � = {0.05,0.10,0.20,0.30,0.40,0.50} (from left to right).

VI. CONCLUDING REMARKS In this paper we presented an overview of the envisaged

cell outage management functionality in future mobile networks. We have discussed the key control parameters that are potentially effective in mitigating outage-induced performance degradations and have presented an analysis of their effectiveness in different scenarios. Key insights provided by the analysis include the observation that both the compensation gains and the most effective control parameter depend on the load and the applied operator policy. Among the considered control parameters, the uplink target received power level P0 and the antenna tilt have proven to be most effective in improving coverage, while P0 is most effective in improving throughput. In our continued research, we further extend the presented sensitivity analysis, including other (combinations of) control parameters, and proceed to develop on-line algorithms for cell outage compensation.

ACKNOWLEDGMENT The presented work was carried out within the EU-

sponsored FP7 SOCRATES project [18].

REFERENCES [1] 3GPP TR 36.902, ‘Self-configuring and self-optimizing network use

cases and solutions’, v1.0.1, 2008. [2] NGMN, ‘Use cases related to self organising networks. Overall

Description’, 2007.

[3] 3GPP S5-090009, ‘NGMN Recommendation on SON & O&M requirements’, RAN3 & SA5 Meeting, Sophia Antipolis, France, 2009.

[4] J.L. van den Berg, R. Litjens, A. Eisenblätter, M. Amirijoo, O. Linnell, C. Blondia, T. Kürner, N. Scully, J. Oszmianski and L. C. Schmelz, ‘Self-organisation in future mobile communication networks’, ICT Mobile Summit ‘08, Stockholm, Sweden, 2008.

[5] 3GPP TS 32.541, ‘Self-Healing OAM; Concepts and Requirements’, v1.2.0, 2010.

[6] I. Siomina, P. Varbrand and D. Yuan, ‘Automated optimisation of service coverage and base station antenna configuration in UMTS networks’, Wireless Communications Magazine, vol. 13, no. 6, 2006.

[7] K. Valkealahti, A. Höglund, J. Pakkinen and A. Flanagan, ‘WCDMA common pilot power control for load and coverage balancing’, PIMRC ‘02, Lisbon, Portugal, 2002.

[8] J. Yang and J. Lin, ‘Optimisation of pilot power management in a CDMA radio network’, VTC ‘00, Boston, USA, 2000.

[9] D. Fagen, P. Vicharelli and J. Weitzen, ‘Automated coverage optimisation in wireless networks’, VTC ‘06, Montreal, Canada, 2006.

[10] K. Valkealahti, A. Höglund and T. Novosad, ‘UMTS radio network multiparameter control’, PIMRC ‘03, Beijing, China, 2003.

[11] E3, ‘Simulation based recommendations for DSA and self-management’, FP7 E3 project ICT-2007-216248, 2009.

[12] 3GPP TS 36.211, ‘Physical Channels and Modulation’, v8.9.0, 2009. [13] A. Simonsson and A. Furuskar, ‘Uplink power control in LTE –

Overview and performance’, VTC ’08, Calgary, Canada, 2008. [14] 3GPP TS 36.213, ‘Physical layer procedures’, v8.8.0, 2009. [15] J. Niemelä and J. Lempiäinen, ‘Impact of mechanical antenna downtilt

on performance of W-CDMA cellular networks’, VTC ‘04, Italy, 2004. [16] M. Amirijoo, L. Jorguseski, T. Kürner, R. Litjens, M. Neuland, L.C.

Schmelz and U. Türke, ‘Cell Outage Management in LTE Networks’, ISWCS ’09, Siena, Italy, 2009.

[17] 3GPP TR 36.814, ‘Further advancements for E-UTRA – Physical layer aspects’, v1.0.1, 2009.

[18] SOCRATES project, www.fp7-socrates.eu, 2010.

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