Cell Outage Management in LTE Networks

Abstract

Cell outage management is a functionality aiming to automatically detect and mitigate outages that occur in radio networks due to unexpected failures. We envisage that future radio networks autonomously detect an outage based on measurements, from e.g., user equipment and base stations, and alter the configuration of surrounding radio base stations in order to compensate for the outage-induced coverage and service quality degradations and satisfy the operator-specified performance requirements as much as possible. In this paper we present a framework for cell outage management and outline the key components necessary to detect and compensate outages as well as to develop and evaluate the required algorithms.

Full text

EUROPEAN COOPERATION
IN THE FIELD OF SCIENTIFIC
AND TECHNICAL RESEARCH
—————————————————
EURO-COST
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COST 2100 TD(09)941
Vienna, Austria
2009/Sep/28-30
SOURCE: FP7-SOCRATES
c/o IFN, Institut für Nachrichtentechnik
Technische Universität Braunschweig,
Braunschweig, Germany
Cell Outage Management in LTE Networks
This paper has been presented at ISWCS 2009, Siena, Italy
Contact:
Prof. Dr. -Ing Thomas Kürner
IFN/Institut für Nachrichtentechnik
Technische Universität Braunschweig
Schleinitzstr.22
D-38106 Braunschweig
Germany
Phone: + 49(0)531-391 2416
Fax: + 49(0)531-391 5192
Email: kuerner@ifn.ing.tu-bs.de

Cell Outage Management in LTE Networks
M. Amirijoo1, L. Jorguseski2, T. Kürner3, R. Litjens2, M. Neuland3, L. C. Schmelz4, U. Türke5
1 Ericsson, Linköping, Sweden, 2 TNO ICT, Delft, The Netherlands, 3 TU Braunschweig, Braunschweig, Germany, 4 Nokia
Siemens Networks, Munich, Germany, 5 Atesio, Berlin, Germany
mehdi.amirijoo@ericsson.com, ljupco.jorguseski@tno.nl, t.kuerner@tu-bs.de,
remco.litjens@tno.nl, m.neuland@tu-bs.de, lars.schmelz@nsn.com, tuerke@atesio.de
Abstract — Cell outage management is a functionality aiming
to automatically detect and mitigate outages that occur in radio
networks due to unexpected failures. We envisage that future
radio networks autonomously detect an outage based on
measurements, from e.g., user equipment and base stations, and
alter the configuration of surrounding radio base stations in
order to compensate for the outage-induced coverage and service
quality degradations and satisfy the operator-specified
performance requirements as much as possible. In this paper we
present a framework for cell outage management and outline the
key components necessary to detect and compensate outages as
well as to develop and evaluate the required algorithms.
I. INTRODUCTION
The standardisation body 3rd Generation Partnership
Project (3GPP) has finalised the first release (Release 8) of the
UMTS successor named Evolved UTRAN (E-UTRAN),
commonly known as Long Term Evolution (LTE). In parallel
with the LTE development, the Next Generation Mobile
Network (NGMN) association of operators brings forward
requirements on management simplicity and cost
efficiency [1][2][3]. A promising approach for achieving these
requirements is the introduction of self-organisation
functionalities into the E-UTRAN [4]. One aspect that
benefits from self-organisation is cell outage management
(COM), which can be divided into cell outage detection (COD)
and cell outage compensation (COC).
There are multiple causes for a cell outage, e.g., hardware
and software failures (radio board failure, channel processing
implementation error, etc.), external failures such as power
supply or network connectivity failures, or even erroneous
configuration. While some cell outage cases are detected by
operations & management system (O&M) through
performance counters and/or alarms, others may not be
detected for hours or even days. It is often through relatively
long term performance analyses and/or subscriber complaints
that these outages are detected. Currently, discovery and
identification of some errors involves considerable manual
analysis and may require unplanned site visits, which makes
cell outage detection rather costly. It is the task of the
automated cell outage detection function to timely trigger
appropriate compensation methods, in order to alleviate the
degraded performance due to the resulting coverage gap and
loss in throughput by appropriately adjusting radio parameters
in surrounding sites. Moreover, if required, an immediate
alarm should be raised indicating the occurrence and cause of
an outage, in order to allow swift manual repair.
Cell outage detection has previously been studied and
reported in literature, e.g., [5][6][7]. In [5], historic
information is used in a Bayesian analysis to derive the
probability of a cause (fault that initiates the problem) given
the symptoms (manifestations of the causes). Knowledge of
radio network experts is needed as well as databases from a
real network in order to diagnose the problems in the network.
In [7], a cell outage detection algorithm, which is based on the
neighbour cell list reporting of mobile terminals is presented
and evaluated. The detection algorithms acts on changes in
neighbour reporting patterns, e.g., if a cell is no longer
reported for a certain period of time then the cell is likely to
be in outage. In comparison with previous approaches we
consider a broader perspective and consider not only detection
but also cell outage compensation. Off-line optimisation of
coverage and capacity has been reported in e.g., [8][9][10][11]
and [12]. Coverage and capacity are optimised by
appropriately tuning, e.g., the pilot power, antenna tilt and
azimuth. Although such off-line optimisation methods may
provide useful suggestions, for cell outage compensation 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 (e.g., capacity), whereas we believe that multiple
objectives (e.g., combination of coverage and quality) need to
be considered. As such, in contrast to previous work, we
intend to develop methods for real-time and multi-objective
control and optimisation of LTE networks.
In this paper we present a framework for COM and
describe the needed functionalities and their corresponding
interrelations, as described in Section II. Further, we address a
number of key aspects that play a role in the development of
cell outage management algorithms. This includes a definition
of operator policies regarding the trade-off between various
performance objectives (Section III), an overview of
potentially useful measurements (Section IV), and a set of
appropriate control parameters (Section V). For development
and evaluation of detection and compensation algorithms we
propose a set of suitable scenarios (Sections VI), and present
our assessment methodology and associated criteria
(Section VII). The paper is concluded in Section VIII, where
we also present our future work.

II. OVERVIEW OF CELL OUTAGE MANAGEMENT
The goal of cell outage management is to minimise the
network performance degradation when a cell is in outage
through quick detection and compensation measures. The
latter is done by automatic adjustment of network parameters
in surrounding cells in order to meet the operator’s
performance requirements based on coverage and other
quality indicators, e.g., throughput, to the largest possible
extent. Cell outage compensation algorithms may alter, e.g,
the antenna tilt and the cell transmit power, in order to cover
the outage area.
Altering the radio parameters of the neighbouring cells
means that some of the user equipments (UEs) served by those
cells may be affected. This should be taken into account and
an appropriate balance between the capacity/coverage offered
to the outage area and the unavoidable performance
degradation experienced in the surrounding cells, should be
achieved. This balance is indicated by means of an operator
policy that governs the actions taken by the cell outage
compensation function (see also Section III).
Figure 1 shows the components and workflow of cell
outage management. Various measurements are gathered from
the UEs and the base stations (called eNodeBs in LTE). The
measurements are then fed into the cell outage detection
function, which decides whether at the current time an outage
has occurred and triggers the cell outage compensation
function to take appropriate actions. In the example given in
Figure 1, the base station in the center is in outage, resulting in
a coverage hole. The neighbouring cells have increased their
coverage in order to alleviate the degradation in coverage and
quality.
Figure 1 Overview of the cell outage management components. The center
site is in outage. Red area indicates the previous coverage of the outage cell.
Cell outage compensation is typically characterised by an
iterative process of radio parameter adjustment and evaluation
of the performance impact. In this process there is a clear need
to estimate the performance in the vicinity of the outage area.
This is useful in order to determine to what degree the
compensation actions are successful in terms of satisfying the
given operator policy during an outage. This can be provided
by the so-called X-map estimation function, which
continuously monitors the network and by possibly using
other sources of information such as propagation prediction
data, estimates the spatial characteristics of the network, e.g.,
coverage and quality. Essentially, an X-map is a geographic
map with overlay performance information.
III. OPERATOR POLICY
The configuration changes for compensating a cell outage
influence the network performance. For the network
performance every operator has its own policy, which may
range from just providing coverage up to guaranteeing high
quality in the network. This policy may even be different for
various cells in the network and may vary for cell outage
situations and normal operation situations. Furthermore, the
policies may be declared differently depending on the
time/day of the week. Below we propose a general framework
for defining an operator policy, taking into account the above
mentioned aspects.
In a cell outage situation an operator may still target the
ideal goal of achieving the best possible coverage, providing
the highest accessibility, and delivering the best possible
quality in the cell outage area and all surrounding cells. In
most cases, not all these goals can be fulfilled at the same time.
As a consequence the targets have to be weighted and/or
ranked in order to provide quantitative input to an
optimisation procedure. Depending on the operator’s policy
the optimisation goal itself may vary, i.e., the weighting and
the ranking of the targets may differ. Hence, the policy
definition should be modular/flexible enough to capture
different operator strategies, e.g., coverage-oriented strategy
vs. capacity-oriented strategy.
When defining the cost function for optimisation and
assessment purposes, it is important to include all cells that
are affected in one way or another by the COC algorithm. In
this light, three groups of cells can be distinguished (see
Figure 2): (1) the cell in outage, (2) cells whose parameters
may be adapted by the COC algorithm, and (3) those
surrounding cells whose parameters are not adapted, but
whose performance may be affected by the COC actions.
cell in outage
cells whose parameters may be adapted by the COC
algorithm
surrounding cells whose parameters are not adapted, but
whose performance may be affected by the COC actions
focus on quality
focus on coverage
focus on accessibility
cell in outage
cells whose parameters may be adapted by the COC
algorithm
surrounding cells whose parameters are not adapted, but
whose performance may be affected by the COC actions
focus on quality
focus on coverage
focus on accessibility
cell in outagecell in outage
cells whose parameters may be adapted by the COC
algorithm
cells whose parameters may be adapted by the COC
algorithm
surrounding cells whose parameters are not adapted, but
whose performance may be affected by the COC actions
surrounding cells whose parameters are not adapted, but
whose performance may be affected by the COC actions
focus on qualityfocus on quality
focus on coveragefocus on coverage
focus on accessibilityfocus on accessibility
Figure 2 Cells that are considered when defining a cost function.
The optimisation goals considered in the cost function may
vary for different cells. For example, in cells covering large
areas the coverage should be kept high, whereas in high-
capacity cells located in the same region the focus will be
more on accessibility and/or quality. As a consequence, the
optimisation goals have to be defined on a per-cell basis.
Furthermore, some cells might be more important for the
network operator then other cells due to, e.g., the number of
Measurements
Detection
Compensation
Operator policy
Control
parameters
X-map
estimation
O&M

customers or generated traffic by the customers in these cells.
As such, in addition to the different optimisation goals, cells
may have different importance or priorities. The cost function
has therefore to take into account all optimisation goals per
cell, i.e., coverage, accessibility, and quality, as well as the
corresponding importance. Since it is not possible to derive
the coverage, accessibility, and quality for the cell in outage,
the cost function may consider only the accessibility and
quality of the surrounding cells and the coverage of the whole
sub-network.
IV. MEASUREMENTS
The continuous collection of measurements and analysis of
radio parameters, counters, KPIs, statistics, alarms, and timers,
are an indispensable precondition for the detection and
compensation of a cell outage. In the following these various
information sources are described as measurements. These
measurements may be obtained from various sources (refer
to [13] for an overview of the LTE architecture): (1a) the
eNodeB which is affected by the cell outage (as far as there
are still measurements available), (1b) the neighbouring
eNodeBs, (2) UEs, and (3) O&M system and access
gateway(s). Note that it is to be further investigated to which
extent these measurements are suitable for use in COM
functions. Some major examples are given below:
A. Measurements from eNodeBs:
• Cell load, e.g. the load can be used to indicate the
degree of traffic that is carried during outage.
• Radio Link Failure (RLF) counter, e.g. a sudden
increase in number of RLFs may indicate a cell outage.
• Handover failure rate, e.g., high handover failure rate
may indicate a cell outage.
• Inter-cell interference, e.g., a sudden change of the
interference level may be used to indicate outages.
Further, interference measurements are required to
detect incorrect settings of tilt or cell power during
compensation.
• Blocked / dropped calls, e.g., high number of dropped
calls may indicate a coverage hole.
• Cell throughput and per-UE throughput.
B. Measurements from UE
• Reference Signal Received Power (RSRP)
measurements taken by the UE of the serving and
surrounding cells. For example, if a neighbouring cell
is no longer reported then this may be an indication of
an outage.
• Failure reports are generated by the UE after
connection or handover failures and sent to eNodeB
for cause analysis. Note, failure reports are not
standardised yet.
C. Measurements from O&M
KPIs and statistics are continuously calculated at the O&M
system. Some of these measurements can be used for the
purpose of outage detection and to validate performance
during and after outage compensation. Alarms appearing at
the O&M system may also be used for outage detection.
For outage detection, taking all the described measurements
into account, a dedicated algorithm is required that combines
the measurements and uses an appropriate decision logic to
determine whether an outage has occurred or not.
Regarding COC, it is clear that the goals presented in
Section III need to be measurable. Accessibility and quality
can be assessed by measurements such as call block ratio and
per-UE throughput. The coverage presents by far greater
challenges. One idea that we are currently pursuing is to
estimate coverage using UE measurement reports. Estimates
of the geographic coordinates of the mobile position may be
assigned to the obtained measurement reports in order to
derive a map which relates geo-reference data to performance
related metrics, e.g., path loss (cf. also Figure 1). We are
intending to investigate to which degree coverage can be
estimated based on UE reports (utilising position information)
as well as what accuracy is needed as input to the COC
algorithms.
V. CONTROL PARAMETERS
All radio parameters that have an impact on coverage and
capacity are relevant from a cell outage compensation point of
view. This includes transmit power and antenna parameters.
The power allocated to the physical channels dictates the
cell size. On the one hand, by increasing the physical channel
power the coverage area of a cell can be increased (in order to
compensate for outage). On the other hand, by lowering the
cell power the cell area is reduced and as a consequence load
and interference caused by the cell can also be reduced.
Further, modern antenna design allows influencing the
antenna pattern and the orientation of the main lobe by
electrical means (e.g., remote electrical tilt and beam forming).
Extensive studies for WCDMA 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 [14]. Beam forming may be used to steer the direction of
the antenna gain toward the area that is in outage (given that
the position and orientation of the base stations are known, at
least to some degree).
Besides the above-mentioned primary control parameters,
which are employed to improve the coverage area, there are
secondary control parameters. These are parameters that might
require an update as consequence of a cell outage as such, or
as consequence of the alteration of primary control parameters.
For example cell outages as well as the triggered adjustment
of e.g. reference signal power and antenna tilt are likely to
induce new neighbor relations and hence neighbor cell lists
need to be updated.
VI. SCENARIOS
In this section several scenarios are described that will be
considered in the development and assessment of cell outage
compensation methods. The appropriateness of the scenarios
lies therein that they capture a diversity of case studies
representing different network situations and where significant

impact on COD and COC performance is anticipated, and as
such, are likely to impact the specifics of the developed
algorithms. For cell outage compensation, we limit ourselves
to cases where an entire site or sector fails and do not consider
particular channel or transport network failures. The following
scenario descriptions specify assumptions regarding network,
traffic, and environment aspects:
• Impact of eNodeB density and traffic load – In a sparse,
coverage-driven network layout, little potential is
likely to exist for compensating outage-induced
coverage/capacity loss. In a dense, capacity-driven
network layout, however, this potential is significantly
higher, particularly when traffic loads are low.
• Impact of service type – The distinct quality of service
requirements of different services affect the
compensation potential. For instance, compensation
actions may be able to alleviate local outage effects to
handle only low bandwidth services.
• Impact of outage location – If cell outages occur at the
edge of an ‘LTE island’ fewer neighbours exist to
enable compensation. For outages in the core of such
an ‘LTE island’, the compensation potential is larger.
• Impact of user mobility – If mobility is low, few users
spend a relatively long time in an outage area.
Alternatively, if the degree of mobility is high, many
users spend a relatively short time in an outage area.
The perceived outage impact depends on the delay-
tolerance and elasticity of the service.
• Impact of spatial traffic distribution – If traffic is
concentrated near sites, it is typically relatively far
away from neighbouring sites and hence the
compensation potential is limited. Alternatively, if
traffic is concentrated ‘in between’ sites, the potential
is larger.
Other possible scenarios that are deemed somewhat less
significant are related to propagation aspects and the UE
terminal class. As an example for the first aspect, note that
e.g., a higher shadowing variation generally causes the outage
area to be more scattered, providing more potential for
surrounding cells to capture the traffic. Regarding the latter,
note that the higher a UE’s maximum transmit power is, the
lower the need for outage compensation, since the UE may
still be able to attach to a more distant cell even without
compensation measures.
VII. ASSESSMENT CRITERIA AND METHODOLOGY
Appropriate assessment criteria are required for comparison
of different candidate COD and COC algorithms, and to
assess the network performance improvement when the COD
and COC algorithms are activated. The assessment criteria
should also quantify the trade-off between the gains in
network performance and deployment impact in terms of
signalling and processing overhead, complexity, etc.
Denote with Tfail and Tdetect the time instant when the failure
occurred and when it is detected, respectively. A failure
duration interval starts with the occurrence of a failure and
ends with the elimination of the failure (e.g., by repairing the
error involved), see Figure 3. A true detection is a detection
which is reported by the cell outage detection mechanism
during the failure duration interval. In contrast a false
detection is reported outside the failure duration interval and
is, as such, an erroneous detection.
Figure 3 Cell outage detection events.
Denote with Nfail the number of failures during the
observation period, and with Ndetect and Nfalse the number of
true and false detections, respectively. Suitable assessment
criteria to evaluate the outage detection performance are:
detection delay Tdetect - Tfail; detection probability Ndetect / Nfail;
and the false detection probability Nfalse / (Nfalse + Ndetect).
The assessment criteria for performance evaluation of the
COC algorithms can be defined from a subset of the UE and
eNodeB measurements, presented in Section IV, or based on
the performance information available in the simulations
models related to e.g. system capacity, provided coverage and
quality of service (QoS), etc. For example, an important
assessment criteria is coverage, which is defined as
(Nbin – Nbin_outage) / Nbin, where Nbin is the number of pixels or
bins in the investigated area while Nbin_outage is the number of
pixels having average Signal to Interference Noise Ratio
(SINR) or throughput lower than a pre-defined threshold.
Suitable criteria for assessing the deployment impact for
cell outage detection and compensation are the signalling
overhead, defined as number of messages (or bytes) per time
unit, and processing overhead that is defined as amount of
processing needed to detect or compensate the outage. Note
that the signalling overhead can be assessed on the transport
network (e.g. X2, S1, and O&M interfaces) and the radio
network (e.g. between the UE and the eNodeB).
In order to illustrate the envisaged evaluation methodology
for the COC we define three system states as presented in
Figure 4. State A, denotes the pre-outage situation, state B is
the post-outage situation without COC, and state C is the post-
outage situation with COC. The red arrow indicates the time
instant at which the cell outage occurs.
time
Supported traffic
A
B
C
Outage
Figure 4 The system states before and after a cell outage event.
F
Detection Delay
D
True Detection
F
Missed Detection
False Detection
Tfail Tdetect
time
FD
Tdetect
FD
Failure occurrence Failure detection Failure duration
D

The proposed evaluation methodology for the assessment
of the COC algorithm consists of the following steps:
Step 1 ‘Controllability and observability analysis’ – Given
the post-outage system state B, it should be determined which
control parameters (see Section V) have the highest impact on
the system performance. Furthermore, the most important UE
and eNodeB measurements should be identified that can be
used to timely observe the system state and performance with
sufficient accuracy.
Step 2 ‘Design of COC algorithm’ – Design of the self-
optimisation algorithm aided by static/dynamic simulations.
Given system state B and starting from the parameter settings
in system state A, perform step-wise adjustments of the
control parameters deemed most effective in the
controllability study of Step 1. After each COC step the
system performance should be measured in order to evaluate
the impact of the previous COC decision(s). The COC steps
consecutively adjust the control parameters until the algorithm
decides that no further adjustments are needed and the system
converges into a stable working state. It is important to
determine the convergence time of the COC algorithm.
Additionally, it is important here to compare the resulting
system performance (with the COC adjustments) to either the
optimal system performance that can be achieved or the
situation when no COC adjustments are made in the system.
Step 3 ‘COC deployment assessment’ – As a final step it is
important to assess the performance of the algorithm
developed in Step 2, considering the full dynamics of the state
transition from state A to state B as well as the practical
constraints such as e.g. availability of the measurements,
duration of measurement intervals, measurement accuracy,
time needed for the parameter adjustments etc. The impact on
the resulting system performance (see state C in Figure 4)
should be determined when the state transition and the
practical constraints are taken into account.
VIII. CONCLUSION
The detection and compensation of outages in current
mobile access networks is rather slow, costly and suboptimal.
Herein, costly refers to the considerable manual analysis and
required unplanned site visits that are generally involved,
while suboptimal hints at the typical revenue and performance
loss due to delayed and poor mitigation of the reduced
coverage and capacity. The framework described in this paper
addresses the shortcomings of today’s practice and proposes
an approach for automatic cell outage management and
outlines the key components necessary to detect and
compensate for outages. We argue that the detection and
compensation of outages, which aims at alleviating the
performance degradation, can be made autonomous, requiring
minimal operator intervention. This means not only a faster
and better reaction to outages, which decreases the revenue
and performance losses, but also less manual work involved
and, as such, OPEX.
We will continue our work with the following three steps.
First, a controllability study will be carried out in order to
assess to what degree the application of the different control
parameters is effective in the compensation of outages, as well
as to understand the relation between control parameters and
performance indicators, e.g., coverage and throughput. In the
subsequent observability study we will investigate which
measurements, counters, etc, are most suitable for use in
outage detection and compensation algorithms. As part of this,
one line of work is to derive methods for obtaining X-maps.
Lastly, we will develop algorithms for cell outage detection
and compensation and evaluate their performance using the
scenarios and assessment criteria presented in this paper.
ACKNOWLE DGMENT
The presented work was carried out within the FP7
SOCRATES project [15], which is partially funded by the
Commission of the European Union.
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