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7/25/2019 20160621_NextGen-SON-whitepaper-VIAVI.pdf
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White Paper
Modern networks are becoming increasingly characterized by a mix of subscribers using a
wide variety of applications each with their own usage type and quality of service (QoS)
expectations. The ways that subscribers use wireless communication networks varies
dramatically between the various subscribers. Each subscriber is unique with individual
characteristic uses for voice and data services. Usage patterns for subscribers tend to be
determined by various demographic factors including age, occupation, whether they are
corporate or commercial subscribers, whether they are pre- or post-paid, and where they live,among other factors.
When it comes to services, each can be split into those offered by
operators and those from OTT service providers. Devices on the
networks may not meet the traditional definition for subscribers
but can also be Internet of Things devices. These in turn may be
fixed wireless or mobile. And, depending on what each is doing,
will determine the demands it will place on the network and the
resulting expectation of what constitutes satisfactory QoS. There is
even a trend toward providing service to subscribers with mission-
critical requirements, such as emergency-service first responders. Allthis adds up to a vast range of usage characteristics between the
multitude of subscribers using the network.
Extreme Non-uniformity in Cellular Networks
The extreme variation in characteristics for the various subscribers
using the network and the applications they use are two examples of
the non-uniformity challenge that modern network operators face.
However, other aspects of extreme non-uniformity compound this
challenge for operators, as illustrated in Figure 1.
Figure 1. Aspects of extreme non-uniformity in modern cellular networks
Time Subscriber
Location Application
Solving theChallenges ofCellular RANManagement withNext-Generation
SON
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
For example, today’s operators typically have a highly complex
network comprising different access network types spanning 2G,
3G, and 4G, and sometimes in Heterogeneous Network (HetNet)
configurations. The infrastructure will often come from multipleequipment vendors, each with their own vendor-supplied
performance management and optimization solutions. In some cases,
some of the network elements may be virtualized and sometimes
the radio access elements may be centralized, adding complexity
to the challenge of managing performance. There is a risk that the
networks’ heterogeneous nature means that the solutions used to
manage and optimize them are also disjointed and heterogeneous.
If this occurs, it also adds cost and complexity to the networks’
management and optimization.
Time is another dimension of extreme non-uniformity. Networks
encounter performance issues on vastly different timescales. At one
extreme, performance will fluctuate from minute to minute as thesubscribers move around and utilization varies. For example, short
timescale variations are also caused by equipment outages. At the
other extreme, utilization will change over a course of weeks and
months. This arises from the growth in demand for data driven by
ever more sophistication in smartphone apps. Some of the increased
demand can only be addressed by capital expenditure (CapEx)
investment, but in other cases the CapEx investment can be avoided
or deferred by optimizing the radio access network (RAN).
Another facet of extreme non-uniformity is location. Voice and
data services consumption varies significantly by location. For
example, a study performed by Viavi Solutions® evaluated how
data consumption was distributed around a network. The networkwas divided into 50 m2 tiles and adding the total data used by all
subscribers in each tile. Figure 2 shows how demand for data is
distributed between the different cells.
Figure 2. Extreme non-uniformity in network usage by location
This shows that half of the data is consumed in 0.35% of the
network’s geographical area. This non-uniformity adds additional
complexity to optimization. Extreme demand non-uniformity
means that site density will be similarly non-uniform. Often anoperator must resort to HetNet solutions with micro- and pico-
cells and in-building solutions, for example, adding yet another set
of challenges to managing and optimizing more network layers.
The parameterization of this heterogeneous RAN serving a highly
non-uniform and dynamic subscriber population increases the
optimization challenge more than ever before.
A Practical Approach to Optimization
A practical self-organizing network (SON) solution must have a
variety of characteristics that allows it to address the challenges
encountered in managing and optimizing today’s RANs. For
example, a complete SON solution must be able to address the
need for optimization on multiple scales. In the time domain, for
example, this includes the very short timescales arising from the
changing subscriber behavior during the day along with short-
term infrastructure failures and impairments. It also includes the
longer timescales of dealing with the trends in changing subscriber
behavior. In the spatial domain, the SON solution must employ
surgical precision to deal with localized phenomena, such as transient
congestion or changing subscriber characteristics throughout the day.
Coupled with this is the need for a wider view to find solutions that
improve performance across larger clusters of hundreds of cells.
A SON solution that cannot discriminate between the varying
needs of the subscriber population and different applications will
have limited scope to act. The QoS expectations will vary radically
between the different types of subscribers. At one extreme is smart
meters, providing background readings characterized by small
amounts of data infrequently and high tolerance to latency in
fixed locations.
The other extreme is the critical first responder who needs higher
data rates with low latency and very high reliability in unpredictable
locations. When a SON solution offers visibility down to individual
subscribers, it can direct performance for the best result. It can use
the information about the type of subscriber, where they are, what
services they are attempting to use, and what constitutes satisfactory
QoS for that service. It can use that information to make decisionsabout how to configure the RAN for routine operations.
Coupled with the need for subscriber awareness is the ability to
calculate the subscribers’ locations with sufficient accuracy to
90% of the data is
consumed in less
than 5% of the area
90% of the data is
consumed in less
than 5% of the area
50% of the data is
consumed in less
than 0.35% of the
area
50% of the data is
consumed in less
than 0.35% of the
area
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
determine the problem’s location. Location awareness facilitates the
shaping of radio resources to deliver services where needed and in
a way that subscribers will notice an enhanced service. This requires
the ability to geolocate significantly more accurately than cell-levelresolution and, in fact, must be to building-level accuracy, as shown
in Figure 3.
Figure 3. Estimates of the mobile locations are required to
building-level accuracy
As well as being able to tune performance to the subscriber, services,
and locations that are most critical for the operator, subscriber
visibility enables you to respond to impairments and failures to
mitigate their impact on high-value subscribers, especially VIPs and
emergency services workers.
The capability for self-learning is a key attribute of a SON solution,
because how the network and the subscribers using it behave and
respond to changes is complex. This coupled with the wide variety
of networks in existence mean that the ways that each network
responds to changes will, to some degree, be unique to that network
and subscriber base. A SON solution must acknowledge this and be
able to learn from experience, which can be achieved in a variety of
ways. For example, self-learning can take into account the historic
behavior of the network and the subscribers to anticipate the future.
This allows it to change the configuration preemptively to deal with
demand changes throughout the day, because the highest load
typically occurs at a similar time each day.
It also has applications for special events, such as sports games
or concerts, where behavior is unusual with respect to a normal
day; but there is similarity between network behavior during the
different events. Self-learning also encompasses the ability to make
exploratory changes, understand the response to those changes,
and use that information as part of future decision- making. This
implies a stateful SON and has applications in coverage and capacity
optimization, for example. Self-configuration is another area that
benefits from self-learning. One goal of self-configuration is to
ensure that a new resource’s configuration, such as a site or carrier,
converges to its optimum quickly. If a SON solution can determinefrom past experience what parameters are suited to a new resource,
it will reduce the cycle time for convergence.
A flexible SON solution can redesign the network for specific
operator goals which will vary from region to region, depending on
such things as the subscriber numbers, terrain, available investment,
and local competition. Sometimes operators place importance on
certain performance measures, for example, some mix of coverage,
quality, and capacity. Other goals will be more business related,
such as providing the best quality of experience (QoE) for certain
differentiating services. At the extreme, the goals will be financially
based, for example, reducing operating expenses (OpEx) by saving
energy. Ultimately operators are dependent upon revenue tounderpin their business operations. In turn, a SON solution must
be revenue- aware; that is, it must satisfy the subscriber’s need for
service with sufficient QoE to prevent churn yet also allow them to
consume, and pay for, the services they want. Thus a flexible SON is
also a revenue- aware SON.
Selected SON Examples
There are many examples of how SON is evolving to satisfy use cases
in ways that address the points described in the previous section.
Here we review some of these use cases.
Subscriber-aware self-healingA typical use case for SON systems is self-healing, which detects
the failure or impairment of one or more network infrastructure
elements, taking carriers or sites out of service either completely or
partially. Some users previously served by the impaired infrastructure
will be unable to obtain service due to being in a transient coverage
hole. Other users will be able to obtain service from nearby cells that
have not been taken out of service. The impact on those users who
have lost service is clear and significant. The impact on the users still
able to obtain service will be less serious but can still be significant.
For example, the remaining infrastructure will be carrying more user
traffic, which can lead to congestion that affects users not previously
served by the failed infrastructure, as their serving cell is carryingmore traffic than before the impairment. Another phenomenon is
that some users will now get service from cells receiving lower signal
strength or signal-to-noise ratio (S/N). Therefore, they may be unable
to achieve the same high data throughput as they did previously.
Not only can this negatively impact the user experience, it can also
compound the congestion problem described above.
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
Self-healing can mitigate these outage effects by managing and
extending the coverage of the remaining infrastructure to provide
rescue coverage. This self-healing involves identifying donor cells and
making changes to their parameters to extend their coverage into theareas not serviced due to the impairment. Increasing the power of
the common pilot channel (CPICH) or reference signal will temporarily
increase coverage along with uptilting antennas. Together these
changes provide rescue coverage for the users that would otherwise
fall into a transient coverage hole.
This traditional type of self-healing can mitigate coverage loss arising
from impairments to the network infrastructure. However, this
remedy is a resolution for the general population. Modern cellular
networks don’t serve one subscriber type using a single service.
Rather, they serve a heterogeneous mix of subscribers from pre-
paid to post-paid, corporate and retail subscribers, with low and
high utilization. Some networks even carry traffic with mission-critical applications like emergency services for first responders. The
applications that subscribers use are now diverse with widely varying
requirements on what performance measures, for example, data rates
and retainability will constitute reasonable QoE. The applications
that subscribers use are diverse with widely varying requirements
on what performance measures will constitute reasonable QoE. For
example, subscribers using e-mail are more tolerant of data rate
variations and occasional dropped connections than subscribers using
voice over LTE (VoLTE) services. Service degradation can also impact
service level agreements (SLAs) for mission-critical users.
When self-healing responds to a network impairment without
considering the subscribers it serves, it can sometimes havesignificant side effects. For example, a donor cell is adjusted to
increase its coverage and additionally serve subscribers who
otherwise no longer have service. However, if an emergency-service
worker is being served by that donor cell, the effect of reconfiguring
the network to mitigate the outage can induce congestion on that
donor cell, resulting in congestion that negatively impacts the
emergency-service first responder.
Other effects may also impact the high-value subscriber. For example,
subscribers being served by less optimal cells can result in increased
power in the system. The increased interference in the system often
lowers S/N and impairs the ability to achieve higher data rates.
For example, Figure 4 shows a network where two sites, marked inred, experience an unplanned outage. The cells marked in green are
those that self-healing identifies as donor cells. Self-healing detects
an impairment in the cells’ ability to provide coverage and applies
changes to the donor cells to provide rescue coverage. In this case,
an emergency-service first responder subscriber is located within the
coverage area of the cell marked with a red circle.
Figure 4. Helper cells (green) in the standard self-healing response to
mitigate outages at the cells shown in red. Critical subscriber is served by
circled cell.
Introducing subscriber awareness reduces the impact on key high-
value subscribers. Subscriber-aware self-healing uses information
about the active subscribers on candidate donor cells before allowing
them to be modified to provide rescue coverage. Candidate donor
cells serving high-value subscribers are excluded from the list of
donor cells that can be optimized to provide rescue coverage. Also,
self-healing addresses the risk for congestion arising from the rescue
coverage and its impact on high-value subscribers. This approach of
excluding cells providing coverage to high-value subscribers is shown
in Figure 5. The candidate donor cell restricted from being changed is
shown in orange.
Figure 5. Helper cells (green) and a cell that is blocked from being
a helper cell (orange) because it is serving a high-value subscriber.
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
By identifying that a cell is serving a critical subscriber, and thus
preventing that cell from helping to provide rescue coverage, the risk
of that subscriber experiencing congestion is reduced. Restricting
a cell from being a donor cell because it is serving a high-valuesubscriber has other advantages. For example, the radio signal quality
often improves for high-value subscribers by reducing additional
interference introduced into the system, as illustrated in Figure 6. This
shows the cumulative distribution functions of the pilot-received
S/N for the emergency-services first responder. This distribution
is shown in various scenarios, such as for the normal, pre-outage
scenario along with the unmitigated outage scenario. The impact of
the outage significantly degrading the S/N is clear. This degrades the
ability of the first responder to achieve higher data rates and may, in
extreme circumstances, threaten the ability to maintain a connection.
It shows the impact of regular self-healing for the first responder.
The self- healing provides some improvement; however, there is still
some degradation from the S/N achieved prior to the impairment.
However, once the subscriber-aware self-healing is deployed the
S/N returns to pre-impairment levels, or even improves marginally.
The improved S/N occurs in addition to other positive factors for the
critical subscriber, such as resilience to congestion stemming from
excluding the serving cell from the list of candidate donor cells and
without being modified to offer rescue coverage.
Figure 6. Cumulative distribution functions of pilot Ec/N0
for emergency-service first responders in various scenarios.
Subscriber-aware self-healing relies on a data feed from the
infrastructure indicating which network elements are providing
service to the critical subscribers. The feed can be monitored during
an outage so that in cases where critical subscribers are moving
around an impaired region of the network, the self-healing can adapt
dynamically to the movement and dynamically update the candidate
donor cells list in response to it.
A synthesis of SON and subscriber-centric optimization
We have described the integration of per-subscriber data with SON
use cases to yield enhanced capabilities, such as the subscriber-
aware self-healing. This is one example of how limited amounts of
per-subscriber data can be used to enhance classic SON use cases.
However, there are degrees to which per-subscriber data can be
used within SON. For example, subscriber-centric optimization can
predict the impact on the subscriber base of supposed parameter
changes. Therefore, it can select new parameterizations across whole
clusters of dozens or hundreds of cells for substantial performance
improvements. This concept is described in the white paper:
Harnessing Subscriber-Centric Optimization for the Next-Generation
of Self-Organizing Networks. This approach can deliver double-digit
improvements in a wide variety of performance measures that are
critical to the subscriber experience. Operators can configure the
optimization algorithms to reflect their goals for the network region.
Subscriber-centric optimization is a powerful capability that doesn’t
fit neatly into traditional SON use cases, because it optimizes large
clusters of cells at once leading to longer cycle times than making
changes with traditional SON use cases. Using subscriber-centric
optimization as part of a real-time self-healing solution is compelling
because the approach has proven it can achieve coverage goals. A
case study that demonstrates this deals with a cluster of over 350 3G
cells on which subscriber-centric optimization was performed, and
the changes to the CPICH powers and antenna tilts actuated to the
network significantly improving the average RSCP for each cell.
Figure 7 compares the RSCP distribution before and after actuation.
In addition to significantly improved received signal strength, serviceutilization increased by 23%.
Figure 7. Distribution of mean RSCP per cell in the optimization cluster
before and after subscriber-centric optimization, showing a significant
increase as a result of the optimization activity.
0
0.05
0.1
0.15
0.2
0.25
–100 –80 –60 –40 –20 0
RSCP (dBm)
Before After
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
Subscriber-centric optimization plays a role despite the fact that
longer cycle times are required than for standard SON use cases with
localized scope. In self-healing, especially when critical subscribers
are involved, reacting to the impairment as soon as possible isessential, therefore, using pre-calculated subscriber-centric solutions
is the solution. A key capability of subscriber-centric optimization
is its ability to optimize “what if” scenarios by supplying optimized
parameters to scenarios where network elements impairments
are simulated. In this way the impairment is mitigated before
customers actually experience them. Therefore, parameter designs
can be precalculated and stored until required, using the strength
of subscriber-centric optimization. As soon as the impairment is
detected, the appropriate precomputed parameter design is retrieved
and deployed to mitigate it instantly.
Selective optimization towards critical subscribers
As well as tuning network parameters toward the applications being
used, when and where they are used, subscriber-centric optimization
can tune performance so that its gains are focused or biased toward
particular groups of critical subscribers. This is in contrast to the
subscriber-aware self-healing which can avoid situations that often
degrade performance for critical emergency-service subscribers. In
contrast to degradation avoidance, subscriber-centric optimization
finds configurations that improve performance and coverage for self-
healing, but prefers configurations that provide optimal performance
for critical subscribers.
This selective optimization technique clearly has applications in
self-healing, where optimization gives preferential consideration to
critical subscribers. However, there are wider applications for using
subscriber-centric data. Optimization can be focused on any group
of the subscriber population. For example, affording preference to
subscribers depending on the services they use, the tariff they are
on, whether they are roamers, where they are located, or whether
they are indoors or outdoors. Table 1 gives more optimization
examples based on connection type.
Table 1. Different connection type classes in which different optimization
focus can be provided.
Connection Type Example Use Case
Critical subscriber Improve service for first responders, mission-
critical workersService type Improve service for VoLTE and video connections
Location Optimize connections in specific buildings, for
example, corporate headquarters
Route Ensure good service on specific roads or on trains
Subscription type Customized QoS for corporate customers, roam-
ers, pre-paid, post-paid, and others
Speed More resilient connections to support subscribers
in vehicles
Device capability Service tailored to those devices unable to useother network layers
One example concerns optimizing particular service types because
of their resilience to adverse conditions like jitter or their high
probability for dropped connections. For example, subscribers rarely
notice a transient connection drop while using an e-mail application,but they usually notice connection failures that occur during a VoLTE
call. Selective optimization capitalizes on the different characteristics
between the critical subscribers and general subscribers, making
changes to improve service where VoLTE services are often used
while maintaining performance where they are seldom used. This
approach can improve performance for the target application while
maintaining performance for other network users.
A case study illustrates this where an operator wanted to improve
VoLTE connection retainability while maintaining performance for
other connections. Figure 8 shows how the optimization improved
significantly the RSRQ for the VoLTE connections. Here the
distribution of RSRQ (signal to noise ratio) is shifted to the right forVoLTE connections which results in better quality.
Figure 8. Cumulative distribution shift in the function of S/N (RSRQ)
for VoLTE connections before and after optimization
Table 2 shows the performance measures changes for VoLTE and
all connections after actuating the optimized network parameters.
Notice the improved S/N after optimization, showing a 20%
improvement in retainability while other measures remain flat, which
was exactly what the operator wanted to accomplish.
Table 2. VoLTE performance measures before and after optimization
show that optimization improvement is successfully targeted at VoLTE
retainability, as required
Baseline After
Accessability 99.90% 99.92%
Accessability (VoLTE) 99.82% 99.82%
Retainability 99.44% 99.46%
Retainability (VoLTE) 97.48% 98.03%
Mean throughput 6.56 7.46
0
0.2
0.4
0.6
0.8
1
RSRQ CDF (dB)
Baseline After
–18 –16 –14 –12 –10 –8 –6 –4 –2 0
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Solving the Challenges of Cellular RAN Management with Next-Generation SON
Bringing subscriber-centricity to other SON use cases
The previous discussion demonstrated how subscriber-centric data
can enhance the self-healing SON use case. Other use cases similarly
can be enhanced with per-subscriber data. For example, a key
coverage and capacity optimization (CCO) application is to minimize
occurrences of locations with poor coverage. Typically this use case
employs network statistics to detect coverage holes and then to take
corrective action to close them. However, in the absence of per-
subscriber data, the hole’s location can only be crudely located to the
precision of the cell coverage area.
The algorithm employs exploratory changes to incrementally improve
coverage, often requiring several iterations before finding the
optimum configuration. Conversely, when data for each subscriber
are available, these can be exploited using the CCO process, which
calculates the locations based on where the data were generated.
When the data include signal strength and quality measurements
as well as events that characterize poor coverage, these locations
will provide additional information about the coverage hole. By
considering the coverage hole’s location along with the antenna
directions, the SON algorithm can calculate which sector or sectors
can best address the coverage hole. The benefit of this is that it
significantly reduces the number of iterations required to find the
optimum solution.
The selective-optimization concept also applies to use case
enhancements where impaired coverage can be selectively addressed
based on the connections they affect. For example, coverage holes
for critical subscribers or particular services or locations receive higher
priority than connections not meeting these criteria.
Per-subscriber data and the selective-optimization concept can
enhance other SON use cases, such as the mobility robustness
optimization (MRO), which selectively focus on too early, too late,
and wrong cell handovers that affect critical subscribers. They also
focus on specific connections rather than the whole subscriber
population.
Yet another example is the automated neighbor relations (ANR) use
case that creates neighbor lists to increase the likelihood that phones
can find neighbor cells to which they can hand over to or add to the
active set quickly to reduce instances of dropped calls. Selectively
considering appropriate neighbors of critical subscribers in preference
to regular subscribers increases the likelihood that critical subscribers
will perform successful handovers.
Problem and opportunity detection
Modern networks are large and complex. Some of the SON actions
with maximum impact also require substantial computation power.
While running SON use cases across the whole network all the time
may seem ideal, in reality it requires a substantial computation
investment. To avoid this massive computation capability investment
requires a selective optimization approach. Some scenarios are
naturally selective; there is only ever value in applying self-healing
when a network is experiencing an impairment, and this limits the
computational investment. Other scenarios, however, require more
nuance like during congestion manifesting as packet delay, loss, or
exhaustion of physical radio resources across large network areas and
can vary from hour to hour or even minute to minute. Given that
computation resources are limited, the issue becomes determining
the best way to deploy the optimization resource to mitigate
the congestion.
Coping with this problem requires a solution that can detect
instances of congestion, characterize the extent that it presents a
problem for subscribers, and prioritize them for by being addressed
by coverage and capacity optimization mitigation. For example, the
degree to which the congestion is prevailing can vary from highly
transient congestion to constant capacity exhaustion. A series of
fleeting capacity exhaustion events will be less serious than more
prolonged congestion.
Another consideration is the connection types affected by the
congestion. Impacting high-value subscribers is more concerning
than impacting lower-value subscribers; whereas, the impact to
mission-critical subscribers is the most serious. Similarly, degradationon VoLTE connections is more serious than a similar impairment to
connections used for background e-mail. It is important to consider
the degree to which a problem can be mitigated. Some congestion
problems can be alleviated through optimization. Others may exist
in highly optimized areas where further optimization adds little
or no extra capacity. The former case is an ideal target for a SON
optimization. However, in the latter case, nothing is gained by
applying a SON solution to the problem.
Figure 9. The critical quadrant is the target that problem
and opportunity detection seeks to find.
Degradation High
Potential SON impact Low
Degradation Low
Potential SON impact Low
Degradation High
Potential SON impact High
Degradation Low
Potential SON impact High
Potential Impact
D e g r a d a t i o n
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© Viavi Solutions Inc.Product specifications and descriptions in thisdocument are subject to change without notice.nextgen-son-wp-nsd-nse-ae
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To reach the Viavi office nearest you,visit viavisolutions.com/contacts.
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The capability to discern the clusters that are most deserving of
optimization attention requires several factors. Subscriber-centric
data are needed to understand whether high-value subscribers are
affected and the types of service that are impaired. It also requiresthe ability to estimate the mitigation level that might be achieved,
which in turn requires a predictive capability so that the need to
perform full optimization up front is not required. This is the basis
for problem and opportunity detection; the ability to detect network
problems with the most meaningful impact on subscribers to focus
mitigation efforts on areas experiencing problems with the greatest
potential to significantly improve.
SON on the path to 5G
The move toward 5G forces the industry to grapple with some
significant challenges. Adoption of LTE Advanced by the industry
brings complex new features where SON offers significant
opportunity. The Carrier Aggregation feature provides an additional
dimension where optimization benefits from looking beyond each
individual carrier in isolation. Here SON must consider the device’s
capabilities to determine which devices can exploit the aggregation
and to what extent. Doing so maximizes the value of the carrier
aggregation and significantly increases the network’s capacity The
Coordinated Multi-Point (CoMP) feature of LTE Advanced is an
example of coordinated transmission and reception schemes that
improve cell-edge performance and raise network coverage. However,
these features can place large demands on a fronthaul network
the more they are used.Thus the need to optimize coordinated
transmission and reception utilization to achieve RF performance
goals while remaining within the fronthaul cloud capacity constraintswill become a capability of future SON systems.
Realizing a flexible and effective SON
In summary, a comprehensive SON solution must be able to address
a range of poor network performance issues flexibly to address
the operator’s business priorities. It should deal with transient
impairments while maintaining and improving the network in its
nominal state. It has to address problems on a range of scales, from
solving localized problems with surgical precision to driving up
performance across whole clusters. Furthermore, it should reduce
its cycle times by predicting the impact of the changes before
making them and should also learn how the network responds to
optimization. The granularity of visibility down to the resolution of
the individual connection event along with its location enables the
solution to focus on driving performance that simultaneously gives
subscribers the most appropriate QoE for the services they are using
while employing the necessary revenue-awareness for the operator’s
business case.
These characteristics are solid foundations for many aspects of the
5G networks of the future. Wider ranges of applications including
mission-critical and high data rate, low latency applications will
require a solution that can respond dynamically to which services
are in demand, by which subscribers, and in what locations. SON in
the access network and orchestration in software-defined networks
in the core network will converge toward an end-to-end SON which
recognizes and exploits the fact that changes in one part of the
network effects other parts of the network. The ability to exploit this
will be a hallmark of the SON of the future embedded within the 5G
networks of tomorrow.