20160621_NextGen-SON-whitepaper-VIAVI.pdf

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



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




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.




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.




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




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




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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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.

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