Reaching_5G_Plateau_of_Productivity_ Guavus_eBook_2019_.pdf

eBook
Reaching 5G’s “Plateau of Productivity”
Big Data Analytics, Machine Learning & AI are Critical for
Service Providers to Master 5G Operational Complexity

Gartner Hype Cycle
Gartner's Hype Cycle tracks market expectations for
emerging technologies, starting with an initial period of
“inflated expectations” that peaks before sliding into the
inevitable "trough of disillusionment”, where visions are
dashed on the shores of market reality. However, new
technologies eventually gain a foothold when mainstream
customers adopt viable products that solve real-world
problems, climbing the “slope of enlightenment” before
ultimately reaching the “plateau of productivity”.
Gartner's hype cycle is only a qualitative measure of
market perception vs. reality, but it provides a frame of
reference for potential adopters to decide when a
technology has matured to the point where it can deliver
real business value.
!
Source Link:
https://www.gartner.com/en/research/methodologies/gartner-hype-cycle 2

5G Hype Cycle
In the global telecom market, no technology has been as heavily hyped as much as 5G, with both service
providers and vendors predicting that 5G networks will enable a new generation of high-bandwidth,
ultra-reliable, massively scalable mobile services. So where is 5G in its hype cycle today? Is it past the peak
of inflated expectations and headed towards the trough of disillusionment? Hard to say, as leading mobile
operators around the world are just starting to roll out the first 5G services on a limited basis. However,
visions of 5G-enabled self-driving cars, smart cities, augmented reality and 4K gaming are still well over the
horizon. A period of disillusionment is inevitable, as service providers and suppliers grapple with the harsh
realities of productizing and deploying complex 5G technologies at scale, and users seek to derive
compelling value from 5G services.
Eventually, as suppliers provide mature, robust products, operators will work out the kinks, leading the
market up the slope of enlightenment.While pundits might debate 5G's exact position in the hype cycle,
given the nascent state of the market, even the most enthusiastic booster will admit that 5G is still a long
way from reaching its plateau of productivity.
!
3

5G Operational Complexity
5G is multi-faceted, leveraging multiple technology innovations that are projected to enable a whole host of
innovative 5G services, applications and use cases.Yet these new technologies will drive a steep increase in
complexity compared to current 4G networks, resulting in significant operational challenges for ensuring the
performance, reliability and security of the underlying 5G infrastructure.The sheer scale, scope and speed of
5G networks will outstrip the capabilities of existing operational tools and technologies. As service
providers gain firsthand experience with the 5G infrastructure and enabling technologies, operations teams
will adopt new tools, technologies and best practices for overcoming the attendant operational complexity.
!
4

Apply Machine Intelligence Across
Multiple Domains to Master 5G Complexity
This eBook describes the leading role that machine intelligence will play in enabling service providers to
master 5G complexity and reach 5G’s plateau of productivity. 5G service provider operations span four
main domains: subscriber, service, edge and core. Machine intelligence, applied across all domains, will power
the business applications employed by different operations teams and key stakeholders. Operator tools will
utilize Big Data analytics, machine learning (ML) and artificial intelligence (AI) to generate real-time insights
that enable right-time decisions and drive closed-loop actions. Service providers will benefit from significant
gains in operational efficiencies that will reduce OPEX, while ensuring high-quality, 360-degree customer
experience.
!
5

Reaching 5G’s “Plateau of Productivity” 6
!

5G Gains in Network Performance,
Capacity & Efficiency
5G promises big gains in network performance, capacity and efficiency, starting with very low end-to-end
latency and increased connection density. Small cell proliferation and multi-gigabit throughput will
combine for a major step up in overall network capacity. New modulation techniques will lead to gains in
spectrum efficiency and another goal is to make 5G networks far more energy efficient than 4G and 3G.

5G Performance & Efficiency Gains
Graphic source: Visual Capitalist
https://www.visualcapitalist.com/5g-next-generation-mobile-connectivity/ 8

A New Generation of 5G Services & Use Cases
Graphic source: ITU News
https://twitter.com/itu/status/1052210124490776576
The GSMA envisions these dramatic gains powering new
consumer, business and machine-to-machine (M2M) applications
and usage scenarios enabled by three classes of mobile services:
• Enhanced Mobile Broadband (eMBB)
• Massive Machine Type Communications (mMTC)
• Ultra-Reliable and Low Latency Communications (URLLC)
Multi-gigabit connections will enable high-speed mobile
applications such as 4K video streaming and gaming. Efficient
mechanisms for connecting a massive number of devices will
support consumer, enterprise and public infrastructure IoT
applications. Ultra-reliable, low latency connectivity will power
mission critical applications in markets including healthcare,
public safety and industrial automation.

10
5G Leads to Operational Challenges
Across All Domains
Although 5G may be over-hyped today, its future promise is real. But so are the operational challenges.
Delivering a new generation of advanced mobile services for leading edge applications and use cases will
stress the ability of network operators to manage the array of enabling hardware and software technologies
in the underlying infrastructure.The next four sections highlight the types of challenges operations teams
will face in each 5G domain.

Subscriber Domain Challenges
5G will inherit the billions of smartphone users on today’s 4G networks, but gigabit connections will
spawn high-speed applications driving a new generation of smartphones and consumer devices capable of
utilizing the huge increase in bandwidth. User quality-of-experience (QoE) will depend on the mobile
operator’s ability to ensure the reliability of these connections and that network quality-of-service (QoS)
meets the demands of each application. Operators will also require insight into the different types of
applications subscribers are using, including time of day, location and duration, in order to project future
usage and provide sufficient network capacity.
5G will unleash a proliferation of embedded, smart devices that will utilize ultra-reliable, low latency
communications channels for real-time, machine-to-machine (M2M) control of vehicles, homes, buildings,
public infrastructure and industrial processes. Mission critical applications with stringent performance
constraints will require constant monitoring and rapid remediation of service-affecting problems.This will
have to be performed automatically by machines as opposed to teams of human operators, who will not
be able to respond quickly enough to satisfy service level agreements (SLAs) for M2M communication.

Another game changer will be the ability of 5G networks to accommodate the massive number of IoT
devices and sensors that will be deployed for instrumenting the physical world for a broad range of IoT
use cases. Operators will have to transition to managing networks connecting 100s or 1000s of times as
many devices as today’s 4G networks. Just keeping track of this myriad of devices will be a Big Data
problem.
Beyond connectivity, IoT devices have the potential to swamp data centers in the cloud with a flood of
data, necessitating pre-processing of data at the network edge. Security is a related challenge, as hackers
and cyber criminals have proven quite adept at hijacking poorly secured IoT devices to create vast botnets
for launching large-scale distributed denial-of-service (DDoS) attacks.
Subscriber Domain Challenges

Service Domain Challenges
The diversity of 5G services and underlying complexity will create service operations challenges for 5G
that are far more daunting than in today’s 4G networks.The next generation of 5G services will extend
well beyond today’s one-size-fits-all voice, text, video and multi-megabit data services, supporting a diverse
array of 5G applications and use cases.
For example, mission critical applications in healthcare, public safety and industrial automation will require
ultra-reliable communications services with demanding SLAs. Service-aware network slicing will enable
the creation of virtual, end-to-end networks tailored to specific application requirements, so that
operators can deliver different types of services via a common physical infrastructure. For example, a
mobile virtual network operator (MVNO) could provide a specialized service targeting a specific vertical
market, delivered via a mobile network operator’s (MNO’s) physical network footprint and utilizing the
spectrum and bandwidth appropriate for customer applications in that market.

Real-time and data-intensive 5G applications are envisioned to require mobile edge computing
infrastructure – service-enabling software deployed in small data centers distributed at the 5G edge. Data
processing close to the source will reduce end-to-end latency for real-time applications and reduce the
flow of data traversing the backbone into the cloud.
Edge computing adds another layer of complexity to an inherently complex service delivery environment.
The edge data center infrastructure could be owned and operated by a 5G MNO, an MVNO, or by a third
party that manages the service-enabling software. In this scenario, assuring demanding SLAs for real-time
M2M communications services will involve coordination between service operations centers at different
service providers.
Service Domain Challenges

Edge Domain Challenges
The majority of breakthrough technologies powering 5G will be utilized in products at the edge of the
network. Compared to the 4G network edge, the 5G edge will be highly dense, diverse and dynamic,
combining an array of new enabling technologies in next generation 5G products to support
high-performance connections for smartphone users and smart devices, and also high-density connections
for IoT devices. Deploying 5G at scale will require service providers to master a level of operational
complexity at the edge that is well beyond the scope of 4G.
5G New Radio will operate in high-frequency (3-6 GHz) spectrum bands, using new modulation schemes
and innovative antenna techniques to increase link capacity. Beamforming and massive multiple-input,
multiple-output (mMIMO) technologies underpin the ability of 5G networks to deliver multi-gigabit
connections.
5G radios operate at high frequencies that are distance-limited, requiring operators to deploy dense
networks of small cells to serve up high-speed radio connections.To cover a given geographic area, small
cell densification will increase the number of 5G radios deployed by a factor of 100 compared to 4G
macro cells.

Beam steering and mMIMO techniques deployed on 100s of densely packed small cells connecting a
diverse assortment of 1000s of moving 5G devices has all the ingredients of a highly intriguing research
project.Yet service providers must operationalize these technologies.
Multi-gigabit connections will drive the need for a massive increase in 5G fronthaul and backhaul
bandwidth, which will be supplied by optical networks to support the increase in capacity. Shifting network
demand and traffic patterns will require dynamic allocation of bandwidth for fronthaul/backhaul capacity,
introducing another layer of complexity.
RAN virtualization technology will enable operators to deploy a more efficient, agile 5G edge, providing
the basis for new capabilities such as network slicing. However, overlaying multiple classes of service on
the underlying physical network infrastructure will inevitably complicate the work of edge and service
domain operations teams.
Edge Domain Challenges

Core Domain Challenges
The 5G core will undergo a fundamental transformation as service providers adopt software-defined networking
(SDN) and network functions virtualization (NFV) for service-enabling infrastructure. SDN/NFV will support
virtual network functions (VNFs) implemented in software, utilizing cost-effective, commercial off-the-shelf (COTS)
hardware in place of purpose-built products based on vendor-proprietary hardware. 5G’s Service Based
Architecture (SBA) defines a full complement of core network functions needed to support 5G service delivery,
which will be implemented asVNFs.The 5G Core (5GC) will also be virtualized, utilizing high-capacity SDN switches
and routers based on merchant silicon.
Early adopters are gaining experience with SDN/NFV in limited-scale deployments, but for 5G to deliver on its
promise, mainstream adopters will need to master the complexity of operating this new generation of core
network infrastructure.
4G and 5G are expected to co-exist for 5 to 10 years, so interworking between 4G and 5G network functions in
the core is a critical requirement that further complicates network and service operations. During the period of
co-existence, the installed base of smartphone subscribers must be able to roam seamlessly between areas of 4G
and 5G coverage. Service providers will be challenged to operate a legacy 4G network in parallel with a new 5G
network, while maintaining subscriber QoE across both domains.

Multi-Domain Challenges Threaten To
Overwhelm Human Operators
Across all 5G domains, challenges rooted in scale and complexity threaten to outstrip the ability of operations teams to keep pace by
relying on human operator workflows to manage customer experience, assure SLAs and operate the underlying service-enabling
infrastructure at the edge and in the core.Typical operator-intensive workflows for managing the delivery of 4G services are likely to be
too slow and cumbersome when applied in the corresponding 5G domain. Supporting ultra-reliable services will require real-time
correlation of information across domains in order to immediately detect and remediate service-affecting and customer-impacting issues.
There simply won’t be any time for human operators to interact in the way operations teams have grown accustomed to in today’s 4G
environments.
When a problem occurs in a 5G environment, whether a fault or performance anomaly, the symptoms could be manifested across
multiple operational domains.With 4G, human operators already struggle to sift through an overwhelming amount of alarms, key
performance indicators (KPIs) and log data generated in multiple operational silos.The flood of telemetry data intended to assist
operators can actually mask the root cause long enough to result in a service-impacting event of unacceptable duration.
5G’s vast scale will result in the generation of a huge volume of monitoring data that will need to be processed and analyzed
in real time. 5G’s inherent complexity across multiple domains will make it difficult, if not impossible, for teams of human
operators to identify and correlate between the critical data points needed to determine the root cause in real time.

Machine Intelligence To The Rescue
Building Blocks of Machine Intelligence
Instrumentation
To overcome the daunting, multi-domain operational challenges posed by 5G, service providers will be compelled to apply machine
intelligence – Big Data analytics, ML and AI – to streamline and automate human operator workflows. Operations teams will utilize a new set
of building blocks, assembled to provide continuous visibility into the current state of multiple 5G operational silos across all domains, and
use that information to generate actionable insights in real time.The ultimate goal is a 5G operational environment where machines are able
to fix machines, automatically.
Machine intelligence relies on many types of data, including machine
data generated in log files and KPIs measured by software
instrumentation.Wire data provides visibility into the metadata and
content for packet flows. Passive and active monitoring agents and
probes can generate data about the state and performance of
network connections, devices, user applications and services.
Streaming Telemetry
Real-time machine intelligence requires real-time data, and streaming
telemetry protocols play a critical role enabling the collection of
instrumentation data in real time.A high-performance
publish/subscribe message bus at the collector enables high-volume
ingestion of streaming telemetry by Big Data platforms.
Big Data Analytics
Big Data platforms are able to ingest, analyze and store a huge volume and
wide variety of data at high velocity. Streaming analytics engines can glean
actionable insights from Big Data consumed in real time. Descriptive
analytics performed on Big Data repositories of historical data provides
insights into past events, while predictive analytics can be used to perform
statistical analysis to determine trends and help predict future events.
Machine Learning
Vast amounts of raw telemetry data can easily overwhelm human
operators, so machine learning algorithms play a primary role in sifting
through a flood of streaming telemetry to detect the signal buried in
the noise. Machine learning algorithms applied to time series data can
identify data clusters and outliers to automatically detect anomalies.
Advanced, supervised machine learning can detect patterns in large
data sets to identify features not readily apparent to the human mind.
AI
AI algorithms reside at the top of the machine
intelligence pyramid. AI includes deep learning
based on neural networks that can be trained to
recognize patterns and identify features in
large-scale data sets. Reinforcement learning is
an AI technique in which a system trains itself to
improve over time by constantly measuring the
values of system outputs in response to varying
inputs. AI is a new realm of advanced
mathematics, algorithms that transcend statistical
analysis and self-learning systems that can
determine how to take action automatically,
driven by more than a set of rules.

Applying Machine Intelligence Within 5G
Operational Silos
Machine intelligence will be needed for 5G networks to be self-optimizing and self-healing. Dense
networks of small cells with advanced antenna technologies will need to be continuously tuned
based on the devices connected, application data flow, bandwidth utilization and RF signal analysis.
Fronthaul and backhaul capacity will need to be managed dynamically based on varying bandwidth
demand and network utilization. Network failures will be remediated automatically, applying
machine intelligence to detect and isolate the root cause and then determine the appropriate fix
or workaround, which may involve reallocating network resources or shifting network demand
onto other resources in the underlying infrastructure.

Applying Machine Intelligence
Across 5G Domains
AI will play a critical role in enabling operations teams to rapidly detect, isolate and remediate problems that are manifested by events
triggered across multiple 5G domains. AI algorithms will enable operators to take insights gleaned from individual operational silos and
correlate those data points across multiple domains. For example, a group of subscribers report poor application performance while an
alert indicates the backhaul network is experiencing a bottleneck, but the root cause is actually a malfunction in a small cell that is flooding
the network with a stream of bad packets.
Without machine intelligence, different operations teams will investigate the same incident by accessing multiple dashboards and examining
various log files to determine what is happening within their silo.Then only after sharing information between teams, investigating further
and ruling out possible root causes will they arrive at a definitive explanation.
The intent of applying machine intelligence is to remove human operators from this process as much as possible, leveraging operations
tools that utilize ML and AI to perform the required data analysis and correlation of insights across multiple domains.The goal is not only
to have machines figure out what is happening, but to automatically take corrective action.While operators may deem this to be too risky,
for many scenarios in 5G networks, this will be a necessity. Services for ultra-reliable machine-to-machine communication will depend on
the ability of machines to fix machines.

SUBSCRIBER
EDGE
SERVICE
CORE
Applying Machine Intelligence Across Multiple Domains

Usage Scenario: 5G Marketing Analytics
The diversity of 5G service offerings presents a challenge for marketing
teams charged with maximizing customer value. How will operators
align subscribers with the right set of services tailored for individual
customer needs? How will marketing gather the service usage and
subscriber behavioral data needed to conceive and plan new products
and services? How will operations teams track subscriber activity in
real time to target customers with personalized offers on-the-fly?
How will operators aggregate and monetize usage and behavioral data
to generate new revenue streams?
5G service providers will employ marketing analytics to realize these
goals, but the task will be complicated by the breadth of service
offerings and target market segments, by a subscriber base that will
include both human users and smart machines, and by the complexity
of the underlying network infrastructure, which will support a wide
range of connection types and devices. Operators will need to leverage
machine intelligence to gain real-time insights into customer behavior
so that the right offer can be presented to the right subscriber at the
right time.
5G marketing analytics will be data intensive and driven by Big Data sourced from
multiple domains. Machines will automatically build each subscriber’s profile by
tracking real-time edge-network interactions, which include content consumed,
applications used, time-of-day, duration, device and location, combined with static
properties, such as demographic and socio-economic data, past purchases,
customer experience history, and service subscriptions.Then by applying behavioral
analytics to subscriber profiles, machines will be able to automatically classify
subscribers by interests and affinities, whereby such classes will become target
market segments.This heavy lifting will require collecting, analyzing and correlating
data from operational silos within the subscriber, service, edge and core domains.
Operators will exploit subscriber behavioral analytics to automatically serve up ads
and present offers to subscribers in each target market segment in real time.
Subscribers will benefit from personalized promotions and services that better suit
their individual needs, while service providers will benefit by increasing
per-subscriber value, improving customer satisfaction and reducing
subscriber churn.
Challenge Opportunity

Usage Scenario: 5G Roaming
The advent of 5G places additional constraints on mobile operators
when selecting a visited network. A smartphone user who frequently
streams 4K video will have high expectations for the performance of an
enhanced mobile broadband service, whether delivered on the
subscriber’s home network or when roaming onto the network of
another operator. Ultra-reliable, low-latency M2M communications will
also have stringent QoS requirements that must be satisfied when a
smart device roams onto a visited network.
Mobile operators will be challenged to ensure QoE for roaming
subscribers. It will be critical for operators to continuously monitor the
state of highly dense, diverse and dynamic 5G networks to ensure that
users and machines can be guaranteed the network QoS required by a
new generation of demanding 5G applications. Support for roaming also
requires that home network operators be able to determine, in
real time, if the desired service on a potential visited network will be
able to deliver the QoS needed, and where the roaming subscriber
should connect to that network.
Ensuring QoE in 5G roaming scenarios starts with monitoring network
performance, reliability and capacity at the edge and in the core, so that the
operator can maintain real-time visibility into network state spanning both domains.
Roaming requires tracking subscriber usage and behavior, which involves analyzing
data generated within silos in both the subscriber and service domains.When
making a decision to select a visited network, an operator needs a complete
picture of the subscriber’s profile, service-specific QoS requirements and the
current state of the roaming network.
To facilitate 5G roaming, visited network operators will monitor network state in
real time by applying machine intelligence within multiple operational silos across
multiple domains. But visited network operators will also need to share this state
information with home network operators so that the latter will be able to select a
roaming network that can guarantee the network QoS needed for the subscriber’s
service. So 5G roaming requires an even broader application of machine
intelligence that extends beyond the environment of the home network operator
to gain visibility into the state of visited operator networks. Furthermore, after a
subscriber roams onto a visited network, the home network operator will need to
continue monitoring network QoS and switch the subscriber to a different visited
network if adequate QoS cannot be assured.
Challenge Opportunity

For service providers to successfully deploy 5G services, machine intelligence will power business applications for operational
stakeholders across all domains.
In the edge and core, stakeholders will include network operations, security operations and DevOps teams responsible for the
service-enabling software deployed on SDN/NFV infrastructure.
In the service domain, service operations and field operations teams will correlate insights generated from data in their own
operational silos with insights gleaned from different silos in the subscriber, edge and core domains.
Customer care in the subscriber domain will rely on its own data sources for managing customer experience, and will correlate
this with data sourced from silos in the service, edge and core domains.
Marketing teams will employ marketing analytics to track subscriber service usage, behavior and location data in order to present
customers with real-time, personalized offers promoting other services and applications, helping to drive revenue and reduce
subscriber churn.This will involve analyzing data from silos within the subscriber and edge domains.
Business Applications for Operational
Stakeholders Across All Domains

The ultimate goal is for operations teams to acquire
real-time intelligence that provides insights into
current operational status, and then use this
information to drive right-time decisions. Ultimately,
AI will drive closed-loop actions, whereby machines
automatically learn what is happening and take the
necessary corrective or preventative actions, without
the need for operator intervention or initiation.This
cycle can apply to a single operational silo within a
given domain or be applied across multiple domains
utilizing data sourced from different silos.
Real-Time Insights, Right-Time Decisions &
Closed-Loop Actions
Big Data
Analytics
Machine
Learning
Streaming
Telemetry
AI-Driven
Automation
Closed-Loop
Actions
Insights, Decisions and Actions for Operational Stakeholders

360-degree customer experience management goes beyond ensuring the performance, reliability and security of 5G
services.The term "360-degree” implies that all aspects of a subscriber’s experience with an operator's products
and services are monitored and managed to ensure total customer satisfaction.This includes customer interactions
with automated systems and personnel in sales and customer care.
Machine intelligence enables information to be correlated cross-domain with insights gained from monitoring
subscriber usage of applications, devices, networks and services to ensure the highest quality, 360-degree customer
experience. Service providers benefit by being able to immediately recognize and rapidly respond to any issues that
negatively impact customer experience, helping to reduce churn and steer subscribers into products and services
that better serve their needs.
Machine Intelligence Powers 360-Degree
Customer Experience

Nokia, a leading supplier of 5G equipment, software and services, has proclaimed that "5G is going to change
everything, every industry, every business, and every consumer experience.” Nokia may be right, but service providers
and suppliers have a lot of work to do in order to realize that vision.
Machine intelligence will play a critical role in enabling 5G service providers to master the operational complexity of
new enabling technologies, services, applications and use cases.This will play out across all 5G domains: subscriber,
service, edge and core.
The benefits of AI-powered operational intelligence will contribute to significant OPEX savings. Reducing OPEX in 4G
networks is already a major focus for all the leading mobile network operators.The urgency will be even greater for
5G, given the scale and complexity of the underlying infrastructure, and the significant CAPEX investment required.
Machine intelligence will enable operations teams to streamline and automate operational workflows, leading to better
outcomes while reducing the time expended in tedious and error-prone, manually-intensive processes. Operations
teams can’t afford to consume the precious time of highly skilled contributors that can be better utilized elsewhere.
The Path to 5G’s Plateau of Productivity

Operators will leverage machine intelligence to reduce the mean-time-to-detect and mean-time-to-repair faults and
anomalies. Real-time performance monitoring combined with closed-loop network monitoring will ensure QoE for
subscribers. Predictive analytics will enable operators to head off potential problems by taking timely preventative
action.The 5G edge domain will be highly complex and involve the continuous tuning and optimization of radio
spectrum, small cell capacity, fronthaul/backhaul bandwidth and virtual RAN resources.AI/ML-based analytics will be
applied in this domain to ensure quality of service for meeting SLAs and efficient utilization of the edge domain
infrastructure.
Knowing what lies ahead, the challenge for 5G network operators is to determine the right path to reach the plateau
of productivity.This starts by gaining an understanding of the operational complexities and then laying the groundwork
to leverage the power of Big Data and AI/ML through analytics.This path will by no means be straight up the slope of
enlightenment. No doubt it will be a tough climb, with several false peaks on the way. But eventually, applying machine
intelligence to master the complexity of 5G will enable the market to finally reach the plateau of productivity.
The Path to 5G’s Plateau of Productivity

Guavus is at the forefront of AI-based big data analytics and machine learning innovation, driving digital transformation
at 6 of the 7 world’s largest telecommunications providers. Using the Guavus Reflex® solution, customers can analyze
big data in real time and take decisive actions to lower costs, increase efficiencies, and dramatically improve the
end-to-end customer experience – all with the scale and security required by next-gen 5G and IoT networks. Guavus
enables service providers to leverage both customizable “self-service analytics” applications and out-of-the-box
analytics products for advanced network planning and operations, mobile traffic analytics, marketing, customer care,
security and IoT.
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