AI & Iot whitepaper

Created by Executives for Executives
The Next Generation
Consumer Experience
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AI Is The Brain, IoT Is The Body
AI & IoT
Telecommunications

The world’s economy is at another pivotal moment as artificial intelligence, the
Internet of Things (IoT), and augmented reality are transitioning from buzz words to
the basis for long-term national economic potential. The catalyst for this economic
growth is wireless connectivity enabled by 5G— a new standard for wireless
telecommunications. 5G is not simply an extension of 4G, nor is it merely a faster
wireless capability. 5G makes possible the connection and interaction of billions of
devices of almost any kind and collection of data from those devices. Indeed, 5G
connectivity promises to lead consumers, industries, and governments to new
frontiers of productivity and innovation.
The switch to AI-enabled 5G networks is happening now
Applying AI applications to 5G for the purposes of streamlining and customizing
services and increasing a provider's return on investment (ROI) is no longer a
futuristic fantasy, but a quickly evolving reality.
All around the world, service providers are in the midst of moving 5G forward
through planning, trials and operations. But the fluctuating complexities of this
exciting 5G revolution have sometimes presented a barrier to obtaining
actionable, reliable data.
The purpose of this report is to cover the fundamentals for those technologies,
enabling an easy understanding of these complex technologies and focusing in its
applications within the telecommunications industry.

Content
 Next Generation Customer Experience
 Augmented Reality Customer Experience
 Customer becoming stakeholders
 Episode Management
 Corporate Collaboration to Benefit the Customer
 Power to the People
 Artificial Intelligence Fundamentals
 Introduction & Background
 Overview
• Machine Learning
• Deep Learning
• Cognitive Computing
• Natural Language Processing
• Computer Vision
 Key Drivers of Artificial Intelligence
 Computer Power
 The continuous rise of Big Data
 Progress in Algorithms
 Corporate Collaboration to Benefit the Customer
 Power to the People
 Artificial Intelligence Opportunities
 Prediction and Prevention
 Emotional Approach for improved CX
 Faster Decision
 Automated Processes
 Main Focuses
 Core Technologies
 Al-Driven Analytics
 Intelligent Automation
 AI-Infused Interface
 AI Trends 2019-2020
 Overview
 AI Chatbot
 AI to move upstream in HR functions
 AI to Work Next to Humans
 Only 1 to 3 Percent Data Utilized
 AI for Personalization
 AI to transform Education Sector

Content
 Mobile VAS & AI
 Overview
 Smartphones
 5G Smartphones market overview
 5G and Artificial Intelligence
 2020 5G roll-out The fuel for AI
 AI in Telecom Network
• AI in SDV
• AI in NFV
• Application of AI in telecom
 AI’s role in automating networks
• How AI will integrate technologies
• AI and new revenue streams
• What does the future hold?
 What is IoT?
• IoT vs. IIoT
• IoT and Blockchain
• How can blockchain help in IoT?
 5G Fundamentals
 What is 5G
 What will 5G enable
 5G Architecture
 Technologies that will aid 5G, IoT and build future networks
 MIMO
 Narrowband IoT
 4.5G
 IoT vs. IIoT
 LoRa (Long Range)
 mmWave
 OFDM
 LTE-U
 Network slicing
 Massive MIMO
 HetNet
 5G Market Overview
 2026 Projection
 Future of 5G
 When is 5G launching
 Standard Patents and 5G technologies
 SOURCES

Next Generation
Customer Experience
Three key drivers are at the core of this. ROI is fundamental, as C-suite executives
at leading service providers demand measurable returns from their outlay. SaaS
suppliers must therefore evolve their customer-facing solutions, demonstrate
greater value and delivering bigger wins for less.
The influence of AI will continue to grow throughout the global economy,
capturing more space within the CX field as end users become more familiar with
fresh technologies and begin to expect more expertly tailored journeys.
A greater pivot towards self-service is the natural outcome. Answering the
pressing need for ROI, the do it yourself approach — powered by intelligent
virtual assistants — will continue to deflect the need for human interaction,
delivering huge cost savings.
Augmented Reality Customer Experience
Collaborative video calls between CS personnel and end users enable faster
resolution, significant truck roll reduction and improved NPS across the board.
AI-powered live video assistance covers the full user experience spectrum, from
unboxing and installation to troubleshooting and upgrading, creating better
customer experiences and slashing costs in the process.
CS personnel and intelligent virtual assistants can now add an additional layer
of visual information, pointing out the exact action the customer needs to take.
Customer becoming stakeholders
Incentivized surveys and refer-a-friend
programs, of the type that delivered
stunning exponential user growth for
Dropbox will give rise to more refined
models drawn from the world of B2B
sales. Rewarding customers for sharing
their experiences and transforming them
into committed social media brand
advocates will serve as effective upsell
enablers. Key to the success of scaling
such activities will be sophisticated AI
approaches for managing and
measuring metrics, both in terms of raw
data and textual analysis.

Episode Management
 Episode Management is now well established as a highly effective CX
management technique with joined-up thinking transforming many
companies’ approaches to specific issue resolution and next generation
customer experience.
 Customers essentially want a single point of contact. Transferring them
between departments is traditionally a significant pain point that causes
broken processes and severe dissatisfaction. This can be avoided by
seamlessly integrating different stages of each customer episode, shaving
precious time off the journey. The approach has the added benefit of
aligning various functions within the organization, driving internal efficiency
and delivering exceptional ROI.
 Servion has predicted that by 2025, some 95% of customer interactions will
be powered by AI. As virtual assistants rapidly replace many of the basic
functions of traditional support center staff, the technology is creating new
opportunities for fostering career development and broadening skill sets.
Companies can identify star performers and grow them as multi-faceted
support, sales and marketing professionals, while cutting the costs
associated with very large workforces.
Corporate Collaboration to Benefit the Customer
 Life’s a journey, and each trip involves interactions with multiple companies
from different industries such as travel, hospitality and retail. Busy consumers
will therefore come to demand greater synergy between service providers,
ultimately enabling them to arrange their entire schedule from a single
touchpoint.
 Booking a flight, a taxi, a hotel and a meal could soon become a single
transaction, and this will involve customers giving trusted service providers
access to their other digital assets. These cross-vertical partnerships will
require a paradigm shift in terms of data sharing across verticals but have
the potential to revolutionize the online — and offline — economies.
Power to the People
 After a year of data-related controversies, not to mention the rollout of
GDPR, consumers are increasingly inclined to demand not only greater
transparency, but access to and ownership of their digital footprints.
 Empowering users with the tools to manage and eventually monetize their
personal information will clearly require new AI-powered approaches
to data ethics, analysis and application. With apps like Killi showing the way,
the time could be ripe for new business models that give power back to the
people to take root. Sharing the wealth with consumers represents a win-
win, establishing users as motivated stakeholders and encouraging them to
happily provide more valuable data.
 As the year unfolds, we’ll be keeping a close eye on how the world’s top
B2B suppliers expand and refine their offerings to meet the ever-increasing
demands of both clients and end users, realizing the possibilities of next
generation customer experience in 2019 and beyond.
2019 Copyright FOCUS International

ARTIFICAIL INTELLIGENCE
Fundamentals

Artificial Intelligence
An introduction
Artificial Intelligence, or AI, is the technology enabling machines to learn from
experience and perform human-like tasks.
AI in itself is describing different technologies, which provide machines the ability
to learn in an “intelligent” way.
artificial intelligence’s major practical application; processing the vast amounts
of data generated daily.
By strategically applying AI to certain processes, insight gathering and task
automation occur at an otherwise unimaginable rate and scale.
Parsing through the mountains of data created by humans, AI systems perform
intelligent searches, interpreting both text and images to discover patterns in
complex data, and then act on those learnings.
Below a number of ways that a machine can be artificially intelligent but the
most successful is Artificial Neural Networks. Inspired by biological brains these
use artificial neurons to form the basic computational unit and networks are
used to describe the interconnection between these artificial neurons. Networks
which use multi-tier or multi-stage neural networking for feature extraction are
called deep learning. So Deep learning is a popular way to achieve machine
learning.
Machine Learning is the subset of AI that deals with the extraction of patterns
from data sets. This means that the machine can find rules for optimal behavior
but also can adapt to changes in the world, it can learn!
AI is a very broad field, with a huge depth of possible applications and the
number of use cases are only limited by your imagination

A LONGER STORY THAN YOU MIGHT THINK
Artificial Intelligence has recently experienced a real boom in the interests of
companies. With the advent of modern deep learning, we have gained a real
insight into Ai-driven technologies in practice: from the creation of medical
diagnoses, the identification of criminals in crowds to autonomous driving. But
the history of AI began earlier than you might think
An Overview

Machine Learning | Learning from experience
 Machine learning, or ML, is an application of AI that provides computer systems
with the ability to automatically learn and improve from experience without
being explicitly programmed. ML focuses on the development of algorithms
that can analyze data and make predictions. Beyond being used to predict
what Netflix movies you might like, or the best route for your Uber, machine
learning is being applied to healthcare, pharma, and life sciences industries to
aid disease diagnosis, medical image interpretation, and accelerate drug
development.
Deep Learning | Self-educating machines
 Deep learning is a subset of machine learning that employs artificial neural
networks that learn by processing data. Artificial neural networks mimic the
biological neural networks in the human brain.
 Multiple layers of artificial neural networks work together to determine a single
output from many inputs, for example, identifying the image of a face from a
mosaic of tiles. The machines learn through positive and negative
reinforcement of the tasks they carry out, which requires constant processing
and reinforcement to progress.
 Another form of deep learning is speech recognition, which enables the voice
assistant in phones to understand questions like, “Hey Siri, How does artificial
intelligence work?
Neural Network | Making associations
 Neural networks enable deep learning. As mentioned, neural networks are
computer systems modeled after neural connections in the human brain. The
artificial equivalent of a human neuron is a perceptron. Just like bundles of
neurons create neural networks in the brain, stacks of perceptrons create
artificial neural networks in computer systems.
 Neural networks learn by processing training examples. The best examples
come in the form of large data sets, like, say, a set of 1,000 cat photos. By
processing the many images (inputs) the machine is able to produce a single
output, answering the question, “Is the image a cat or not?”
 This process analyzes data many times to find associations and give meaning to
previously undefined data. Through different learning models, like positive
reinforcement, the machine is taught it has successfully identified the object.

Cognitive Computing | Making inferences from context
 Cognitive computing is another essential component of AI. Its purpose is to
imitate and improve interaction between humans and machines. Cognitive
computing seeks to recreate the human thought process in a computer model,
in this case, by understanding human language and the meaning of images.
 Together, cognitive computing and artificial intelligence strive to endow
machines with human-like behaviors and information processing abilities.
Natural Language Processing (NLP) | Understanding language
 Natural Language Processing or NLP, allows computers to interpret, recognize,
and produce human language and speech. The ultimate goal of NLP is to
enable seamless interaction with the machines we use every day by teaching
systems to understand human language in context and produce logical
responses.
 Real-world examples of NLP include Skype Translator, which interprets the
speech of multiple languages in real-time to facilitate communication.
Computer Vision | Understanding images
 Computer vision is a technique that implements deep learning and pattern
identification to interpret the content of an image; including the graphs, tables,
and pictures within PDF documents, as well as, other text and video. Computer
vision is an integral field of AI, enabling computers to identify, process and
interpret visual data.
 Applications of this technology have already begun to revolutionize industries
like research & development and healthcare. Computer Vision is being used to
diagnose patients faster by using Computer Vision and machine learning to
evaluate patients’ x-ray scans.

KEY DRIVERS OF AI
COMPUTING POWER
 Digitization and the triumph of smartphones have led to ever more
compact and at the same time more cost-effective computing power – in
addition to sensors and cameras, which are also declining in price, but
more sophisticated. The increasing competition between established and
new players is currently leading to more and more AI-specific chip
innovations. In 2008, the world's first single-teraflop supercomputer cost USD
100 million and filled an entire room. Nvidia's Titan V, launched in 2017, is an
AI processor chip with a capacity of 110 teraflops.
THE CONTINUOUS RISE OF BIG DATA
 AI systems live on data. The more data an algorithm can analyze, the
better it can recognize and understand patterns. The still growing spread of
mobile devices, the progress of the Internet of Things or the beginning use
of self-propelled cars, will massively increase the amount of available digital
data.
PROGRESS IN ALGORITHMS
 The algorithms used today for speech recognition and natural language
translation have evolved remarkably over the last decade: What used to
take weeks with a previous algorithm is now often solved within hours with
new statistical models, neural network designs and learning methods.

Al Opportunities
PREDICTION AND PREVENTION OF OUTAGES
Predictive analytics will have a massive impact on the
condition of machinery and equipment. By combining
sensors, IoT platforms and AI-controlled analysis tools,
companies will not only be able to monitor their equipment,
but also predict failures and outages.
EMOTIONAL APPROACHES FOR A BETTER CUSTOMER
EXPERIENCE
Enhanced interfaces will increasingly close the gap in
Artificial Intelligence between IQ-intensive interactions and
EQ-driven experiences, allowing brands to engage with
customers at a much deeper, personalised level.
FASTER DECISION-MAKING THANKS TO MORE DATA POINTS
Thanks to breakthrough advances in computer vision,
computers can recognise things faster and see differences
that people can't see. Businesses can use these features to
gain better insight into consumers or analyse vast amounts
of visual data.
PROCESS AUTOMATION: MORE TIME FOR VALUE-CREATION
Chatbots for customer service, AI process automation, and
AI-driven decision making reduce the effort required for
more everyday cognitive tasks. This allows employees and
organizations to focus more on higher-value tasks and work
that requires more imagination or creativity.

MAIN FOCUSES
AI-DRIVEN ANALYTICS
We are in a phase of digital transformation in which data not only
accelerates decision-making processes, but also forms the basis for
future decisions with the help of predictive analytics. Companies
across multiple industries are using AI-as-a-Service solutions from
both established vendors and start-ups and increasingly
purchasing "out-of-the-box" AI-based enterprise tools to obtain
Amazon-like personalization, Google-like search mechanisms, and
IBM Watson-like predictive capabilities.
CORE TECHNOLOGIES
Virtual reality shopping is giving consumers the convenience of
online shopping and the experience of being in a store at the
same time – in order to experience stores, products and service in
a whole new way. With the help of VR or AR applications
customers will be able to try and test products – at home or in-store
– more easily, and in a more personalized or gamified manner.
Virtual reality technology will play a secondary role, while
augmented reality apps will continue being the forefront
AI-INFUSED INTERFACES
Human-machine interfaces are on their way to becoming more
and more natural and to replace smartphones or tablets; "How
Smart Speakers stole the show from Smartphones" was the
Guardian's headline on this topic at the beginning of 2018. Tech
giants in particular, but also newcomers are competing to be the
developers of the "next platform". They use AI to increase the
intuitiveness and intelligence of existing and new interfaces.
Conversational user interfaces – especially voice-activated
solutions – have experienced a massive increase in media
attention and consumer acceptance in recent years, due to
improved processing and understanding of natural language.
However, progress is also being made in the area of emotionally
intelligent and empathetic AI, which means that more and more
home robots can be developed.
INTELLIGENT AUTOMATION
Artificial Intelligence is well on the way to automating cognitive
tasks in addition to repetitive manual tasks. Today, autonomous
systems are not only used in factories, but more and more on our
roads, in the air, on the water and in offices. Today, advanced AI-
based systems are driving preventive plant maintenance and the
optimization and automation of supply chain operations. And
increasingly sophisticated and diverse robotic process automation
tools are helping to automate everyday rule-based business
processes, allowing companies to spend more time on higher-value
work. According to Elon Musk, founder and CEO of Tesla, road
traffic will also experience a revolution: "I think we will see how
autonomy and artificial intelligence advance tremendously. My
guess is that in probably ten years it will be very unusual for cars to
be built that are not fully autonomous", says Musk.

Artificial intelligence, with the aid of machine learning,
has the power to study and interpret data, which is
revolutionizing the way of functioning of industries, and
has also given birth to two words almost all companies
want to associate themselves with –
‘Digital Transformation’.
Artificial Intelligence in 2020 to Take the Front-seat in IT Set-up
 AI has recently gained prominence due to its ability to integrate with technologies
like cloud, analytics and security solutions. Further, owing to the rising storage costs
and increase in the number of connected devices, AI has now taken a front seat in
an IT set-up.
This is further validated by a Gartner forecast that pegs AI-driven business value
to reach $3.9 trillion in 2022.
 In 2019 we are witnessing the usage of sensor data to drive several AI/ML use cases in
asset health and performance management, shop floor planning, and improving the
overall equipment effectiveness. Field force and the shop floor operators are getting
AI/ML driven personal assistants that will provide seamless access to relevant
information over voice commands, help in resolving complex issues and
troubleshooting. We will also see a wider usage of complex AI/ML in the automotive
sector for autonomous vehicles.
Explainable AI (black box to glass box algorithms) will garner more investments and
main stream adoption. Models like deep-learning behave like black boxes and
typically fail to provide an explanation for predictions made by the model. For
example, if an ML model recommends rejection of a loan application, typically it is
difficult accurately determine ‘why’ the model has rejected it. Some regulated
industries and other specific use cases require this causal analysis hence there will be
a lot of focus on this.
AI Trends
2019-2020

Chatbot
In 2019 we see aggressive AI implementation in fields like healthcare
and retail where extensive groundwork has been carried out over the
recent past. Looking inward, AI, as the child of IT is an inherent part of
the industry and will increasingly drive more aspects of the software
development life cycle like application testing and cybersecurity
 On the same note, “From a business perspective, most
organizations – both small and big – have either started to or are
looking to adopt AI-led technology in their customer service
process. So in 2020, watch out for more interactions with smarter
Chatbot or Virtual Agents, who will be able to drive intelligent and
decision-based conversations with consumers. AI along with
Machine Learning (ML) and Analytics will be increasingly used to
sieve through enormous amounts of data to establish patterns
and identify new areas of productivity and revenue generation.
Juniper’s new research finds that
chatbots will lead to a cost savings of
$8 billion by the year 2022.
AI Trends
2019-2020

Only 1 to 3 Percent Data Utilized so Far
 We believe Artificial Intelligence (AI) will have significant impact in India and
globally in the coming years. About 90% of global data was created in the last
couple of years but only 1 to 3% of this data has been analyzed. The AI and
analytics journey is just at the beginning and there is immense opportunity to
derive value from the rapidly increasing volume of data. Governments and
organisations are increasing their investments in the development of ever more
intelligent solutions. Be it banking, healthcare, agriculture, financial tech, the
opportunities for AI are massive.
Artificial Intelligence to Work Next to Humans
 Four out of five executives surveyed by Accenture agree that in the next two
years, AI systems will work next to humans in their organizations, as co-workers,
collaborators and trusted advisors.
 The frantic pace of technological advancement is set to continue a faster rate,
leading to bold predictions such as the fact that AI will be primarily responsible
for new life sciences discoveries in 5 years, or that within 10 years, one or more
countries are likely to ban human-driven cars (Accenture).
Artificial Intelligence to move upstream in HR functions
 AI is used in recruitment to perform functions such as sourcing, screening and
interviewing candidates. Given the rate of progress of AI, 2019 is likely to witness
the technology being increasingly used in learning process and improvement
processes such as Onboarding, Training & Development. AI uses huge amounts
of data, which can thereby be utilised to understand employee patterns and
design specific training processes accordingly.
AI Trends
2019-2020

Artificial Intelligence for Personalization
 We are witnessing the next wave of effort by firms in converging
digital and data to render significant ‘Intelligence led’ capability.
Data consumption patterns have evolved dramatically over the
last decade and analytics is being applied for better customer
experience, personalization, acquisition and retention – to
enhance business capabilities leveraging AI and ML techniques.
As adoption of Analytics becomes mainstream, organizations
have started integrating the AI & ML capabilities as part of their
product offerings, with tight integration across the data spectrum.
Artificial Intelligence to Transform Education Sector
 We believe AI is going to completely transform education system
across the globe. Eventually, AI will play the role of a mentor, who
can assist a learner throughout his learning journey. With AI, we will
be able to provide differentiated and individualized learning, as
everyone’s learning needs and patterns are different. As AI gets
more sophisticated, it might be possible for a machine to read
learner’s facial expressions and figure out if student is struggling to
grasp the subject and accordingly modify a lesson to respond to
that.
AI Trends
2019-2020

Mobile VAS & AI
The Next Generation Consumer Experience
VAS - Voice Added Services
The increasing popularity of AR in location-based
games is one of the major trends being witnessed.
The adoption of AR in location-based games, where
virtual content is layered over real-world surroundings
and objects, is increasing. These games can be
accessed through smartphone devices and
wearables such as smart glasses. The growth in
popularity of AR in location-based games can be
attributed primarily to platform flexibility.
Unlike virtual reality (VR) games that require investments for dedicated eye gear
and headsets, AR is compatible across most mobile devices and headsets.
Post the massive success of Niantic's Pokémon Go released in July 2016,
location-based AR games have gained increased traction for game developers.
Google's announcement, in March 2018, regarding the opening of the Google
Maps platform that allows game developers to develop better location-based
gameplay is expected to improve the quality of such game offerings. This will
drive the mobile games segment, thereby fueling the growth of the global
mobile VAS market during the forecast period.
Apart from increasing smartphone penetration, another major factor driving the
growth of the market is digitization of key industries. With the rapid advent of
urbanization, mobile Internet and technology are finding a profound role in
customers' day to day activities.
The digitization of key industries such as the transport and banking industry signify
the prominence of mobile-based services. For instance, the global popularity of
cab booking applications such as Uber and Ola, where customers can avail
transportation through mobile location-based service (LBS) applications, has
revolutionized the transport sector

SMARTPHONES
Mobile internet connections on 4G networks are quicker than the internet
connections that many people have at home. What used to be unthinkable in the
early days of the mobile internet is now reality. Streaming HD video or
downloading music, apps and games on the go without a wi-fi connection is no
problem on today’s wireless networks. According to Ericsson’s latest Mobility
Report, the number of 4G (LTE) smartphone subscriptions worldwide will have risen
to more than 4 billion by the end of this year. The next evolution of wireless
connections is already on the horizon though: 5G.
While Samsung and several other smartphone makers are planning to release their
first 5G handsets this year, another industry heavyweight is in no rush to adopt the
new technology. According to reports surfaced this week, Apple is planning to
bring 5G to its premium iPhones in 2020 and wait until 2021 before moving its entire
iPhone line-up to the new standard. While Apple has a history of waiting for new
cellular technology to mature before adopting it, delaying the switch to 5G could
prove costly, some experts argue.
According to latest estimates by Ericsson, however, 5G technology won’t really
take off until 2021/2022 anyway, suggesting that Apple could hold out on the new
standard without sacrificing too much in terms of potential iPhone sales. Ericsson
puts global 5G smartphone subscriptions at 11 million by the end of this year and
at 72 million by the end of 2020. Two years later, however, the researchers are
expecting the worldwide 5G population to have risen to 627 million.
A number of 5G phone announcements have been
made in 2019, however only a handful are currently
available, and the choice is further limited by country
and carrier.
In the US, Motorola's 5G Moto Mod provides next-
generation connectivity to a select few Moto Z
handsets, plus the Samsung Galaxy S10 5G is also
available.
For those in the UK, you can currently get hold of six 5G
phones; the Samsung Galaxy S10 5G, Oppo Reno 5G,
OnePlus 7 Pro 5G, Xiaomi Mi Mix 3 5G, Huawei Mate 20
X 5G, and the LG V50 ThinQ 5G. The Samsung Galaxy
Note 10 Plus 5G is also available for pre-order at the
time of writing.
In Australia, only three 5G smartphones are available,
and only two of these are available outright. The Oppo
Reno 5G and the LG V50 ThinQ 5G can be be
purchased for AU$1,499 and AU$1,729 respectively,
while you'll need to look for a plan with Telstra in order
to score the Samsung Galaxy S10 5G.
2019 5G smartphone

5G Smartphones
Market Overview
 The penetration of smartphones, especially in emerging economies such as
India and China will continue to increase due to improvements in Internet
speeds and technological infrastructure. The decrease in the price of
smartphones resulting from the increase in market competition, will further
boost the purchase volume of these devices. This will positively influence the
mobile VAS market.
 Owing to the growth in millennial population and increased adoption of
smartphones, there has been a rapid shift from e-commerce to m-
commerce systems. Advances in technology that enable secure payment
processes on mobile devices in a simplified manner are expected to
increase the frequency of mobile payment transactions. The rapid shift
toward m-commerce is identified as one of the key trends that will gain
traction in the market,” says a senior research analyst at Technavio for the
machine to machine (M2M) and connected devices industry.
 The market players present in mobile VAS market are mainly focusing on
product enhancements by implementing advanced technologies. By
signing partnership, contracts, joint ventures, funding, and inaugurating new
offices across the world permit the company to maintain its brand name
globally.

2020 ─ 5G Roll-out
Fuel for Artificial Intelligence
 As mentioned, we are witnessing unprecedented levels of
advancements particularly in perception AI technologies such as
computer vision and natural language processing. This combined
with sensors, cameras, and IoT, will enable businesses to innovate
faster. With the rapid evolution of AI, use cases will further expand to
include areas not part of the core business. AI is heavily impacting
sectors such as retail, healthcare, telecom, travel, and Fintech.
Further, human-machine interaction will advance swiftly through the
democratization of deep learning and speech recognition.
 We expect an explosion in the availability of digital data, which is the
fuel for AI. However, there are two risks to be considered. Firstly
executives will need to be careful in seeing through the AI hype and
carefully choose AI projects they should pursue and secondly,
policymakers take steps to implement data protection regulation.
I remain extremely optimistic about the potential of AI in having a
transformative impact on every business in the year 2020.
 Technology advancements, driven by Artificial Intelligence and
Cloud Computing will dominate 2020 in redefining enterprise
competitive advantage and create a base for future enterprise
progression. AI will play a key role in solving enterprise challenges
ranging from predictive analytics, fraud analytics, customer 360,
location analytics etc.
 Deep Learning and Machine Learning will increasingly be leveraged
for pattern recognition, computer vision, gesture recognition, natural
language processing, robotics etc. AI is on an accelerated path to
being ubiquitous and is getting seamlessly integrated with everyday
lives of end users.

in Telecom Networks
An Overview
AI with learning can introduce new services into the communication networks to
improve network efficiency and UX. AI can be introduced to the areas of
designing, operating, maintaining and managing communication networks and
services.
Telecom operators preferably follow a model that is:
 Automatic: They should be able to transition from an order driven approach
to a model driven approach.
 Adaptive: Deep analysis should form the key to move from open-loop to
closed-loop.
 Autonomous: Implementation of self-learning to move from a static policy
towards a phase of enhanced self-learning and implementation.
Operators need intelligent decisions to manage complex resources and
dynamic traffic. But so far no one single model has the ability to accurately
describe the network traffic characteristics. Through deep learning, the machine
system can use the existing training data to process large amounts of data
through data mining.
From the TDM automatic switch, it has been the pursuit of the communications
industry to introduce intelligence into network operations, management and
maintenance management.
The goal of greater automation requires telcos to address the question of how
changes to networks are designed, not only how changes are executed. By
incorporating AI technology into the design process, operators will be able to
remove internal barriers to speed and scale, and enable truly intelligent,
autonomous networks.
SELF-ORGANIZING NETWORK
SON technology minimizes the
lifecycle cost of running a
mobile network by eliminating
manual configuration of
network elements at the time
of deployment, right through to
dynamic optimization and
troubleshooting during
operation. Besides improving
network performance and
customer experience, SON can
significantly reduce the cost of
mobile operator services,
improving the OpEx-to-revenue
ratio and deferring avoidable
CapEx.

AI in Software Defined Network (SDN)
SDN provides network operators with a logical centralized control and
flexible programming interfaces which greatly promote the capabilities of
network automated management and control; more than was previously
possible. A typical SDN framework is composed of three layers:
 Infrastructure layer
 Control layer
 Application layer
AI in Network function virtualization (NFV)
With virtualization technology, network functions virtualization can divide
network-level functions and applications, such as
 Routing.
 Customer premises equipment (CPE).
 Mobile core
 IP multimedia subsystems (IMS)
 Content delivery networks (CDN)
 Switching elements
 Mobile network nodes
 Home routing operations
 Tunnel gateway elements
 Traffic analysis
 Service assurance
 (SLA) monitoring
 Testing and diagnosis
 Next generation network (NGN) signal
 Aggregation/network range functions
 Application optimization
 Security policy

 As telecom companies are adopting technologies like virtualization, SDN-
NFV, orchestration; Artificial Intelligence is going to play a big role in smooth
integration of these technologies and automating the networks.
AI application in mobile networks circles around three applications –
Self Optimizing networks (SONs), Software defined networks (SDN) & Network
Function Virtualization (NFV) and enablement of neural networks. Among
these, we may see SONs at the earliest. SONs enable operators automatically
to optimize the network quality based on traffic information by region and
time zone based on various machine learning algorithms.
IDC on the other hand has predicted that 31.5% of the telecommunication
organizations are primarily focusing to leverage existing
investments/infrastructure and rest 63.5% are making new technology
investments for AI systems. While these continue to be global trends, India
should equally see an increase around AI; primarily driven by enterprise
needs to drive viable efficiencies and consumer demand for
contextualization.
On the subscriber side, AI and Machine Learning will help telecom operators
in subscriber profiling and analyzing offer conversion rates, content usage
trends and network activity. This will help them push offers that are tailored
as per subscriber needs at the right time, believe analysts from Counterpoint.
 Using AI and data analytics, operators will be able to identify and push
various services to the customers at the right time, for e.g. – in case of post-
paid customers, operators must encourage high speed data services and
offer tailored data packs when subscriber is running low on data. The timing
of offering tailored packages based subscriber intelligence is very important.
A good example is Airtel partnering with Korea’s SK Telecom to enable AI-
assisted network. SK Telecom has deployed an AI-assisted network (known as
TANGO) with big data analytics and machine learning capabilities to
enhance customer experience through automated detection,
troubleshooting and optimization of mobile networks.
in Telecom Networks

 As more reliable and affordable bandwidth is enabled, it unleashes a
plethora of opportunities that can traverse over telecom networks. So,
a convergence at network level becomes possible. This is then value
enhanced by adding dynamism and intelligence in to the systems
through AI which makes the solution intuitive, proactive as well as
reactive to the situations. Telecom becomes the default highway for
anything that is to do with digital and adds a lot of opportunities in the
telecom domain. One may not see the telecom the way we look at it
presently, meaning a different set of revenue streams as well.
 AI is expected to have an impact in a multitude of areas – the most
important being traffic classification, anomaly detection and
prediction, resource utilization and network optimization, along with
network orchestration. Further, it will also assist the mobile devices with
virtual assistants and bots.
 We believe that Artificial intelligence will solve most of the issues
related to customer care, network coverage, billing, service/product
offering and many more. Personalization of service and care would
witness a new benchmark.
 AI will help telecoms in creating alerts and advice subscribers to the
best plan. It will be essential for creating personalized and adaptive
customer journeys.
Source: Pointnext
in Telecom Networks

AI’s role in
automating networks
 Emerging technologies such as IoT and cloud are pushing the networks to
handle higher volumes of data, therefore; making automation an
imperative for better network planning and connectivity.
Typically, networks through nodes observe something and then the
controller, generally a human being, takes a desired action. With AI, the
network can decide on its own and also take the next course of action
through various hardware / software solutions, essentially IoT solutions.
Added to it through Machine Learning, the network will keep on adding
intelligence, so it will grow in capabilities like humans as they acquire
more skills and knowledge.
 AI-based intelligent network applications such as precision algorithms
can provide intelligent network optimization/operation solution, and
intelligent network operation and maintenance. Further, AI technology
will also lead to the evolution of automatic, self-optimizing and self-
healing networks, complemented with high performance computing
power and data analytics capability.
 Existing business processes such as network operations (both planning
and optimization) have been performed manually resulting in delays and
errors, which negatively impact on customers' experience. To resolve
these challenges, CSP business processes can be automated using AI
capabilities such as machine learning, deep learning, and natural
language processing. The need for AI to drive automated CSP operations
will continue to grow as the CSP network moves from being physical to
being virtual. Software defined networking (SDN) and network functions
virtualization (NFV) will be dependent on automated processes to deliver
service agility and cost efficiency. Capabilities such as self-diagnostics
and self-optimization can only be achieved using intelligent insights
obtained from the analysis of quality data sets.
 AI-enabled networks can think beyond their correlative programming
and suggest outcome-based scenarios (‘what would you like to
happen’). In the future, AI will be able to differentiate between
correlative and causal, and proactively pursue their own choice of
outcomes beyond the scope of human programming, and before any
problems are noticed by subscribers (‘I can take care of myself’).

 How AI will integrate technologies
There will be requirement of all software/hardware tools to add intelligence.
These will help in building sensory system to the networks and in a decentralized
architecture which is important for such a solution. Also, through SDx
(Software Defined Anything), the networks will have the agility to respond to the
situations without requiring phenomenal changes in the system components.
SDN/NFV in combination with AI is becoming a powerful tool for evaluating and
securing networks effectively. It can help telcos in addressing their concerns
over analyzing massive volumes of information to detect consumer patterns,
anomalies and potential security concerns. This could further help them in
optimizing the profit margin arising from enhanced network operations and
reconfiguring the network to restore or mitigate services in the event of any
cyber security attack.
 AI and new revenue streams
AI algorithms can combine historic patterns and behavior (plus “look alike”
patterns) with ongoing real-time engagement to provide the right next best
action to the customer at the right time and in the right context of their journey.
The outcome for the consumer will be recommendations and offers that are
personalized, well targeted, and relevant. The result for the CSP will be an
increase in revenues and ARPU.
The key area where telcos can deploy AI to generate new revenue source
would be ‘Subscriber Intelligence’. From contextual and personalized upselling
to innovative credit models, telcos can customize their offerings in "real time" to
improve the conversion rate of offers, thus enabling incremental wallet shares
from their customers.
As offers start becoming smarter with improved machine learning, customers
buyer behavior will also improve. This will result in higher conversion rates of
newly launched business models; that are technologically powered by AI.
AI’s role in automating networks

What does the
future hold?
 IDC’s research has stated that the primary goal of telecom/media companies
to leverage AI technologies is largely driven by improving efficiency, reducing
staff related costs and increasing revenue. Other key drivers include improving
customer support, marketing & engagement, operational insight, regulatory
compliance, fraud detection, along with supporting business innovation.
“Most telco players today continue to experiment with AI, especially in the
domain of generating actionable intelligence from structured and unstructured
data. In this they are partnered by emerging vendors/startups.
 Over half of operators expect to adopt AI in their networks by 2020
 With the integration of 5G technologies such as new radio spectrum bands, denser
topologies, massive MIMO and beamforming, mobile data traffic is expected to
increase by a factor of five over the next five years
 the rapid expansion of the Internet of Things will require close to real-time latency.
 91 percent of operators in Southeast Asia, Oceania and India want more AI in the
network
 64 percent of operators will focus their AI efforts on improving network capacity
planning
 Focus areas include reducing network planning timeframes, and infrastructure
modeling to reduce capital expenditures (CapEx).
 a strong correlation between increased reliance on smart automation enabled by
AI and significant returns on investments realized principally through reduced
operating costs.
 By 2024, 5G is expected to cover more than 40% of the world's population. Will you
and your organization be part of that astounding growth?
According to Ericsson latest AI Report

IoT can be classified into the following categories
Things that receive data and act on it
For example, 3D printers, wearable devices and smart TVs that collect data from
sensors and take actions based on it.
Things that gather information and send it
For example, motion sensors, moisture sensors and light sensors that send relevant
data for better decision-making.
Things that perform both functionalities
IoT based farming includes the sensors that gather information about soil
moisture to find how much water is needed by the crops.
What is IoT?
Internet of Things (IoT) is an ecosystem of connected physical objects that are
accessible through the internet. The ‘thing’ in IoT could be a person with a heart
monitor or an automobile with built-in-sensors, i.e. objects that have been assigned
an IP address and have the ability to collect and transfer data over a network
without manual assistance or intervention. The embedded technology in the
objects helps them to interact with internal states or the external environment,
which in turn affects the decisions taken.

IOT VS. IIOT
IIoT refers to a subcategory of the broader Internet of Things. IoT includes IIoT
plus things like wearables, smart ovens, or smart consumer products.
IIoT focuses specifically on industrial applications such as manufacturing or agriculture

IoT &
Blockchain
The combination of blockchain and the Internet of Things has appeared
as one of the most exciting use cases for the technological era. Two big
companies, Volkswagen and Bosch believe that the decentralized data
and Internet of things marketplaces should co-exist. Also, research done
by IDC says that 20% of all IoT deployments will have blockchain services
enabled by 2019.
As the effectiveness of IoT relies on the information available in the system,
protecting the information throughout its lifecycle is crucial. This is where
blockchain comes into the picture for IoT.
Why IoT needs blockchain?
The biggest drawback of IoT is that it depends on centralized communication
models to interact with the system. It can also be said that all the devices in IoT
setup are identified, connected and validated via centralized cloud servers.
However, centralized clouds and networking equipment used in the existing IoT
solutions have high maintenance and infrastructure cost. As IoT systems are
connected through these services, scalability can become a significant issue.
As the number of IoT devices increases, the number of interactions between the
server and devices increases the cost. That is the reason why current systems
cannot support large IoT networks.
Also, cloud servers are vulnerable to a single point of failure which means the
failure at one point can affect the entire ecosystem. Therefore, using a peer-to-
peer model instead of a client/server model can be the right solution that IoT
industry needs today.
With decentralization in place, storage needs and computation can be
distributed across millions of IoT devices and central failure cannot have an
impact on the whole network.
So, the use of blockchain in IoT can help the IoT devices to scale up efficiently.

How can blockchain
help in IoT?
 As the blockchain is tamper-proof and decentralized, it can do what IoT exactly
requires. Using blockchain in IoT can help you track billions of connected
devices in the network.
 Integrating the blockchain in IoT devices can also reduce the costs of installing
and managing servers for an IoT network. Blockchain uses cryptographic
algorithms which ensure the confidentiality and security of the data on the IoT
network. Blockchain in IoT also protects the network from the man-in-middle
attacks because it does not have a single thread of communication.
 With smart contracts, agreements can be created which will execute when
certain conditions are met. For example, temperature sensors can fetch the
data and send it to the blockchain. Based on the fetched records, smart
contracts can trigger to take specific logic added to it.
Three use cases of Blockchain in IoT
 Supply Chain & Logistics
Blockchain combined with IoT can improve the traceability of the supply chain
network. IoT sensors like temperature sensors, motion sensors or GPS connected to
the vehicles provide information about the shipment status. Data fetched from the
sensors gets stored in the blockchain, bringing traceability, auditability and
transparency in the system.
o Smart Homes
IoT devices allow the home security system to controlled remotely from the
smartphone. But the centralized model for exchanging information generated by
IoT sensors lack ownership of data and security standards. By moving the data
gathered from IoT devices to the blockchain can solve security issues.
 Parking Solutions
A company named NetObjex has come up with an idea of a smart parking
solution with IoT and blockchain. Using IoT sensors, it can become easier to find the
empty parking space and pay automatically with crypto wallets.
IoT sensors installed in the parking area can fetch information such as time for
which car remains parked and vehicle number to obtain the linked wallet address.
The data gets stored in the blockchain and triggers smart contracts to automate
payments.
Numerous industries have started to experiment with the potential of blockchain in
IoT networks. As a blockchain development company, we can help you
understand how Blockchain IoT combined can transform the various sectors.

5G
Fundamentals

WHAT IS 5G?
 5G is the 5th generation of mobile networks, a significant evolution of todays
4G LTE networks. 5G is being designed to meet the very large growth in
data and connectivity of today’s modern society, the internet of things with
billions of connected devices, and tomorrow’s innovations. 5G will initially
operate in conjunction with existing 4G networks before evolving to fully
standalone networks in subsequent releases and coverage expansions
 In addition to delivering faster connections and greater capacity, a very
important advantage of 5G is the fast response time referred to as latency.
Latency is the time taken for devices to respond to each other over the
wireless network. 3G networks had a typical response time of 100
milliseconds, 4G is around 30 milliseconds and 5G will be as low as 1
millisecond. This is virtually instantaneous opening up a new world of
connected applications.
 5G uses radio waves or radio frequency (RF) energy to transmit and receive
voice and data connecting our communities. In addition to delivering faster
connections and greater capacity, a very important advantage of 5G is the
fast response time referred to as latency. Latency is the time taken for
devices to respond to each other over the wireless network. 3G networks
had a typical response time of 100 milliseconds, 4G is around 30 milliseconds
and 5G will be as low as 1 millisecond. This is virtually instantaneous opening
up a new world of connected applications.
.
.
Growing demand
related to new 5G
use cases will trigger
investment across all
network domains.

WHAT WILL 5G ENABLE?
 5G will enable instantaneous connectivity to billions of devices, the Internet of
Things (IoT) and a truly connected world
 5G will provide the speed, low latency and connectivity to enable a new
generation of applications, services and business opportunities that have not
been seen before.
There are three major categories of use case for 5G:
 Massive machine to machine communications – also called the Internet of
Things (IoT) that involves connecting billions of devices without human
intervention at a scale not seen before. This has the potential to revolutionize
modern industrial processes and applications including agriculture,
manufacturing and business communications.
 Ultra-reliable low latency communications – mission critical including real-time
control of devices, industrial robotics, vehicle to vehicle communications and
safety systems, autonomous driving and safer transport networks. Low latency
communications also opens up a new world where remote medical care,
procedures, and treatment are all possible
 Enhanced mobile broadband – providing significantly faster data speeds and
greater capacity keeping the world connected. New applications will include
fixed wireless internet access for homes, outdoor broadcast applications without
the need for broadcast vans, and greater connectivity for people on the
move.

5G
ARCHITECTURE
Most operators will initially integrate
5G networks with existing 4G
networks to provide a continuous
connection.
5G network architecture illustrating
5G and 4G working together, with
central and local servers providing
faster content to users and low
latency applications.
A mobile network has two main
components, the ‘Radio Access
Network’ and the ‘Core Network’.
 The Radio Access Network - consists of various types of facilities including small cells,
towers, masts and dedicated in-building and home systems that connect mobile
users and wireless devices to the main core network.
Small cells will be a major feature of 5G networks particularly at the new millimetre
wave (mmWave) frequencies where the connection range is very short. To provide
a continuous connection, small cells will be distributed in clusters depending on
where users require connection which will complement the macro network that
provides wide-area coverage.
5G macro cells will use MIMO (multiple input, multiple output) antennas that have
multiple elements or connections to send and receive more data
simultaneously. The benefit to users is that more people can simultaneously
connect to the network and maintain high throughput. MIMO antennas are often
referred to as ‘Massive MIMO’ due to the large number of multiple antenna
elements and connections however the physical size is similar to existing 3G and 4G
base station antennas.
 The Core Network - is the mobile exchange and data network that manages all of
the mobile voice, data and internet connections. For 5G, the ‘core network’ is being
redesigned to better integrate with the internet and cloud based services and also
includes distributed servers across the network improving response times (reducing
latency).
Many of the advanced features of 5G including network function virtualization and
network slicing for different applications and services, will be managed in the core.
The illustration below shows examples of local cloud servers providing faster content
to users (movie streaming) and low latency applications for vehicle collision
avoidance systems.
5G network architecture illustrating 5G and 4G working together, with central and
local servers providing faster content to users and low latency applications.

Technologies that
will aid 5G,
IoT and build future
networks
While telcos, telecom gear makers and network providers
have started working towards deployment of 5G
technology, there are technologies that are being
explored globally which will aid 5G, IoT and will be a
significant part of the future networks.
MIMO
 MIMO is short for ‘multiple input, multiple output’ which is an antenna
technology for wireless communications. As part of this technology, two or
more transmitters and receivers are used to send and receive more data at
once. The antennas are then combined to minimize errors and optimize
data speed.
The technology works well under conditions of interference, signal fading,
and multipath. Recently, Sprint and Samsung tested MIMO technology in
South Korea.
Narrowband IoT
 Narrowband IoT (NB-IoT) will be useful for connecting billions of devices in
the IoT that will be used in machine-to-machine (M2M) rather than in
human communications. The technology needs just 200kH of bandwidth
and can run adjacent to existing cellular networks. NB-IoT is optimised for
low throughput and can give uplink and downlink rates of around 200kbps.
4.5G
 4.5G or LTE Advanced is the next wireless upgrade beyond LTE or 4G which
is faster and better in user experience and highly efficient in spectrum use.
4.5 G offers average download speed of 2 to 3 times that of 4G. This
suggests download speeds on LTE Advanced could be 14-21Mbps vs. 7-12
Mbps over early LTE/4G.
LTE -A to its higher speeds, far greater spectrum efficiency and use of
heterogeneous network, can boost network capacity by 3 to 5 times in
comparison to 4G. Carrier aggregation is key; up to five carriers can be
used on 4.5G. Wider spectrum bands will boost speeds.

LoRa (Long Range)
 LoRa technology offers a mix of long range, low power consumption
and secure data transmission. Using this technology, public and
private networks can provide coverage that is greater in range
compared to that of existing cellular networks.
Tata Communications has implemented the LoRa network in
Jamshedpur and they're trying to do some tests.
mmWave
 Millimeter wave is the band of spectrum between 30 gigahertz (Ghz)
and 300 Ghz. Currently, researchers across the globe are testing 5G
wireless broadband technology on millimeter wave spectrum.
AT&T is conducting its 5G test using mmWave technology in Austin,
Texas and expecting tests to yield speeds of 1 GB per second
OFDM
 OFDM is a frequency-division multiplexing (FDM) scheme used as a
digital multi-carrier modulation method where a large number of
closely spaced orthogonal sub-carrier signals are used to carry data
on several parallel data streams or channels.
OFDM-based waveforms are the foundations for LTE and Wi-Fi systems.
OFDM-based waveform and multiple access are recommended for
5G.
LTE-U
 LTE in unlicensed spectrum (LTE-U) is a proposal, originally developed
by Qualcomm, for the use of the 4G LTE radio communications
technology in unlicensed spectrum.
Technologies that
will aid 5G,
networks

Network slicing
 As per Ericsson’s definition Network slicing is one of the key capabilities
that will enable flexibility, as it allows multiple logical networks to be
created on top of a common shared physical infrastructure.
The technology allows a network operator to provide dedicated
virtual networks with functionality specific to the service or customer
over a common network.
According to a white paper published by Ericsson, some of the 5G
network slicing use cases will include expanded mobile broadband
with more video, higher speeds and wide scale availability; massive
machine-type communication with transportation monitoring and
control; mass market personalized TV with big data analytics
Massive MIMO
 Massive MIMO takes the concept of MIMO to a new level by placing
dozens of antennas on a single array. With massive MIMO, future 5G
networks will be able to carry more data onto the same amount of
spectrum
Huawei, ZTE, Vodafone, Samsung, Sprint and Ericsson are actively
working towards testing and leveraging this technology for faster data
speeds and low latency.
HetNet
 As per the definition of telecomabc, Heterogeneous networks
(HetNet) is a term used for modern mobile communications networks.
A modern mobile communications network is comprised of a
combination of different cell types and different access technologies.
HetNet combines large and small cell sites and different radio
technologies like cellular and Wi-Fi.
Technologies that
will aid 5G,
networks

5G Market Overview
The 5G infrastructure market is estimated to be valued at USD 2.86 Billion in 2020
and it is further projected to reach USD 33.72 Billion by 2026, at a CAGR of 50.9%
between 2020 and 2026. The major factors driving the growth of the 5G
infrastructure market include increasing demand for mobile data services, rising
importance of software implementation in communication network, growth of
machine-to-machine (M2M) communication in industries, and growing
demand for high speed and large network coverage.
The communication infrastructure that will power 5G is three-fold. Small cell
deployments will lead the market, with a $5.6 billion market size by 2026, the
report said. The macro cell market will be valued at $2.9 billion, and the radio
access network (RAN) market is predicted to have a value of $436.1 million by
2026.
For core network technologies, network function virtualization (NFV) will grow to
the largest market size, built on a CAGR of 71.2%. According to the report,
software-defined networking (SDN) will have the second largest market, but will
only have a CAGR of 55.7%. The third largest market will belong to multi-access
edge computing (MEC), with 65.8% CAGR. Fog computing (FC) will have the
largest CAGR at 73.2%, but the smallest market size.
As 5G gets applied to real-world use cases, industrial automation and consumer
electronics will benefit the most. Here’s how the 5G infrastructure market will
break down by application, and its percentage share of the market in 2026:
5G Adoption

Smarter business applications
 Faster download and data transfer speeds will give business applications the
power to provide better services and experiences than ever before. Because
these applications can handle more incoming and outgoing data, they will be
capable of doing more. Lower latency rates will also allow for more consistent
performance, which will be essential for business applications that can’t
function properly if latency disrupts the user experience.
 For businesses using cloud-based solutions, faster data transfer speeds will
make these solutions more functional, powerful and responsive. Overall,
business technologies and software will be much more useful and productive
for remote workers relying on mobile technology to be productive while
working in the field — or in any location outside of the business office.
Enhanced enterprise communications
 Faster network speeds will streamline communications for enterprise
organizations whether they’re working with people on the other side
of the world or trying to communicate with customers in areas that
don’t have fast, reliable internet service.
 Enterprise collaboration on large, data-rich projects will be supported
with ease. Meanwhile, weak connections and other pains of digital
phone and video conferences will be dramatically reduced, allowing
for high-quality connections and faster, more productive digital
meetings
Better support for IoT ecosystems
 IoT technologies are an exciting opportunity for businesses on many
different fronts, but providing a signal to ever-expanding internet-
connected devices taxes a company’s bandwidth. The future of 5G
networks will provide such strong internet service that IoT devices can
be properly supported without putting too much strain on the
company’s available bandwidth. This will make IoT solutions easier to
implement, and as more IoT devices are added to the network,
companies won’t have to worry about how these new technologies
strain their current network.
Future
of 5G Networks

When will
5G launch?
In the US
Verizon surprised most of the world by launching its 5G network at the start of April
2019, making it the first globally to offer the next-generation network.
It's currently only available in limited parts of Chicago and a few other locations,
and there are just two handsets currently available to use on the new 5G network.
In Chicago, US we've managed to obtain speeds of up to 1.4Gbps, which is
massively faster than 4G's theoretical top speed of 300Mbps (although average
speeds tend to be below 100Mbps).
However, 5G coverage is patchy and you had to move around the city's various
5G masts to get this top speed.
AT&T has rolled out its 5G network to 19 cities across the States, but it still doesn't
offer any 5G phones - with your only option for now a 5G Netgear Nitehawk
mobile hotspot.
Meanwhile, T-Mobile is yet to launch its 5G network in the US, but it previously said
it would bring 5G to 30 cities, starting in New York City, Los Angeles, Dallas, and
Las Vegas.
In the UK
EE was the first UK carrier to launch its 5G network, switching it on in six cities on
May 30 2019. It has promised to bring 5G to 10 further cities by the end of 2019.
It was followed by Vodafone on July 3, 2019, when it launched 5G in seven cities,
rolling out to a further eight towns and cities on July 17.
Next up was Three, which launched a 5G service in London on August 19,
however, there's a catch - it's initially only available for home broadband.
However, it will be coming to mobile later this year, as well as to 24 more towns
and cities.
O2 meanwhile is the only major UK network not to have any sort of 5G service yet,
but it plans to roll 5G out in October.
5G in London, UK is more of a mixed bag, with speeds in our test ranging from
200Mbps to 550Mbps - still much quicker than 4G, but not the same level as we
are seeing in Chicago.
In Australia
Telstra's 5G coverage went live as of May, 2019, with the launch of the first 5G
smartphone in Australia – the Samsung Galaxy S10 5G.
At the time, coverage was limited to 10 major cities and regions and, within those
regions, was somewhat limited and patchy. This includes Adelaide, Brisbane,
Canberra, Gold Coast, Hobart, Launceston, Melbourne, Perth, Sydney, and
Toowoomba.
Optus, on the other hand, hasn't released any concrete timelines or roadmaps,
although its official website does mention that it aims to have 1,200 5G sites built
by March 2020, which could indicate a date for public availability.

Standard Patents
& 5G Technologies
The number of SEPs owned by the companies operating
in a specific field plays an important role in providing
insights into industry trends. The chart below shows the
number of 5G SEPs listed in the ETSI database. Simple
patent families are taken into consideration.
The chart reveals
that Huawei, Nokia, Samsung, LG,
and Qualcomm are the top five SEPs owners in
5G. Among them, Huawei holds the highest
number of SEPs.
If we consider the top ten,
ZTE, Ericsson, Intel, CATT, and SHARP step into
the game, meaning that we already
have three Chinese companies
(Huawei, ZTE, and CATT) in the top ten.

Sources
5G PPP
InfoVista
Accedian Networks
GenXComm
AirHop Communications
HCL Technologies
Hitachi
Juniper Networks
KDDI Corporation
Keima
Deloitte
Ericsson
CNBC
Nokia
Huawei Technology
Vodafone Group
Bain
3GPP
Qualcomm
ITU
TM Forum
Nokia Networks
Linksys
Google
NEC Corporation
RED Technologies
Telefónica Group
Thales
Vodafone UK
WBA (Wireless
Broadband Alliance)
Forbes
Servion
Fortinet
Innoplexus
HP Enterprise
Mckinsey
Accenture
IBM
KPNG
EMF
WEF

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