Chapter 3 -5G New Technology Innovative Application

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1 5G + New Technology Innovative 
Application 
We learned in the previous chapter about 5G networking modes and key 
technologies. We explored why non-standalone (NSA) and standalone (SA) 
modes are needed and how they are implemented. We looked at 5G's key 
technologies from three aspects: radio access, transport, and core networks. 
We also understood that 5G boasts a flexible architecture, lending itself to 
meeting the requirements of different industries. Alongside rapidly 
developing 5G, we have seen quite a number of emerging and trending 
technologies, such as big data, artificial intelligence (AI), Internet of Things 
(IoT), and cloud computing. What dynamics will occur when 5G is integrated 
with these technologies? In this chapter, we will focus on how the 
convergence of 5G with these technologies will help grow various vertical 
industries. 
1.1 Convergence with ICT Promotes the Digital 
Economy 
1.1.1 What Is the Digital Economy? 
Throughout the history of mankind — from the agricultural and industrial 
ages to the information age — every technological or industrial revolution 
promotes a great progress in productivity, driving human civilization to the 
next level. As is in the agricultural age where hunting, planting, and livestock 
farming were the primary productive forces, the industrial age was 
characterized by relying on steam engines and electricity to boost 
productivity. In the information age represented by the Internet, information 
has evolved into the prime factor of production and constitutes the essential 
technical and material basis of the information-driven society. 
In the information age, the Internet has profoundly revolutionized the way 
people work and live and injected strong impetus to global economic 
development. The world is now stepping into the age of the digital economy 
— a new form of economic and social development that uses digital 
knowledge and information as a primary production element. Driven by 
digital technology innovation, it relies on modern information networks, 
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enabling the deep integration of digital technologies and the real economy to 
improve the level of digitization and intelligence in traditional industries and 
accelerate the reconstruction of economic development and governance 
models. 
The power of technology promotes mankind to discover new frontiers. The 
Internet, big data, cloud computing, and 5G are leading the world to a higher 
level of productivity, digitalization, and intelligence. The world is undergoing 
a new development phase of the digital economy, and we are on the edge of 
the new era. As a new form of social and economic progress in the 
information age, the digital economy facilities achieving both economics of 
scope and economics of scale, becoming a new driving force for global 
economic growth. 
1.1.2 ICT Reshapes the Digital Economy 
ICT is reshaping economic growth patterns and social governance models. As 
the digital economy further develops into a new era, the digital economy has 
pivoted from the consumer Internet to the industrial Internet. The industrial 
Internet connects new digital technologies, such as 5G, AI, cloud computing, 
big data, and IoT, with industrial development and social governance, 
enabling them to improve internal efficiency and external services and 
achieve leapfrogging development. Its essence is to allow enterprises to 
maximize the potential of digital technologies for improving efficiency and 
optimizing configuration, while coupling enterprises with data throughout 
industry chains1 to improve internal efficiency and external services. For 
example, by encouraging the development of smart manufacturing and 
smart city, governments aim to improve industry competitiveness and 
administrative efficiency through new ICT technologies. 
China Academy of Information and Communications Technology (CAICT) 
predicts that, between 2020 and 2025, 5G will directly drive a total economic 
output of CNY10.6 trillion and generate an additional CNY3.3 trillion in 
economic value in China. Indirectly, these value will increase to about 
CNY24.8 trillion and CNY8.4 trillion, respectively. By 2025, 5G is expected to 
directly create more than 3 million jobs. This shows that 5G will be a major 
contributor to economic growth. 5G will change people's lives and 
production methods, and will even bring fundamental changes in society. 5G 
will become a key infrastructure for comprehensive economic and social 
digital transformation. 
Over the next two to three decades, an intelligent society will become 
reality, where all things are aware, connected, and intelligent. 
 
 
1 "5G + Cloud + AI": Engine for the New Era of Digital Economy, CAICT 
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1. Everything in the physical world will be sensed and converted into 
digital signals. Multi-sensory channels (such as temperature, space, 
touch, hearing, and vision) will enable situational awareness and 
interaction to deliver an immersive user experience. 
2. The full connectivity will bring all data online, providing wide-ranging 
connectivity across cities, mountains, and even outer space to enable 
intelligence. 
3. Everything will become intelligent thanks to big data and AI, and 
individuals, families, industries, and cities will gradually embrace digital 
twins to enhance the physical world. A second world — a digital one — 
will emerge to augment the physical world, enriching life. 
All of this will be made possible as ICT technologies continue to advance. ICT 
infrastructure will be the foundation of an intelligent world and based on the 
device-network-cloud IT architecture. 
 
 
Devices include mobile phones, cameras, and sensors, and they sense the 
physical world. Networks can be mobile (such as NB-IoT, 4G, and 5G) or fixed 
(such as broadband and private lines). Cloud refers to cloud computing. In 
the future, all device-sensed data will be transmitted to and pooled on the 
cloud through the networks to form big data, based on which AI analytics will 
be reality. For example, cameras and sensors in cities will collect various data 
and send the data to cloud to form big data that can enhance smart security 
while improving urban management efficiency. 
This means that 5G enables smart city to usher in a new opportunity of 
development by providing connectivity to everything anytime and anywhere. 
With such connectivity, each person, thing, and organization in digital-twin 
cities will be connected in real time, making them seamlessly integrated and 
interactive with the physical cities and enabling all intelligent connections to 
act as distributed super brains. The cities will become more intelligent to 
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fulfill various personalized needs2. None of these will be possible without the 
high bandwidth, low latency, solid reliability, and massive capacity enabled 
by 5G. 
In the future, 5G, cloud, AI, and IoT technologies will enable vertical 
industries. 5G and cloud will become the foundation for information-driven 
development. The device-network-cloud architecture will further enable top-
level applications required to improve overall efficiency. 5G capabilities will 
play a significant role in achieving these goals. 
1.2 Characteristics and Current Developments of New 
Technologies 
1.2.1 Internet of Things 
1.2.1.1 What Is the Internet of Things? 
The term "Internet of Things (IoT)" was first coined by Massachusetts 
Institute of Technology (MIT) in 1999. The original concept of IoT refers to 
the radio frequency identification (RFID)-enabled technologies and devices, 
which interoperate within the Internet based on agreed communications 
protocols to intelligently identify and manage objects as well as 
interconnect, exchange, and share information. IoT is envisaged as a 
network of things all connected to the Internet with the supportfrom 
sensors, such as QR code readers, RFID devices, infrared sensors, global 
positioning systems, and laser scanners, to realize information exchange and 
communication, thereby enabling smart tagging, positioning, tracking, 
monitoring, and management3. 
IoT is an Internet for thing-to-thing connection. This means that its core and 
foundation are still the Internet, with the connections extended and 
expanded to things for communication and information exchange between 
them4. 
The concept of IoT dates back to Bill Gate's 1995 book titled The Road Ahead, 
in which he mentions the idea about the Internet of Things. It attracted little 
attention due to the development of wireless networks, hardware, and 
sensors. In 1998, MIT creatively proposed an IoT-like concept, which was 
then called the EPC system. In 1999, the Auto-ID Center in the US first 
proposed the concept of IoT based on item coding, RFID technology, and the 
Internet. 
China launched the Made in China 2025 initiative in 2015, vowing to promote 
the deep integration of digital and smart manufacturing with information 
technologies such as IoT, cloud computing, AI, and smart manufacturing as 
the main path for future development required to upgrade China from a 
 
2 Source: 5G + Smart City White Paper 
3 Source: International Telecommunication Union (ITU) 
4 Source: baike.baidu.com 
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workshop of the world to a world manufacturing power. At the end of 2018, 
China prioritized IoT as a new infrastructure, marking that IoT is moving 
toward the fourth phase as the infrastructure of the digital economy. The 
development of IoT will continue to be driven by the digital and intelligent 
transformation across industries and growing consumption demand, as well 
as internal drivers such as maturing technologies and ecosystem 
developments. 
 
 
1.2.1.2 IoT Technology Architecture 
IoT was originated to provide connectivity and transmission functions. As its 
architecture matures, data processing becomes increasingly complex and 
burdensome, and this leaves IoT increasingly interlinked with edge 
computing, cloud-edge synergy, and other technologies. As its development 
continues, the industry introduces a logical architecture that divides an IoT 
network into three parts: cloud, networks, and devices, with the cloud 
processing data, networks performing transmission, and devices functioning 
to connect things and people and provide data presentation and interaction. 
Based on this logical architecture, an IoT network is divided into four layers. 
Layer Function 
Application layer It provides data presentation and customer interaction. 
Platform layer 
It is generally a cloud technology platform that provides 
device communication management, data storage, and 
service planning. 
Network layer 
It is also called the transport layer, and its function is to 
provide terminal access and transmit data. 
Sensing layer 
It consists of sensors and video surveillance devices, and its 
functions include data collection and signal processing. 
 
The device side belongs to the sensing layer, where sensors collect data and 
are connected to the access and transport networks, such as the 2G, 3G, 4G, 
NB-IoT, and 5G networks, over edge IoT gateways. Data is transmitted to the 
cloud to form a big data cloud platform, and the platform uses the data to 
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create benefits to industry applications. On the network side, 5G has more 
advantages compared with other networks in terms of high bandwidth, high 
rate, low latency, solid reliability, and massive connectivity. Therefore, 5G 
can greatly improve user experience. 
1.2.1.3 Technologies Powering IoT Networks 
IoT networks use both wired and wireless communication technologies. This 
chapter will focus on wireless IoT communication technologies, which can be 
divided into long-distance and short-range technologies. The short-range 
wireless technologies include Bluetooth, Wi-Fi, ZigBee, and Z-Wave. 
1. Bluetooth is a short-range wireless digital communication standard that 
features a large capacity. It supports a maximum data rate of 1 Mbps 
over a maximum distance of 10 cm to 10 m. With a higher transmit 
power, the transmission distance can reach 100 m. It features a high 
speed, high security, and low power consumption. Supporting only 
limited nodes, it is not suitable for multi-point deployment. 
2. Wi-Fi allows electronic devices to connect to a wireless local area 
network (WLAN) on the 2.4 GHz UHF or 5 GHz SHF ISM band. It features 
a wide coverage and high data transmission speed, but cannot 
guarantee adequate security and stability performance or maintain a 
low power consumption. 
3. ZigBee is a short-range wireless technology that features a low power 
consumption and data speed. It boasts a low complexity and supports 
self-organization, and is widely used in industry and smart homes. 
4. Z-Wave is an emerging RF-based short-range wireless technology that is 
cost-effective and highly reliable while consuming a low power. It is 
advantageous in its simple architecture and is suitable for low-rate 
application scenarios. 
The long-distance wireless technologies include Sigfox, LoRa, NB-IoT, and 
eMTC. 
1. Sigfox uses the Ultra Narrow Band (UNB) technology to maintain stable 
data connectivity at a low power consumption. It supports a maximum 
distance of 1,000 km and a capacity of up to 1 million IoT devices per 
base station. 
2. LoRa, short for Long Range, is maintained and managed by the LoRa 
Alliance. It supports two-way data communications over a long distance 
based on the physical layer, and features a high capacity and long 
battery life. LoRa is best suited for automatic metering, smart home, 
building automation, wireless warning and security protection, 
industrial monitoring and control, and remote irrigation. 
3. NB-IoT is a cellular narrowband IoT. Built on cellular networks, it 
requires a cell bandwidth of only around 180 kHz and can be deployed 
on top of legacy GSM, UMTS, and LTE networks, which is favorable to 
reduce costs and ensure smooth upgrades. NB-IoT is an emerging 
technology that focuses on global use cases requiring low power 
consumption and wide coverage. It is built for low-speed services and 
features wide coverage, huge connectivity, cost effectiveness, low 
power consumption, and excellent architecture. NB-IoT has been 
incorporated into 5G standards. 
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4. eMTC is a wireless IoT solution proposed by Ericsson. Based on LTE, it 
designs the soft features of wireless IoT networks. It is mainly used in 
low-rate IoT use cases where in-depth coverage and low power 
consumption with massive connectivity are required. 
With NB-IoT incorporated into 5G standards, let's take a further look at the 
four features of NB-IoT. 
● Low costs 
Huawei SingleRAN solution facilitates the upgrade and 
reconstruction of legacy devices, helping cut construction and 
maintenance costs. NB-IoT chips are specifically designed for IoT 
narrowband and low-speed demand, supporting only single-
antenna transmission, half duplex mode, and simplified signaling. 
As a result, NB-IoT chips cost only a few dollars. 
● Low power consumption 
NB-IoT uses the power saving mode (PSM) and extended 
discontinuous reception (eDRX) for IoT services where small 
packets are occasionally transmitted. With these features, IoT 
devices enter the dormant state immediately after sending data 
packets and wake up only when data transmission is required 
again. As a result, IoT devices can be kept dormant for up to 99% 
of their service time, achieving ultra-low power consumption. 
 
 
The eDRX behavior in idle mode can be customized based on 
3GPP to extend the paging cycle from 2.56s to a maximum of 
2.92 hours. This reduces the number of times UEs in idle mode 
periodically listen to paging channels, enabling UEs tostay in the 
low-power deep sleep state over a long time to reduce power 
consumption. 
● Wide coverage 
NB-IoT is specially purposed for IoT, especially for Low Power 
Wide Area (LPWA) applications. It uses retransmission over the air 
interface and ultra-narrow bandwidths to provide gains of over 20 
dB over GSM. This means that a wider coverage can be possible 
even with fewer sites while ensuring strong signal penetration 
(down to basements). Devices such as electricity and water 
meters in hard-to-reach areas can be covered, and pet tracking 
and other services that require broad coverage can be provided. 
● Massive connectivity 
NB-IoT devices are cost effective and are widely deployed across 
industries, especially in industries where various instruments are 
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necessary. NB-IoT provides 50 to 100 more times connections per 
base station compared with other wireless technologies. This 
means that one sector can host 100,000 connections at most. 
By comparing different wireless IoT access technologies, NB-IoT has 
noticeable advantages over proprietary short-range technologies, with 
support for low delay sensitivity, ultra-low device costs, low power 
consumption, and optimized network architecture. 
 
 
1.2.1.4 5G+IoT 
Based on market statistics by IDC, the global IoT connections had 
approximated 30 billion by 2020, with the IoT market size expected to grow 
by 16.9% per year to reach USD1.7 trillion by 2020. The breakthrough of 5G 
provides a new opportunity to the IoT industry. Compared with 4G, 5G has 
more powerful communication and bandwidth capabilities, meeting the 
requirements of IoT use cases for high speed, stability, and wide coverage. 
With 5G, many IoT applications that are still in the theoretical or 
experimental stage may see brand-new opportunities to be quickly 
implemented and exploited. 
With all things connected, massive machine-type, and mission-critical 
communications will impose higher requirements on network speeds, 
stability, and latency. People will have stronger demands for heavy-traffic 
applications and connectivity of things over the mobile Internet. New 
applications, including autonomous driving, AR, VR, and tactile Internet, 
urgently need 5G. 
5G helps clear away the hurdles to the transmission speed and massive 
connectivity of IoT. mMTC, as a 5G use case, will support one million 
connections per square kilometer, achieving connectivity of everything. As 
such, 5G IoT will enable a massive number of devices to access networks, 
supporting smart cities, smart metering, and smart parking among many 
other applications. 
5G supports 1 ms of end-to-end latency required for remote control in 
autonomous driving and industrial production. With 3G for remote control, 
the braking distance is 3.3 m, given that a car travels at a speed of 120 km/h. 
With 4G, the braking distance is 1.67 m, which is still not short enough to 
ensure safety. By contrast, 5G reduces the braking distance to 0.033 m, 
markedly improving safety. 
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This means that 5G will bring enormous business opportunities to IoT 
applications. 
1.2.2 Cloud Computing 
1.2.2.1 What Is Cloud Computing? 
Cloud computing applications are everywhere. Cloud albums, cloud videos, 
and cloud music are all based on cloud. Cloud computing as a service is a 
business model where network, computing, and storage resources are 
purchased from cloud servers based on customer needs. The resources can 
be quickly provisioned and freed up with minimized management workload 
and interaction with the service providers. 
According to the National Institute of Standards and Technology (NIST), 
cloud computing is defined as a model that supports convenient, on-
demand access to a shared pool of configurable computing resources (like 
networks, servers, storage, applications, and services) that can be rapidly 
provisioned and released with minimal management effort or service 
provider interaction. 
By its definition, cloud computing has the following key features: 
1. On-demand self-service: Computing, storage, and network resources 
are purchased as required for services. 
2. Ubiquitous network access: Cloud services can be accessed through 
wired or wireless networks at any time. 
3. Location-irrelevant resource pooling: Computing resources of service 
providers are centralized for customers to rent. Both physical and 
virtual resources can be dynamically allocated to customers as required 
without having their exact locations controlled by or known to the 
customers. 
4. Rapid elasticity: Computing, storage, and network resources can be 
quickly deployed and elastically scaled up or down based on service 
requirements. 
5. Pay per use: Charging is based on the usage or usage duration (normally 
by month or year). 
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Cloud computing consists of three layers: Infrastructure as a Service (IaaS), 
Platform as a Service (PaaS), and Software as a Service (SaaS). The IaaS 
layer provides basic computing, storage, and network services, with typical 
IaaS services including Elastic Cloud Server (ECS) and cloud storage. The PaaS 
layer provides an environment for running and developing applications, in 
addition to the components for R&D, with database services being a typical 
PaaS service. The SaaS layer provides software-related functions through 
web pages, and typical SaaS services include portal websites and enterprise 
office application (OA). 
 
 
In the context of cloud computing, it is worth mentioning virtualization — a 
technology considered as the foundation of cloud computing. With 
virtualization, multiple virtual machines (VMs) run on a physical server, 
sharing the physical server's CPU, memory, and I/O resources while being 
logically independent of each other. In computer science, virtualization 
refers to the abstraction of physical resources of computers to provide one 
or more operating environments. Therefore, virtualization implements 
simulation, isolation, and sharing of resources. 
Prior to virtualization, servers are independent resource pools, with the 
operating systems strongly coupled with the hardware resources. After 
virtualization, the hardware resources are abstracted into shared pools and 
decoupled from the operating systems, with the pooled resources flexibly 
allocated to applications. 
 
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Virtualization is a process with which a lower-layer software module 
abstracts a virtual software or hardware interface for an upper-layer 
software module by providing interfaces that are completely consistent with 
the exact operating environments expected by the upper-layer software 
module. In this way, the upper-layer software module can directly run in the 
virtual environment. Virtualization abstracts a resource into one or more 
parts by means of space and time division and simulation as well. Common 
virtualization includes memory virtualization (page file), disk virtualization 
(RAID and volume), and network virtualization (VLAN). 
Virtualization has noticeable advantages. 
● Partitioning: Large, scalable hardware resources are partitioned into 
multiple independent servers, enabling multiple operating systems and 
applications to run on a single physical system in parallel and computing 
resources to be pooled and effectively managed. 
● Isolation: Virtualization provides idealized physical machines, with each 
isolated from the other to prevent data from leaking, ensuring that 
applications communicate only over configured connections. 
● Encapsulation: All the environments of a virtual unit are stored in a 
separate file and are presented to applications as standardized virtual 
hardware to ensure compatibility, and each disk partition is stored as a 
file to facilitate backup, transfer, and copy operations. 
● Independence: Virtual machines can be migrated to other servers 
without any modification. While ensuring high availabilityand dynamic 
adjustment of resources, virtualization greatly improves system 
sustainability. 
1.2.2.2 5G+Cloud Computing 
Cloud computing ensures flexible, on-demand, and ubiquitous resource 
utilization for applications. 5G brings new opportunities for cloud computing 
development. 
Cloud services will be fully upgraded. Though 4G has brought cloud 
computing to enterprise users on a large scale, the access to cloud services is 
still limited for individual users. This will be changed with 5G. 5G will improve 
cloud computing and cloud services, enabling them to have a direct impact 
on daily lives. For example, by being deeply integrated with 5G, smart 
services, including IoT, IoV, smart city, industrial Internet, and smart 
healthcare, will be greatly improved, enabling people from all over the world 
to embrace an era of smart life. 
5G will promote a comprehensive upgrade of cloud vendors. With 5G to 
rapidly improve networks, cloud service providers will be provided with 
better opportunities to upgrade and reconstruct cloud infrastructure, 
architecture, service models, and business systems, so as to accelerate the 
adoption of cloud solutions across vertical industries as they look to keep 
pace with the development of cloud computing. 
5G will shift cloud computing from network centers to network edges. As 
networks improve, more and more devices will be networked and users need 
to exchange more and more data. Persistent data exchange with data 
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centers will put 5G application experience at a considerable risk. With edge 
computing, users can exchange data with edge data centers, and this will 
further reduce latency to enable service responsiveness. In addition, edge 
computing will accelerate the integration of industry ecosystems, explore 
new services, and develop cloud service models for verticals. 
 
 
At Huawei, we believe that applications in the 5G era will be mostly based on 
the synergy of mobile networks, devices, and clouds, and we call this Cloud X. 
5G brings new eMBB networks and also brings edge computing closer to 
users, hopefully to reshape the entire service chain. Ubiquitous 5G 
connectivity and edge cloud access will help move computing, storage, and 
rendering from clients to the cloud, driving clients to become "thinner", 
more cost effective, and more mobile while making it easier for the industry 
to deploy and promote services. Moreover, with services centralized on 
cloud, networks, edge computing, and network slicing capabilities will 
become more indispensable to cloud sustainability. This further highlights 
the importance of networks, further enhancing the leading role of operators 
in the ecosystem. 
According to Huawei's Wireless X Labs, cloud VR that features rendering on 
the cloud will be a new trend of VR. Unlike in local VR mode where terminals 
must be cabled to local servers — a major cause for poor experience and 
high cost, terminals in cloud VR mode are wireless and rendering is 
completed on cloud. As such, terminal costs can be reduced and user 
experience can be improved. 
Cloud VR requires higher mobility, larger bandwidth, and lower latency. For 
example, entry-level VR needs a bandwidth of 100 Mbps and a latency of 10 
ms, while to ensure ultimate VR experience, the bandwidth will be increased 
to 9.4 Gbps but the latency reduced to 2 ms. Only 5G networks are able to 
meet the requirements of ultimate VR experience. 
5G+Cloud VR is only one application, and more will be explored in the future. 
1.2.3 Big Data 
1.2.3.1 What Is Big Data? 
To date, there is no universally agreed definition of big data. The mainstream 
definition interprets big data from the following four characteristics: 
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● Variety: This reflects both diversified data sources and various 
structures, with the former reflecting that data can be collected from 
different channels and platforms and the latter showing that data can be 
structured and non-structured. 
● Volume: This reflects the massive amount of data generated on the 
Internet. As the Internet progresses with each passing day, the volume 
of data increases continuously. The data generated now in one year 
equals the total of the data generated in the past. 
● Velocity: This involves the entire process of big data, such as the growth 
rate and processing speed of data. For many types of data, real-time 
feedback is already possible, enabling the data to have an impact on our 
life as soon as it is collected. 
● Value: This expresses the low value density of big data — with the tiny 
amount of useful data being completely overwhelmed by the massive 
amount of useless data, thus posing a serious technological challenge 
for exploring the value of big data. In other words, a larger amount of 
big data does not necessarily ensure a better effect. The key is to 
perform in-depth analysis of the massive amount of useless and 
complex data to mine data that is of value. 
These are the basic features of big data. 
What is the size of big data? 2.9 million e-mails are sent worldwide in one 
second, which would take one person 5.5 years to finish reading them day 
and night, assuming that one email consumes one minute. 28,800 hours of 
videos are uploaded to YouTube every day, which would take one person 3.3 
years to watch them day in and out. On the Internet, a large amount of data 
is generated every day. So, what is the use of such a large amount of data? 
Let's look at big data technologies first. 
Big data technologies are an umbrella name for technologies involving the 
collection, storage, analysis, and application of big data, as well as those 
related to dealing with the massive amount of structured, semi-structured, 
and non-structured data through various tools to produce analysis and 
prediction results. For example, GE uses a large amount of aircraft engine 
running data for analysis to predict when the engine will be likely to 
encounter faults. In the financial industry, Citibank uses IBM Watson to 
recommend products to wealth management customers, and Bank of 
America uses customer clicking data to recommend featured services. 
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1.2.3.2 5G+Big Data 
So how is 5G related to big data? 5G enables the scale and diversity of data 
to grow continuously. Providing high bandwidth and wide connectivity, 5G 
will create a tremendous amount of data. With both people and things in 
cities connected to cloud servers through 5G, big data can be generated and 
utilized to help governments better make decisions. In factories, people, 
machines, materials, processes, and environments will also be connected to 
cloud servers over 5G to create industrial big data. 5G facilitates the use of 
big data for intelligent decision-making and stimulates IoT expansion, which 
in turn fuels the progress of big data. Big data is analyzed and processed 
through dedicated technologies to facilitate decisions that are aimed at 
improving efficiency. In smart transportation, data generated by people-to-
vehicle, vehicle-to-road, people-to-people, and vehicle-to-vehicle 
connections can be sent to the cloud through 5G for further calculation. This 
will help optimize driving routes to lessen traffic congestion and improve 
overall efficiency. 
1.2.4 Artificial Intelligence 
1.2.4.1 What Is Artificial Intelligence? 
Artificial intelligence (AI) is a hot technology topic. First proposed by John 
McCarthy in 1956, with the meaning of allowing machines to behave like 
human beings, AI is defined as the science and engineering of making 
intelligent machines. It aims to enable machines to work intelligently, similar 
to the way that the human mind works. Currently, AI has become an 
interdisciplinary that overlaps with various fields, including cognitive science, 
psychology, and linguistics. 
The industry has not reached consensus on the definition of AI. Intelligent 
machines are generally classified as four categories:"think like a human", 
falling into the field of weak AI, with examples including Watson and 
AlphaGo; "act like a human", falling into the field of weak AI, with examples 
including humanoid robots, iRobot, and Atlas (by Boston Dynamics); "think 
rationally", belonging to the field of strong AI, which is still not yet available 
due to the bottleneck in brain science; and "act rationally", which also falls 
into the field of strong AI. 
When it comes to AI, one can never ignore machine learning and deep 
learning. AI is a new science that studies and develops the theories, 
methods, techniques, and application systems to simulate and extend human 
intelligence. Machine learning studies how computers acquire new 
knowledge or skills by simulating or performing the learning behavior of 
human beings, and how they reorganize the existing knowledge structure to 
improve its performance. It is one of the core research fields of AI. Derived 
from the research of artificial neural networks, deep learning is a new field in 
machine learning that simulates human beings to interpret various data, 
such as images, sounds, and texts. For example, multilayer perception is a 
type of deep learning structure. 
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Deep learning is a specific branch of machine learning. To understand deep 
learning, it is necessary to fully understand the basic principles of machine 
learning. 
Task T: How a machine learning system processes examples. A sample is a 
collection of quantized features that are collected from objects or events 
processed by the machine learning system, such as classification, regression, 
and machine translation. 
Performance measure P: How the abilities of a machine learning algorithm, 
such as accuracy and error rate, are evaluated. 
Experience E: While most of machine learning algorithms can be perceived 
as for gaining experience on an entire data set, some are not trained on a 
fixed data set. Reinforcement learning algorithms that need interacting with 
an environment are typical examples. In such cases, feedback loops will be 
created between their learning system and training process. Depending on 
the learning process, machine learning algorithms can be categorized as 
unsupervised or supervised. 
The overall process of machine learning starts from data collection. There is 
a famous saying in the industry: "Data determines the upper limit of machine 
learning, and models and algorithms are just used to approach this upper 
limit." Therefore, data is critical for the entire machine learning project as AI 
modeling requires a large amount of data cleansing. 
Data loss, uneven distribution, exception, and irrelevant impurities occur 
more or less in data sets. This requires the collected data to be cleansed 
through processing of missing and deviated values, data normalization, and 
data conversion among other methods. 
Data cleansing aims to ensure data normalized for subsequent feature 
extraction — a process of extracting features of data sets and reducing data 
dimensions. 
After data processing, a proper machine learning model is selected for data 
training. During model selection, different models are used to train the data 
and the output results are compared to choose the best model for 
evaluation and testing. 
After the best model is selected from a model class, model evaluation is 
performed to determine whether the model is over-fitting or under-fitting. If 
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the data fitting is not proper, parameters are adjusted to optimize model 
deployment and integration. 
Then, the trained machine learning model is deployed to the production 
environment. 
 
 
Deep learning is a learning model based on unsupervised feature learning 
and feature hierarchy. It has great advantages in speech recognition, natural 
language processing, and computer vision. 
Currently, AI is heavily applied in the following technical fields: 
1. Computer vision: It studies how to make computers "see" objects. Its 
applications include target detection, image segmentation, target 
tracking, text recognition, and facial recognition. 
2. Speech processing: It is a general term for speech processing 
technologies, including vocalization, statistical features of speech 
signals, speech recognition, machine synthesis, and speech perception. 
Its applications include smart speakers, spoken language assessment, 
voiceprint recognition, and consultation robots. 
3. Natural language processing: Its research topics include machine 
translation, text mining, emotion analysis, and public opinion analysis. 
1.2.4.2 5G+AI 
AI is undergoing a third wave of development triggered by deep learning, 
with remarkable progress achieved in data, computing power, algorithms, 
and platforms. 5G and AI are growing into strategic technologies driving new 
technological and industrial revolution, emerging as a high priority of new 
infrastructure. The two emerging technologies enable and benefit each 
other, accelerating the digital transformation together in both economic and 
social areas. 
Rapidly developing 5G networks and maturing synergy of 5G with cloud, 
edge, and devices will help achieve full connectivity and data convergence 
and reduce barriers to AI applications while boosting AI integration into 
social and economic development. According to the forecast of an 
authoritative organization, the AI market will be worth more than USD6 
trillion by 2025, confirming that AI will become ubiquitous. Turning into a 
core application on 5G networks, AI will accelerate the intelligent 
transformation of 5G networks as well as cloud, edge, devices, and other 
HCIA-5G Learning Guide 
infrastructure, maximizing the comprehensive potential of cloud and 
networks. Ultimately, AI will make 5G become even more intelligent5. 
5G will be a key infrastructure for various industries to achieve digital 
transformation. Featuring high bandwidth, massive connectivity, and low 
latency, 5G will facilitate AI development in terms of data, computing power, 
and applications. 5G's massive connectivity facilitates data collection. 
According to the IMT-2020 White Paper on 5G Vision and Requirements, 
more than 100 billion devices will be connected to mobile networks 
worldwide by 2030. 5G's full connectivity will lead to an explosive growth in 
data volume, types, and forms, providing high-quality data sources for both 
AI training and modeling. 5G's high bandwidth will also ensure the smooth 
data transmission required to maximize the potential of AI in data analysis 
and mining, providing better support for upper-layer applications. 
Conceivably, the convergence of 5G and AI will trigger a chain of 
transformation to accelerate full connectivity, sensing, and intelligence, 
creating a far-reaching impact on the digital transformation socially and 
economically. 
5G AR glasses facilitate AI facial recognition to improve security efficiency. 
AR glasses will enable security staff to upload videos through 5G networks to 
the background database for personnel matching. This will help check 
whether a person is suspicious, helping security staff determine the actions 
that need to be taken correspondingly. 5G provides huge bandwidth, 
ensuring that data is sent back to the cloud in real time for AI analysis. 
1.3 5G+New Technologies Empower Vertical 
Applications 
At the 2019 HUAWEI CLOUD Summit, Edward Deng, President of HUAWEI 
CLOUD Global Marketing & Sales, commented on the huge effect of the 
convergence of new technologies. "The convergence of cloud, AI, 5G, and IoT 
will provoke a chain of positive effects on everything, from lives to work and 
society at large, featuring all-new applications and experience and creating 
new industries. This will enable industries to improve under-performing 
elements and deliver what was once impossible and unimaginable, creating 
revolutionary new values for society." 5G, cloud, AI, and IoT are already 
changing society. 
Thiswill be a multi-phase process. Considering the advantages of 5G in 
bandwidth, connectivity, and latency, the joint applications of 5G and other 
new technologies in vertical industries are divided into three phases. 
 
5 Excerpt from the speech by Dong Xin, the general manager of China Mobile, at the 
WAIC Cloud Summit 2020 
HCIA-5G Learning Guide 
 
 
In phase one, video services are focused. At the early stage of 5G 
development, NSA networking dominates to provide adequate support for 
only eMBB services, such as HD broadcasting, UAV HD video upload, and 
video surveillance. 
In phase two, 5G networks are more mature to support certain low-latency 
applications, such as remote crane control in ports and smart unmanned 
mining operations to improve efficiency. 
In phase three, 5G networks are matured, supporting a minimal latency of 1 
ms and a speed of up to 10 Gbps. With this level of performance, remote 
surgery, autonomous driving, and accurate remote control will be possible. A 
massive number of devices will be connected to networks, helping make 
cities more intelligent. 
5G, cloud, AI, and IoT are the basic elements of future infrastructure. The 
convergence of 5G and other new technologies is already changing society, 
providing the basis for the digital economy.