Artificial Intelligence in Telecom – Industry Adoption Analysis

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June 2019
ARTIFICIAL INTELLIGENCE IN TELECOM
Industry Adoption Analysis
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AI in Telecommunication
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Table of Content
▪ Overview of the Telecom Value Chain Participants
▪ Role of AI in the Telecom Ecosystem
▪ Use Case 1: AI in Network Optimization
▪ Use Case 2: AI for 5G Networks
▪ Use Case 3: AI in Cloud
▪ Use Case 4: AI Security
▪ Use Case 5: Other AI Implementations
▪ Business Model 1: AI Managed Services
▪ Business Model 2: AI Powered Chipsets
▪ Business Model 3: Data Monetization
▪ Business Model 4: Open Source Platforms
▪ Business Model 5: AI-Enabled Cloud Services
Executive Summary
Impact of AI in the Telecom Ecosystem
Use Cases of AI in the Telecom Industry
How is AI Transforming Telecom Business Models ?
Overview of AI Adoption by Key Telecom Entities
▪ Adoption Status of Telecom Companies
How is AI Driving M&A Activity in Telecom Sector?
▪ Technology Drivers of the Deals
▪ Technology Distribution
▪ Overview of the Acquired Technologies
▪ Prominent Acquirers in the Space
▪ Post-deal Integration Examples
▪ Netscribes Analysis and Insights
▪ Overview of the Deals
How are Startups Innovating in the AI Telecom Space?
▪ Key Challenges Targeted by Startups
▪ Startups Focusing on:
▪ SON
▪ Cloud Native Networks
▪ Network Security and Analytics
▪ Interference Cancellation
▪ AI Hardware
▪ AI Assistant
Insights & Recommendations
▪ Current Adoption Status of AI in Telecom
▪ Recommendations for Telecom Companies
▪ Recommendations for Other Industry Players
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AI in Telecommunication 3
Executive Summary – (1/2)
Over the years, mobile network operators (MNOs) have struggled to manage network complexities with the growing demand of higher data rates.
Artificial Intelligence (AI) is …. , a primary concern in the telecom sector. Such models are thus gaining traction owing to the necessity.
The initial adoption of AI can be observed for deriving intelligence related to network operations by applying machine learning (ML) algorithms to
massive network data. Such solutions allow MNOs gain a competitive advantage by proactively monitoring network parameters and taking
corrective actions to reduce downtime, while maintaining the network quality. With the advent of 5G, the need for……………………….
…………………………………………………………………………………………………..without manual intervention. Presently, SON is a key strategy for a telecom entity to
transform RAN processes. The telecommunication industry is also witnessing a shift towards cloud native architecture that harnesses cloud
capabilities to build digital telecom networks. …………………………………. …………………………….. and software to eliminate traditional hardware
dependencies.
The road to network automation is pushing telecom companies to adopt new business models and develop core AI skills. AI-managed services,
intelligent chipsets, data monetization, open source and …………………………………………………………………………………in the telecom sector.
………………………… virtualized 65.5% of its network and aims to virtualize 75% by 2020. Ciena’s ………………………………………. CTNet 2025 initiative show
that all the key telecom companies are globally gearing for the future autonomous self-healing network.

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Executive Summary – (2/2)
M&A trends suggest that 2018 witnessed the maximum number of acquisitions in different domains, while the early deals in 2019 were majorly
related to security and cloud native. Also, these two technology areas were amongst the major high value deals. The M&A transactions in the AI
telecom ecosystem highlight …………………………………………………………………………………………..as the prominent acquirers. CSPs …………………………….leading
acquirers. Deals by ………………………..are focused on network analytics and cloud native, respectively. Telecom operators are inclined toward AI
security solutions. Telefonica’s strategic acquisition highlights a unique value proposition ……………………... While startups are
……………………………………………………………………capitalizing by investing in AI solutions for CSPs that are seeking to transform to secure digital networks.
The adoption of AI/ML solutions is accelerating growth of startups that are addressing key challenges of the telecom industry. The major challenges
being ………………………………………………………………………………….. Altiostar is the startup with maximum funding of USD 325 Mn providing cloud native
solutions for end to end RAN virtualization. The company is working with …………………………………………………………………………………………disrupting the
telecom space with self-interference cancellation technologies for full-duplex communication. In competition with Cambricon’s AI
chipsets…………………………………………………………………….following a distinctive approach that includes the usage of metamaterial beamforming
technologies for 5G systems.
According to Netscribes’ analysis, to realize the ultimate goal of self-driving networks, MNOs, CSPs and other participants in the telecom ecosystem
should consider ………………………………………………………………………………………………………….. form a strong R&D roadmap for next-generation autonomous
networks. Additionally, companies from other sectors (semiconductor, IT companies, startups and investors) can also capture new revenue streams
by tapping into the evolving AI telecom market. For instance, IT companies can ……………………………………………………………………….. with their cloud
native expertise.

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Methodology for the Industry Adoption Study
This study covers the industry adoption analysis of AI technologies in the telecommunication industry. In order to understand the adoption status of AI and
ML tools in the telecom environment and to assess the different opportunities available in the domain, different factors have been considered.
Use Cases of AI in the Telecom
Sector
Transformation in Business
Models
Impact of AI in the Telecom
Ecosystem
Implementation Strategies of
Key Telecom Companies
Key Challenges Addressed by AI
Solutions
M&A Analysis for AI Technologies in
Telecom Industry
Role of Startups for Enabling
the AI Solutions
Opportunities in the AI Telecom
Market
Parameters Used to Assess the Adoption Status

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AI in Telecommunication 6AI in Telecommunication
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Impact of AI in the Telecom Ecosystem

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xx
Network Planning Network Optimization Network O&M
▪ Interference estimation
▪ Self-healing network
▪ Dynamic cell reconfiguration based on
network parameters
▪ Real-time adaptive networks
▪ Real-time migration of services
▪ Intelligent node updation
▪ Data verification
▪ Intelligent cell site designing
▪ Dynamic resource allocation
▪ Network intelligence for improved cell
coverage and capacity
▪ Traffic prediction and management
▪ Intelligent slicing
▪ Intelligent power control of network nodes
▪ Service quality management
▪ Network virtualization
▪ Monitoring of network KPIs
▪ Automated ticketing systems
▪ Failure detection
▪ Root cause analysis
▪ Network intrusion detection and
elimination
▪ Alarm correlation
▪ Predictive hardware maintenance
Role of AI in the Telecom Ecosystem

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Use Cases of AI in the Telecom Industry

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Use Case 1: AI in Network Optimization
AI and ML algorithms are transforming traditional networks into
intelligent systems. Predictive mechanisms are enabling telecom
companies take faster and better data-driven decisions. Intelligence built
on huge amount of historical network data provides a pattern for
network anomalies. Using AI models, telecom operators can detect
network failures, forecast traffic patterns, understand traffic congestion,
predict customer behavior, build intelligent security, and gain actionable
insights. The intelligent networks will be built using self-organizing
networks (SON) for efficient field planning and optimization.
Improved customer
experience
Minimized truck rollsIncreased ROI
Reduced maintenance
cost and OPEX
Reduced churn
AI Overcoming the Limitations of Siloed Network Planning
Operational Network
Efficiency
Traditional Networks Intelligent Networks
Reactive approach to optimize
network parameters
Oriented towards hardware
infrastructure
Increased downtime and network
failures
Manual effort required for
hardware maintenance
Prone to breaches, attacks and
other security issues
Not suitable for applications
requiring multi-mode capabilities
Defined by technological and
industrial silos
Complexities in network
virtualization

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Use Case 2: AI for 5G Networks
Network slicing is critical for the 5G-based diversified use cases and to maximize the flexibility of 5G networks. The incorporation of AI techniques can detect
slicing related anomalies and faults, and also use the learning to improve slicing strategies. Additionally, AI can ensure cost effective and high quality slicing
solutions.
AI techniques can be used to plan 5G network deployment. The initial non-standalone architecture implementation will face multiple challenges
associated with cell allocation, varied systems, multiple frequency bands and network configuration. AI and ML can be used for 5G network planning
based on insights related to multi-mode coverage, cell distribution, traffic management and analysis of network parameters for better planning and
increased network capacity.
Neural networks and ML algorithms can be used for complex channel modeling and estimation techniques for the large scale MIMO technology. These
algorithms can be applied on the beamforming parameters to adjust coverage in different scenarios. Intelligent mechanisms for interference calculation
and adaptive optimization in intra-cell network deployment will result in higher accuracy.
ML techniques can be applied to predict configuration of carrier aggregation between 5G network nodes. Additionally, algorithms applied for design and
fabrication of millimeter wave RF front circuitry is going to reduce the time to market.
Network Slicing
Planning for 5G Deployment
AI-Enabled Massive MIMO
RF Front End Circuitry
Intelligent
5G Networks

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How is AI Transforming Telecom
Business Models ?

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AI Transforming Telecom Business Models
Telecom companies are reinforcing strategies to incorporate AI in their core business offerings. CSPs are changing their core value propositions and are making
fundamental changes to the traditional revenue models. These transformations are enabling them to capture new revenue streams and, in the future, can
help them expand several industry verticals. In order to prepare for the digital transformation, telecom companies are therefore adopting different routes with
a focus on delivering automation for the future complex networks. Some of the CSPs are considering …………….. of data. The evolution in the business models is
a proof of how widely AI technologies are getting accepted in telecommunication.
AI-Managed Services AI Chipsets Open Source Platform Data Monetization AI Cloud Services
Telecom companies are
providing ………….network
requirements.
This is crucial
with the increasing number of
………….. service requirements.
Open source frameworks
will lead to …………..
innovation and is a huge
transformation for the
traditional business models.
Telecom companies are
harnessing the power of data
for …………. opportunities.
Data-sharing models are
bridging the gap between
……….. across sectors.
Telecom entities are
focusing on automation in
cloud that places them in
direct competition with
………….
Telecom companies’ strategic
approach to develop ………… to
enter new business areas. In
the future, this will lead to
direct competition with the
……………….
Business Models for Integration of AI in the Telecommunication Industry

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Business Model 1: AI Managed Services
Companies Providing Managed Services
Ericsson has signed multiple …….. telecom companies
to support the innovation for next generation
networks.
o Ericsson’s partnership with Airtel is focusing on
utilizing AI/ML for xxx operation management.
o Mobily and Ericsson are working on anomaly
detection, proactive ………… rollout for 5G and IoT
use cases.
o Ericsson is catering …….. regions for Telefonica.
o Ericsson’s partnership with existing managed
services customer MBNL to ……….
Telecom companies are providing AI-based managed services to transform the traditional
network-centric business models to a …………………………………………………………………….of data
insights for network optimization, design and application development.
This operating model addresses the ………………………………………………………………………………..
……………………..network capital and operator expenditure. The managed services model is aiming
towards addressing the future complexity challenges driven by ………………………….transformation.
Companies
• Xx
• Xx
• xx
Benefits

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Overview of AI Adoption by Key Telecom Entities

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Adoption Status of Telecom Companies – (1/5)
Other AI
Solutions
AI Strategy
• AT&T’s open source initiatives (ONAP, Danos,
Acumos AI, Akraino Edge Stack), and the
architecture and roadmap for …….. intelligent
software-defined framework.
• FlexWare is a network virtualization solution to
deploy multiple functions through software.
• The company’s ECOMP will …….. networks.
• In 2018, the company virtualized 65.5% of its
core network and is targeting 75% by 2020.
• Verizon has partnered with ………. for the
development of virtualized cloud RAN. In 2018,
the company had tested vRAN using xx equipment
and xx processors. Verizon has also conducted
trial related to disaggregating hardware and
software in the network.
• The company is also deploying its Intelligent Edge
Platform as a part of its automation strategy.
• Verizon is also using AI and ML models to enhance
………………….. for improved customer experience.
• Rakuten is following a greenfield approach to
build a cloud native and virtualized network.
The company’s partnership with……………..
………………………..Altiostar, (for vRAN software),
Nokia (Impact IoT), …………… and others are
critical factors for the deployment of cloud-only
5G network.
• AT&T is using AI models for improving customer
experience, ……………… computing foundation.
• Verizon has launched Digital CX, an end to end
managed services that leverages AI for improved
customer experiences.
• The AI-driven business models will also help
Rakuten explore new IoT applications in
industrial and automotive sectors.
• AT&T aims to provide a common open AI
platform and …………. of the future to offer
applications across sectors. The company has
done multiple partnerships related to AI, cloud
computing and 5G.
• Verizon’s AI solutions are largely built from open
source platforms. The company is also …………
…….will enable predictive analytics, ML, and AI
applications.
• Rakuten is directly competing with
………………………. in virtualization and container
technologies for the telecom environment.
• Rakuten’s vision is to innovate at ……………., thus
reducing significant capex investment. The
company intends to launch its commercial
service in xx with a fully virtualized network.
AI for
Networks

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How is AI Driving M&A Activity in Telecom Sector?

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M&A Analysis: Technology Drivers of the Deals
SON
Predictive
Analytics
Security Network
Analytics
Intelligent
Spectrum
vRAN Others
Cloud
Native
Acquirers are focusing on the following technology areas to expand AI capabilities and create new business models.
* Other category includes deals related to data sets, IT operations, customer support & internal business operations.
*Deals related to chatbots and digital assistants were not considered for analysis.

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The following heatmap provides insights related to the technology distribution in the M&A deals across the years. Transactions related to cloud native, SON
and security have been gradually increasing over the years. The year 2018 witnessed a spike in the number of deals with companies making acquisitions in
different domains. In 2019, four cybersecurity related deals highlight the need of security companies in the future for autonomous detection and response. As
virtualization is the aim of telecom companies, it is expected that M&A activities in vRAN space will continue in the future.
M&A Analysis: Technology Distribution
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M&A Trend Across AI Technologies
*Other category includes deals related to data sets, IT operations, customer
support & internal business operations

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M&A Analysis: Prominent Acquirers in the Space
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Maximum number of deals have been done by ……………….. Nokia is prominent buyer in ……… space.
While Ericsson is focused on ………. solutions.
The security-related acquisitions were done by telecom operators including …………. Telefonica has
followed a unique approach of ………….. in the telecom AI market. The company has integrated ……………
for catering to clients globally.
Some of the transactions highlight that startups are also expanding their niche portfolios to gain a
competitive edge. ………….. has made multiple acquisitions to enter the telecom industry with
capabilities such as intelligent RAN behavior analytics.
A private equity firm has acquired …………… owing to the huge business growth opportunity available
in the telecom industry. Such investors are capitalizing on the transformation need of the CSPs
towards all-cloud network. This route not only enables the buyers an entry into the 5G market but
also other parallel markets such as IoT.
Solution Providers
Telecom Operators
Startups
Investors

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M&A Analysis: Post-deal Integration Examples – (1/2)
Blue Planet became a part of Ciena’s
portfolio with the acquisition of
Cyan, a SDN solution provider.
Addition to the existing Blue Planet
portfolio for enabling the vision of adaptive
networks with self-healing capabilities
Blue Planet spun out as an
independent division to focus on new
growth strategies and roadmaps for
closed-loop network automation.
Layer 3 network optimization,
topology and route analytics
Intelligent inventory
management solutions
2015 2018 2019
Ciena’s post acquisition analysis highlights how telecom companies are considering vital strategies to build network with automation capabilities and explore
adjacencies for growth.
Existing Portfolio Acquired Capabilities Post-acquisition Outcome

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M&A Analysis: Netscribes Analysis and Insights
The M&A trend highlights some of the significant technology domains on the radar of different participants in the telecom space. However, according to our
analysis, as deep learning technologies are becoming pervasive in the telecom sector, other AI solutions are also going to be significant in the future M&A
deals. Few of these technology areas include:
Reinforcement Learning Hardware Innovations
Innovative reinforcement learning………………
solutions…………………………………………………………………………………………….. to
the ecosystem requirements. Such companies will be the potential
targets.
Startups that are ………………………………..are likely to grab attention from
industry participants. ………………..has been included in the next section.
• Self-healing solutions
• Reinforcement and deep-learning algorithms
• Xx
• xx
• Xx
• xx
• Xx
• xx

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M&A Analysis: Overview of the Deals – (1/9)
Sr.
No
Buyer Target
Date of
Deal
Region of
Target
Overview
Technology
Category
1 Securelink May-19 Belgium,
Europe
SecureLink provides security intelligence based on AI and ML techniques. It
has a significant cybersecurity portfolio ranging from maintenance, support
and consulting to advanced managed detection and response. This
acquisition, with the deal size of USD 575 Mn, puts Orange at the forefront
of European cybersecurity market.
Security
2 SecureData Feb-19 UK, Europe SecureData employs AI-based engines for identifying critical threats in the
network and the cloud. This acquisition will help Orange strengthen its
position in the European market as a cybersecurity leader.
Security
3 BluVector Mar-19 Virginia, US BluVector's proprietary ML engines provide detection, analysis of advanced
cyberthreats. Comcast plans to leverage BluVector's expertise for new
products and initiatives.
Security
4 Mist Mar-19 California, US Juniper aims to expand its end to end software defined enterprise portfolio
for its IT operations by using MIST'S AI Engine and cloud based solutions.
IT Operations
5 Protectwise Mar-19 Colorado, US Protectwise applies ML and AI algorithms to predict and automate
detection of security events and respond to them in real time. With rapid
expansion in next-gen 5G networks, Protectwise brings the network
detection and response capabilities to Verizon's global networks.
Security

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How are Startups Innovating in the Space?

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Key Challenges Targeted by Startups
Challenges Solutions Companies
Full duplex communication has ………. frequency. Presently, xx are
used for noise suppression. The parameters in such devices cannot
be ………….
Interference in Full-duplex Communication
Traditional hardware in the telecom vertical were …………… it is
challenging to scale up the network for new use cases in multi-
vendor scenarios.
Decoupling Hardware & Software
SON implementation for autonomous 5G networks requires
increased ………… of new 5G systems for higher cell coverage,
higher bandwidth and millimeter frequency range.
SON Implementation Challenges
AI-based ………….. techniques allow multiple transmitters to
coexist enabling, …………… for mobile operators and eliminates
the requirement for traditional xx.
AI for Interference Cancellation
Telecom companies are introducing …………. of the network. To
accelerate the virtualization of RAN, they are exploring cloud
native ……… of software and hardware and become xx
independent.
Cloud Native Architecture
Companies are approaching ……….. with ML techniques for
dynamic cell site ……… and xx algorithms for self-healing and self-
configuring networks.
Targeting Autonomous Telecom Network

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Overview of Startups

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Foundation Year: 2013
Headquarters: Singapore
Technology Focus: Intelligent SON
The company is targeting
…………………………………associated with multi-layered
and service oriented wireless networks for
virtualized 5G deployment with self-healing.
Funding: USD 24.5 million
Cellwize’s centralized elastic-SON platform eliminates the hardware dependencies for future
adaptive and scalable networks to provide a multi-vendor environment for SDN/NFV. The
company’s virtualized SON architecture is closed-loop and allows easy deployment of new
network clusters with continuous monitoring and optimization.
Cellwize’s business strategies suggest a focus on truly automated SON architecture. Cellwize has
initiated ……………………………………………………………………………….. investigating AI and ML for mobile
load balancing, automatic neighbour relation and other use cases.
The strategic acquisition of CrowdX strengthened Cellwize’s customer-centric SON offering. By
leveraging the acquired ………………………………………………… customer experience. Cellwise has
partnership with companies like Tech Mahindra, HP Enterprise, Comarch, IBM, Telefonica, etc. The
company has conducted proof of concept with
……………………………………………………………………………………………………… autonomous management.
Additionally, Cellwize plans to focus on 5G rollouts and expansion of global footprint with the
recent funding from Deutsche Telekom Capital Partners. Telefonica, Axiata, and Bell Canada are its
global clients.
Startups Focusing on SON
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Technology Overview
Company Overview
Future Focus

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Foundation Year: 2011
Headquarters: Massachusetts, USA
Technology Focus: Virtualized RAN for Cloud
Native Telecom Network
The company is planning to use the recent funding
for expansion of its ……………………….. globally to build
cloud native networks. Additionally, Altiostar’s
partnership with its investors is focused on
developing new 5G solutions.
Funding: USD 325 million
Altiostar is an open virtualized RAN (vRAN) software provider that is providing end to end cloud
native 5G networks. The company is catering to telecom operators for migration to non-
standalone 5G architecture and later to future standalone 5G network.
The company’s innovative software architecture is deployed on commercially-off-the-shelf (COTS)
hardware. Altiostar’s core vRAN software disaggregates hardware and software to build a multi-
vendor environment of the future. The vRAN software can be deployed across the RAN ecosystem
including massive MIMO, small cells and macro radios. It uses SON algorithms for easy
configuration of new small cells and spans across technologies including LTE-A, Gigabit LTE, IoT,
Hetnet, 5G NR, NFV, orchestration, C-RAN, CoMP and others.
Altiostar’s software reduces the time to market as there is no requirement to change the
hardware. The company is already working with Rakuten, a Japanese mobile operator, to deploy
the first fully virtualized network that is targeted to be commercially launched in late 2019.
Altiostar’s vision of virtualized RAN is aligned with its investors (Qualcomm Ventures, Tech
Mahindra and Rakuten) aim to transform the mobile industry.
Startups Focusing on Cloud Native Networks
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Technology Overview
Company Overview
Future Focus

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Insights & Recommendations

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Current Adoption Status of AI in Telecom
Frontrunners
AT&T and xx are the two companies that can
attain network virtualization faster as compared
to other telecom operators.
AT&T has achieved 65.5% virtualization already
xx, xx, Ericsson and xx are targeting self-driving
networks.
Huawei is creating reinforcement learning techniques
Most of the MNOs have deployed AI at a chatbot
level, driving improved customer experience and for
ensuring internal business operation.
Followers
Late-adopters

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Recommendations for Telecom Companies – (1/3)
5G Market New Market Opportunities Threats
The 3GPP Release 16 for 5G standards is
expected to be finalized by March 2020.
MNOs, …… solution providers and …..
should consider working in collaboration
with the regulatory bodies to accelerate the
adoption of ML in telecom industry. ITU-T
……… of standalone 5G networks.
Telecom industry will hold a strong position
in the future AI market and can have the
capabilities to enter new industry segments.
For instance, …… directly with cloud service
providers such as Amazon and Microsoft.
xx business transformation from MVNO to
MNO is an example of how IT or cloud
service providers can enter the telecom
sector by leveraging their existing
infrastructure support.

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Presently, R&D in xx techniques is critical for designing autonomous, self-driving networks of the future. According to our analysis, telecom entities should
consider partnership with universities and research institutes that are investigating xx. These universities are researching on these algorithms for different
aspects covering traffic scheduling issues, offloading decisions, SDN/NFV 5G slices, interference mitigation, heterogenous network solutions and others.
Telecom operators can collaborate with such universities and contribute with historical network data sets.

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Recommendations for Other Industry Players
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Semiconductor companies should
explore deep learning frameworks
like ………………for maintaining lead
in the telecom space that is
witnessing growth in competition.
For instance, xx’s strategy towards
AI chips.
As key integrators in the telecom
domain, IT companies can consider
the opportunity of offering
……………………….. 5G deployments.
They also have an opportunity to
enter the telecom market if the
regulatory policies favour such a
move. Then ………………… business
transformations would be possible
with other technology companies
like xx directly competing with
telecom operators to fuel price
competition.
Companies developing AI solutions
can cater to ……………………….. and
scaling it up till SON.
AI startups can conduct PoCs in
collaboration with telecom entities
for 5G rollouts.
Investors can consider portfolios
that integrate automation
solutions with 5G-enabling
technologies. xx is already
exploring 5G business ……… with
the help of newly acquired
spectrum.
This is the right time for them to
tap in the telecom market.
Investors can increase ROI as
……………. CSPs to new application
areas.
In the near term, semiconductor
companies can consider selling
intelligent ………. the 5G market.
Additionally, carrier aggregation
demands in 5G require complex
filtering configurations that can be
overcome by ……………….
Startups InvestorsSemiconductor Companies IT Companies

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Appendix

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References – (1/4)
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❑ Cellwize. “LP-AW-K-Self-Organizing Networks.” Cellwize, 4 May 2016, cellwize.com/self-organizing-networks/.
❑ Cambricon. “Cambrian Is Committed to Building Core Processor Chips for All Types of Intelligent Cloud Servers, Smart Terminals and Intelligent
Robots.” Cambricon, www.cambricon.com/index.php?c=page&id=9.
❑ Parallel Wireless. “HetNet Gateway.” Parallel Wireless, www.parallelwireless.com/products/hetnet-gateway/.
❑ Kumu Networks. “Technology.” Kumu Networks, kumunetworks.com/technology/.
❑ Altiostar. “Cloud Native Network.” Altiostar, www.altiostar.com/solutions/radio-access-network/cloud-native-network/.
❑ RamiRahim. “Juniper Networks Announces Intent to Acquire Mist Systems to Bring AI to IT, Delivering on Promise of Software-Defined Enterprise.”
J, 3 Mar. 2019, forums.juniper.net/t5/Engineering-Simplicity/Juniper-Networks-Announces-Intent-to-Acquire-Mist-Systems-to/ba-p/459688.
❑ Orange. “Orange Signs an Agreement to Acquire SecureLink and Accelerate Its Leadership in the European Cybersecurity Industry.” Site
Institutionnel D'Orange, 10 May 2019, www.orange.com/en/Press-Room/press-releases/press-releases-2019/Orange-signs-an-agreement-to-
acquire-SecureLink-and-accelerate-its-leadership-in-the-European-cybersecurity-industry.
❑ AT&T. “AT&T Completes Acquisition of AlienVault.” AT&T News, Wireless and Network Information, 22 Aug. 2018,
about.att.com/story/2018/att_completes_acquisition_of_alienvault.html.
❑ ZephyrTel. “VoltDelta. Multi-Channel Contact Centre Solutions.” VoltDelta Cloud and on-Premise Contact Center Solutions, Virtual Call Center
Infrastructure Services, www.zephyrtel.com/solutions/voltdelta/.
❑ Hamilton, Rick. “Ciena Completes Acquisition of Packet Design.” Ciena, 2 July 2018, www.ciena.com/insights/articles/Ciena-Completes-
Acquisition-of-Packet-Design.html.
❑ Rakuten. “Rakuten Is Building the World's First End-to-End Cloud-Native Mobile Network.” Rakuten Today, 22 Feb. 2019,
rakuten.today/blog/rakutens-upcoming-end-to-end-cloud-native-mobile-network.html.
❑ Nohrborg, Magdalena. “The MobileBroadband Standard.” SON, www.3gpp.org/technologies/keywords-acronyms/105-son.
❑ Ericsson. “New AI-Based Ericsson Operations Engine Makes Managed Services Simple.” Ericsson.com, 29 Jan. 2019, www.ericsson.com/en/press-
releases/2019/1/new-ai-based-ericsson-operations-engine-makes-managed-services-simple

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References – (2/4)
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❑ ITU. “ITU Workshop on ‘Machine Learning for 5G and beyond.’” ITU Workshop on "Machine Learning for 5G and beyond", www.itu.int/en/ITU-
T/Workshops-and-Seminars/20180807/Pages/default.aspx.
❑ Nokia. “#MWC19: Nokia, Korea Telecom to Conduct 5G Trials for Service Automation, Network Virtualization and Slicing.” Nokia, 24 Feb. 2019,
www.nokia.com/about-us/news/releases/2019/02/24/mwc19-nokia-korea-telecom-to-conduct-5g-trials-for-service-automation-network-
virtualization-and-slicing/.
❑ Incelligent. “Future RAN Evolution: Tools to Support Strategic Decision Making.” Future RAN Evolution: Tools to Support Strategic Decision Making,
www.incelligent.net/news/247-future-ran-evolution-are-there-tools-to-support-strategic-decision-making.
❑ Guangping, Zhu. “SoftCOM AI: Hard on the Competition with Zero Faults - Huawei Publications.” Huawei, 6 June 2018,
www.huawei.com/en/about-huawei/publications/communicate/85/softcom-ai-hard-on-the-competition.
❑ Ciena. “Waveserver Ai: Simple, Scalable DCI.” Ciena, www.ciena.com/products/waveserver-ai/.
❑ Luca. “Big Data Technologies: Statiq for Geolocation Data.” LUCA Data-Driven Decisions | Big Data Solutions | Data Engineering, luca-
d3.com/technology-statiq/index.html.
❑ Metawave. “AWARE AI Engine | Metawave - Revolutionizing the Future of Wireless Communications.” Metawave, www.metawave.co/aware.
❑ Chinchali, Sandeep, et al. Cellular Network Traffic Scheduling with Deep Reinforcement Learning. AAAI, 2018.
❑ Huang, Liang, et al. “Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks.”
Arxiv.org, 20 Apr. 2019, arxiv.org/pdf/1808.01977.pdf.
❑ Faris, and Brian. “Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement.” Deep Q-Learning for
Self-Organizing Networks Fault Management IEEE Conference Publication, IEEE, 21 Feb. 2019,
ieeexplore.ieee.org/abstract/document/8645083/authors#authors.
❑ Sadeghi, Alireza, et al. “Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities.” IEEE Journals & Magazine,
IEEE, 29 Dec. 2017, ieeexplore.ieee.org/abstract/document/8241758/authors#authors.

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Artificial Intelligence in Telecom – Industry Adoption Analysis

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